The growing industrial demand for customized and cost-efficient large language models (LLMs) is fueled by the rise of vertical, domain-specific tasks and the need to optimize performance under constraints such as latency and budget. Knowledge distillation, as an efficient model compression and transfer technique, offers a feasible solution. However, existing distillation frameworks often require manual intervention and struggle to meet such complex user-defined distillation requirements. To bridge this gap, we propose Stratos, an end-to-end LLM distillation pipeline that automates server/model selection, knowledge distillation, and deployment in distributed cloud environments. Given user-defined constraints on model performance and system budget, Stratos automatically selects Pareto-optimal servers, dynamically matches teacher–student pairs, and adapts distillation strategies based on task complexity to optimize cloud hosting. Experiments show that Stratos produces a student model that achieves four times the accuracy of its GPT-4o teacher baseline on a rare, domain-specific Mahjong reasoning task with reverse synthetic data and knowledge injection. Moreover, it achieves reduced latency and cost without compromising accuracy. These results highlight its promise for vertical-domain LLM deployment.

Vehicular edge computing is rapidly amplifying the need to process computation-intensive tasks generated in vehicular environments. Conventional centralized processing frameworks struggle to meet these low-latency demands due to network latency and bandwidth constraints. Federated learning (FL) addresses these challenges by retaining raw data on vehicle nodes (VNs). However, sustainable FL training in vehicular edge computing faces persistent obstacles: the instability of VNs (high entry/exit rates), resource heterogeneity, and the lack of effective incentives. Notably, while existing incentive mechanisms are extensively studied, they inadequately address two critical sustainability barriers: extreme data heterogeneity and the volatile energy costs of mobile VNs. To overcome these challenges, we propose an auction-based sustainable federated learning (ASFL) framework. In this framework, the edge server acts as both the FL task publisher and the auctioneer, while VNs serve as bidders. Each bid encapsulates critical attributes including data quality, computing capacity, and projected energy cost. The core objective of ASFL is to maximize long-term social welfare. Formulating this objective reveals an inherently nonconvex optimization problem. Through rigorous analysis, we derive an equivalent convex formulation. The systematic bidder selection process inherent in ASFL simultaneously mitigates data heterogeneity and promotes rational energy utilization across FL. We theoretically prove that the framework achieves incentive compatibility and individual rationality. Experimental results on MNIST and CIFAR-10 datasets demonstrate the effectiveness of the method in mitigating the impact of non-i.i.d. data and reducing energy consumption.

Edge computing, an emerging paradigm, utilizes decentralized edge nodes to offer low-latency, high-quality network services. Quality of Service (QoS) is a crucial metric to measure network service quality, and network resource scheduling relies on QoS measurement results. However, current QoS measurement methods often measure all QoS data among edge nodes, these dense measurement approaches introduce significant costs. Besides, edge nodes may adopt varied network access manners, these factors cause fluctuations in QoS between edge nodes. But existing QoS measurement works often focus on measuring exact QoS values while merely considering QoS fluctuations, resulting in unreliable measured QoS data. In addition, existing QoS measurement methods often can not support online QoS measurement, leading to stale offline QoS data affecting network resource scheduling. To tackle the two issues, we propose a novel Continual Sparse QoS range measurement method (CSQoS) with four innovative designs: (1) To reduce measurement costs, we propose to measure QoS by only sampling partial QoS data and using them to impute unmeasured QoS data. To achieve sparse QoS imputation, we propose a novel variational Bayesian model (BayGNN) with an edge-enhanced Graph Neural Network (GNN) as the encoder for feature extraction and a Multilayer Perceptron (MLP) as the decoder to predict unmeasured QoS data. (2) To assess QoS data ranges, we design the proposed BayGNN model to produce uncertainty simultaneously. (3) To fulfill reliable online QoS predictions, we incorporate continual learning and residual connections in BayGNN. Experimental results on 2 real-world datasets demonstrate that CSQoS has minimal QoS imputation error with the lowest measurement costs, reducing 17.6% RMSE and 20% sampling costs.

Future wireless networks, such as 6G, are envisioned to deliver ultra-reliable, high-quality services with ultra-low latency and dynamic connectivity across heterogeneous environments, driving the adoption of edge–cloud collaborative architectures. Within this paradigm, container-based microservices, with their lightweight, modular, and portable characteristics, offer an effective foundation for scalable and adaptive service provisioning in heterogeneous wireless networks. The layered architecture of microservices facilitates efficient resource management through layer scheduling and caching. However, dynamic service requests and diverse container layers pose major challenges for layer-aware service provisioning in future wireless environments. These includetime-exceeded offline service provisioning, tangled microservice orchestration, andlayer cache redundancy. To address these challenges, we propose Tri-Ring, an asynchronous online provisioning framework for future wireless networks, empowered by large AI models (LAMs). The framework optimizes request dispatching, orchestration, and layer updates across three timescales. At the small timescale, we formulate request dispatching as a linear programming (LP) subproblem. At the medium timescale, the estimator-assessor algorithm manages microservice orchestration, where a diffusion-enhanced prediction model serves as the estimator to predict layer caching strategies. Moreover, submodular optimization serves as the assessor to determine deployment and scheduling. At the large timescale, we introduce the age of layer (AoL) to guide the pruning of infrequently accessed cached layers to reduce storage overhead. Comprehensive evaluations on real-world datasets demonstrates that Tri-Ring outperforms existing baselines, increasing utility by 44.78%, reducing microservice startup time by 78.64%, and optimizing storage resources by 36.38%.

Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is crucial for efficient application deployment and resource provisioning. However, the heterogeneous application patterns, variable infrastructure performance, and frequent deployments in MT-ECP pose significant challenges for accurate prediction. Existing clustering-based methods often incur excessive costs due to maintaining multiple data clusters and models, while end-to-end time-series prediction methods struggle with dynamic environments. To address these challenges, we perform a comprehensive analysis on a large-scale workload dataset in real-world MT-ECP and propose DynEformer, an end to-end framework with global pooling and static context aware ness, offering a unified workload prediction scheme for dynamic MT-ECP. Meticulously designed global pooling and information merging mechanisms can effectively identify and utilize global application patterns to drive local workload predictions. The integration of static content-aware mechanisms enhances model robustness in real-world scenarios. We also extend DynEformer's capabilities to Long-term workload forecasting (LTLF) and Long-period service (LPS) tasks. Experiments on six real-world datasets demonstrate that DynEformer achieves state-of-the-art performance, with a 32% relative improvement on nine baselines and a 52% improvement in application switching and new entity scenarios. Additional experiments on long-term prediction and online learning further confirm its effectiveness for LTLF and LPS tasks.

Deep neural network (DNN) model partitioning and pruning have proven to be effective methods for enhancing resource efficiency and reducing inference delay by strategically allocating DNN workloads across heterogeneous edge and cloud infrastructures. Nevertheless, the heterogeneous nature of resources complicates the deployment of DNN in mobile edge-cloud computing (MEC) networks. In this paper, we present an innovative framework for collaborative DNN inference in MEC networks by integrating fine-grained model partitioning and magnitude-based pruning. However, the joint model partitioning and pruning policy presents significant challenges due to the inherently coupled and mutually influential nature. To address it, we adopt Long Short-Term Memory (LSTM) networks as action generation controllers to generate discrete actions for model partitioning and pruning alternately. After that, we adopt the policy gradient algorithms to optimize the LSTM-generated actions with a moving average according to the Monte Carlo estimate. By directly optimizing the policy function, the proposed framework enhances the efficiency and stability of action space exploration, yielding faster convergence and improved inference performance. Experimental results on standard datasets indicate that the proposed framework outperforms state-of-the-art approaches, achieving an 8.247% increase in system reward and an average reduction of 27.313% in total delay within the considered MEC networks.

With the rapid advancement of mobile technologies, including self-driving cars and drones, the deployment of mobile software has become increasingly complex. In this context, virtualization plays a pivotal role by simplifying service deployment through containers and enabling container orchestration plat forms to efficiently manage an expanding number of container clusters. This is achieved by leveraging standardized interfaces and minimizing resource optimization overhead. However, the use of distributed servers in mobile edge clusters introduces several challenges, such as bandwidth limitations, network performance fluctuations, and resource constraints, which complicate deployment in these dynamic and resource-constrained environments. In this paper, we rethink the layer-based structure, a fundamental container design, and analyze the challenges and potential of real edge platform traces. Consequently, we propose BREAK, an acceleration middleware for efficient container deployment. With the primary insight of enhancing layer-reuse and deriving benefits from it, we develop a co-design approach centered on layer structure for efficient deployment, ensuring backward compatibility: (i) a container image refactoring solution that optimizes efficiency while preserving the stack-of-layers structure, (ii) distributed shared layer-stack caches, dynamically optimized for collaborative container deployment among mobile edge clusters, (iii) a customized Kubernetes (K8s) scheduler extending awareness of network performance, disk space, and container layer cache for container placement, and (iv) a tailored storage-driver of the standard container runtime for efficient layer extraction. Results indicate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to the state-of-the-art approach.

Federated learning (FL) that preserves privacy has appeared as a prospective paradigm in mobile edge computing networks. However, due to the system and data heterogeneity of mobile clients (MCs), group-based FL with a sampling mechanism is crucial for minimizing model training costs. To address these challenges, we investigate and formulate the problem of group-based FL with a sampling mechanism for reducing model training cost (i.e., latency and energy consumption), and propose a group-based FL with a cost-efficient sampling mechanism (GFLCSM) framework to address it. More precisely, before training, each MC locally pre-trains a model, estimates its data distribution from the classifier's gradient norms, and uploads it to the central server (CS) instead of raw data to preserve privacy. Using this information, the CS transforms vanilla FL into a group-based FL. During training, GFLCSM replaces the random sampling mechanism with a cost-efficient one. Moreover, to enhance robustness against network dynamics, we extend GFLCSM with a backup resampling mechanism, termed GFLCSM-E. Experimental results indicate that GFLCSM surpasses the baseline frameworks, reducing latency by 24.63% and energy consumption by 11.47% on average across two datasets, while GFLCSM-E maintains high performance even under client dropout. The source code address ishttps://github.com/kt4ngw/GFLCSM.

Deploying federated learning (FL) in mobile edge computing (MEC) networks has become a prevalent approach to distributed learning. However, the inherent heterogeneity in computing, transmission and data on edge devices (EDs) brings challenges in improving training efficiency and speed. Existing approaches primarily focus on increasing batch sizes or employing adaptive batching to expedite convergence, but often overlook the generalization ability of the model. In this paper, we propose an energy-efficient adaptive batching approach for FL in MEC networks, aiming at minimizing the energy consumption by balancing training efficiency and speed. Initially, we exploit the relationship between batch size and loss improvement while determining the optimal learning rate corresponding to the batch size and understanding the correlation among loss improvement, learning rate, and gradient noise scale (GNS). Then we dynamically adjust the batch size based on the GNS and propose a low-complexity approach for measuring GNS. Finally, we fine-tune the batch size by assessing gradient similarity on each ED to ensure an optimal level of gradient noise during training, thereby enhancing the model's generalization. Experiment results demonstrate the effectiveness of our approach with an approximate 50% and 20% reduction in energy consumption and time consumption compared with existing approaches.

To meet the demands of high-capacity and low-delay services, Fifth Generation (5G) Base Stations (BSs) are typically deployed in ultra-dense configurations, especially in urban areas. While this densification enhances coverage and service quality, it also leads to substantially increased energy consumption. However, the dense deployment pattern makes BS workloads more responsive to the spatiotemporal variations in user behavior, offering opportunities for energy-saving strategies that dynamically adjust BS operation states. In this context, we propose a Delay-aware and Energy-efficient Integrated Optimization System (DEIS) based on Deep Reinforcement Learning (DRL), which jointly optimizes energy consumption and network delay while maintaining user satisfaction. DEIS leverages a real-world dataset collected from operational 5G BSs provided by partner network operators, containing both BS deployment data and high-volume user request logs. Extensive simulations demonstrate that DEIS can achieve a 41% reduction in energy consumption while ensuring reliable delay performance.

Computing Power Networks (CPNs) represent an innovative, collaborative architecture that integrates resources via the communication network, optimizing resource allocation to support service demands. Due to the increased need for services powered by artificial intelligence across various domains, CPNs are increasingly required to allocate users efficiently to appropriate servers to meet the low-latency needs of service computing. However, challenges such as users' dynamic mobility, weak communication paths, and high-dimensional solution spaces persist in optimizing user allocation in CPNs. In this context, we propose a diffusion-driven optimization approach for mobility-aware user allocation. To tackle the challenge of users' dynamic mobility, we adopt a user location prediction approach incorporating the users' movement patterns to forecast future movement, calledCAMPE. To tackle the challenge of weak communication paths, we establish the new transmission path by reconfigurable intelligent surface and enhance the quality of the communication link by adjusting the phase configurations. Moreover, faced with the challenge of high-dimensional solution spaces associated with phase adjustment and user allocation decisions, we devise an action-generation strategy based on diffusion models namedDiffUser. This approach motivates the generation of optimal solutions even in complex and dynamic environments. Finally, we conduct extensive simulations in user location prediction and system latency optimization. Compared with other solutions, the superiority of our approach has been demonstrated.

With the rapid deployment of large language model (LLM) technology across various fields, numerous LLMs have been deployed on edge servers (ESs) to provide low-latency generative services for edge devices. However, as factual knowledge evolves, the massive number of parameters in LLMs poses significant challenges for updating LLMs on ESs. Existing studies employ federated fine-tuning to update LLMs, but these unconstrained updating approaches can lead to overfitting and knowledge forgetting, while also resulting in substantial overhead. Knowledge editing (KE), as a promising technology, ensures the injection of new knowledge while preserving existing knowledge by editing specific parameters. In this paper, we propose multi-ES collaborative KE for the first time and design the CoKE and pCoKE frameworks in edge-cloud scenarios. These frameworks enhance editing efficiency by extracting identical expressions of the same knowledge across different LLM parameters for collaborative editing across multiple ESs. Additionally, we incorporate a personalized selection module in pCoKE to provide domain-specific generative services on ESs. To further reduce editing latency, we design a binary search-based resource allocation algorithm to balance editing latency across ESs. Extensive experiments demonstrate that CoKE and pCoKE reduce editing latency by 73% while maintaining high editing quality. Moreover, pCoKE achieves an approximately 6% improvement in editing quality.

Federated learning (FL) enables model training on mobile clients (M Cs) while protecting data privacy by keeping the data local. However, the data and system heterogeneity among MCs can significantly undermine model performance, slow convergence, and increase energy consumption. To achieve green and efficient edge intelligence, we propose an energy consumption optimization problem under the FL framework for mobile edge computing networks in this paper. Our goal is to reduce the energy consumption of MCs and improve the FL model performance. Then we design a heterogeneity-aware client selection and gradient optimization (HCSGO) algorithm. Specifically, HCSGO selects MCs based on data, computation, and communication quality to mitigate the impact of heterogene-ity on model performance, while leveraging a residual gradient mechanism to optimize gradient aggregation and accelerate convergence. Experiment results demonstrate that the proposed algorithm achieves the lowest energy consumption and improves the model performance compared to the baselines.

Proactive SSD failure prediction can help maintenance personnel in addressing failing drives in advance and has long been a major research direction in the field of dependable systems. However, the improvement of existing modeling methods' accuracy has been severely hindered by issues such as inconsistent data distributions, extreme data imbalance, and dynamic changes in the correlation between attributes and failures. Herein, we propose an innovative framework called GMPpredictor, which significantly improves the accuracy of SSD failure prediction. Specifically, GMPpredictor first partitions the data based on drive models to handle the distribution differences among different models. Secondly, in addressing the issue of extreme data imbalance, we optimize both the data and model levels, employing a dynamically adjusted loss function to balance the class weights. Then, by leveraging gradient information, we assign higher weights to features that are strongly correlated with failures, further enhancing the model's focus on critical features. Finally, we combine LSTM and xLSTM in a hybrid structure for failure prediction, fully utilizing the advantages of both networks to handle complex patterns in failure data across different drive models. GMPpredictor achieves a precision of$\mathbf{9 3. 7 7 \%}$and an F0.5 score of 85.44 %, with a FAR of only 0.05 %. Notably, we have evaluated the effectiveness of GMPpredictor using realworld data collected from large-scale solid-state drives in data centers, achieving successful application from laboratory-scale to industry-scale.

With the rapid proliferation of streaming services, network load exhibits highly time-varying and bursty behavior, posing serious challenges for maintaining Quality of Service (QoS) in Crowdsourced Cloud-Edge Platforms (CCPs). While CCPs leverage Predict-then-Schedule architecture to improve QoS and profitability, accurate load forecasting remains challenging under traffic surges. Existing methods either minimize mean absolute error, resulting in underprovisioning and potential Service Level Agreement (SLA) violations during peak periods, or adopt conservative overprovisioning strategies, which mitigate SLA risks at the expense of increased resource expenditure. To address this dilemma, we propose HRS, a Hybrid Representation framework with Scheduling awareness that integrates numerical and image-based representations to better capture extreme load dynamics. We further introduce a Scheduling-Aware Loss (SAL) that captures the asymmetric impact of prediction errors, guiding predictions that better support scheduling decisions. Extensive experiments on four real-world datasets demonstrate that HRS consistently outperforms ten baselines and achieves state-of-the-art performance, reducing SLA violation rates by 63.1% and total profit loss by 32.3%. Our code is available at [29].

As the rapid development of artificial intelligence (AI) and large language models (LLM), how to deploy related applications onto computing nodes has become a hot topic, and containerized service provides an excellent approach for this. The most time-consuming step of this approach is image extraction, the procedure of decompressing all layers of image package downloaded from remote image registry. Therefore, achieving fast image extracting is crucial for the efficient deployment of AI services. In this paper, we introduce a collaborative orchestration and extraction strategy, CORES. Firstly, we eliminate the dependencies among image layers, which impede unordered extraction of image layers. Based on this, we model the image extraction as a mixed integer linear programming (MILP) problem, aiming to minimize total extraction time. Then we use improved Benders decomposition to iteratively obtain a near-optimal solution with lower time complexity. Extensive experiments conducted on the real system validate the superior performance of our strategy. Compared with our closest baseline LOPO, CORES reduces the average image extracting time by 19.60%, significantly enhancing the efficiency of AI service deployment.

The rapid development of Artificial Intelligence Generated Content (AIGC) technology has led to a strong demand for elastic computing resources. The crowdsourced edge cloud system builds a flexible resource pool by integrating heterogeneous idle servers and even personal devices to meet the dynamic computing requirements of AIGC services. The system relies on the serverless architecture to realize the dynamic scheduling of resources, which needs to trade off resource benefits and energy consumption costs to improve the overall social welfare, resource efficiency, and environmental sustainability. However, challenges such as unfair resource pricing, dynamic resource availability, and the complexity of strategy optimization remain unresolved for green and efficient resource management. In this paper, we propose a resource management framework named ReFluid. We introduce a game-theoretical pricing model to ensure fair pricing, a fluid model-based analysis for promoting a more sustainable management of computing resources, and a diffusion-based optimization mechanism to enhance model stability and adaptability. The evaluation shows that ReFluid significantly improves average social welfare and reduces energy consumption.

With the emergence of massive data and advances in artificial intelligence (AI), large-scale transformer based model have demonstrated superior performance. As its critical applications, performing inference near users, e.g., distributed inference on edges, can significantly reduce response latency. Thus, many studies have investigated collaborative inference across edges. However, collaborative inference across edges has brought about significant resource constraints and heterogeneity. These characteristics have posed unprecedented challenges, including the inaccurate parallelism mechanism, and low-time efficient inference decision. In this paper, we propose a chunking adaptive mixed parallelism mechanism, namely ChAMP. To tackle the problem of inaccurate parallelism, we introduce a transformer chunking adaptive mechanism, to adapt the input sequence chunking and the workload distribution, and use a hybrid parallelism approach. To tackle the challenges of low-time efficient inference decision, we learn an adaptive inference adjustment, where a hierarchical reinforcement learning method is designed to perform local chunking adjustments and real-time optimization. Finally, we evaluate the performance of ChAMP in heterogeneous edge computing environments. We compare ChAMP with two state-of-the-art parallel approaches, i.e., achieved a time efficiency increase of approximately 23.8% compared to M-LM and 18.0% compared to SP.

End-edge collaborative inference refers to the fact that edge servers (ESs) and end devices (EDs) jointly participate in inference tasks, which can not only reduce communication latency and bandwidth consumption with the cloud but also protect user data privacy. However, existing collaborative inference methods do not fully consider the limited resources of EDs and ignore the latency and accuracy requirements of different inference tasks. In this paper, we design a DNN inference acceleration framework to balance inference latency and accuracy. Specifically, we first use a compression method based on deep reinforcement learning to determine the compression ratio and deeply compress the original model to reduce the complexity of the model. To reduce the cumulative error caused by compression, a knowledge distillation-based scheme is used to fine-tune the compressed model. Finally, the DNN model is partitioned and deployed on the ED and ES, respectively. Extensive experiments demonstrate the effectiveness of the framework in achieving lowlatency DNN inference on demand.

Efficient workload forecasting is pivotal for both service orchestration and request dispatching in quality of service (QoS)-oriented multi-tenant edge cloud platforms (MT-ECPs) with a native tiered architecture. However, the spatial-temporal heterogeneity and structural constraints of native tiered architecture present significant challenges for the forecasting in sophisticated MT-ECPs. To tackle these challenges, we propose SCOUT, which is a novel Self-supervised learning-enhanced Cloud-edge collabOrative Unified workload forecasTing framework. First, we design a cross-granularity collaborative mechanism that enables SCOUT to balance accuracy and efficiency in forecasting within the tiered architecture of MT-ECPs. Notably, we employ an auxiliary self-supervised learning method at the cloud that enhances workload pattern representations, making them reflective of both spatial and temporal heterogeneity. Extensive experiments on two real-world workload datasets show that SCOUT outperforms state-of-the-art methods for MT-ECP's workload forecasting, decreases time consumption and reduces communication costs.

With the rise of AI-Generated Content (AIGC) services, deployments within edge-cloud environments are becoming increasingly prevalent. Containerization offers resource isolation, lightweight deployment, and portability, making it a suitable technology for AIGC services. However, deploying AIGC services often requires large container images, leading to high deployment latency and bandwidth consumption. Based on real-world trace analysis showing the long-tail effect, where a few popular images account for the majority of requests, there is strong potential for optimizing caching mechanisms. This pattern can result in frequent cache misses and increased bandwidth consumption, especially under heavy load. In this paper, we propose a ValueOptimized Distributed Caching Mechanism (VODCM), which dynamically optimizes caching policies through a value-driven framework combined with deep reinforcement learning (DRL). VODCM prioritizes high-value images based on access frequency, layer size, and network latency, significantly improving cache hit rates and reducing network overhead. Preliminary evaluations show that VODCM enhances cache efficiency and reduces network and resource demands, offering an effective solution for AIGC image management in edge-cloud environments.

In mobile edge computing (MEC) networks, federated learning (FL) has emerged as the leading distributed framework for training a shared machine learning model, primarily benefiting from its ability to exchange the information of edge devices (EDs) while safeguarding their privacy. However, in MEC networks, the heterogeneity of communication, computation, and data can result in challenges such as stragglers and data imbalances, thereby impeding the training process of FL. To address these challenges, we propose a Semi-Asynchronous Federated Learning (Semi-AFL) framework with cluster-based scheduling. In Semi-AFL, the EDs can perform local training at their own pace using different stale global models to tackle the straggler effect. Considering the asynchronousity of Semi-AFL and data heterogeneity, we propose a cluster-based scheduling strategy that includes device clustering and device selection. Specifically, it performs clustering based on the label distribution and obtains device-to-cluster information. We further select devices based on clustering information as well as model staleness and contribution, aiming to reduce variance and bias and accelerate model convergence. Experiment results demonstrate the effectiveness of the proposed method in reducing the latency of FL.

Pre-trained Transformer-based large models have revolutionized personal virtual assistants, but their deployment in cloud environments faces challenges related to data privacy and response latency. Deploying large models closer to the data and users has become a key research area to address these issues. However, applying these models directly often entails significant difficulties, such as model mismatching, resource constraints, and energy inefficiency. Automated design of customized models is necessary, but it faces three key challenges, namely, the high cost of centralized model customization, imbalanced performance from user heterogeneity, and suboptimal performance from data heterogeneity. In this paper, we propose ACME, an adaptive customization approach of Transformer-based large models via distributed systems. To avoid the low cost-efficiency of centralized methods, ACME employs a bidirectional single-loop distributed system to progressively achieve fine-grained collaborative model customization. In order to better match user heterogeneity, it begins by customizing the backbone generation and identifying the Pareto Front under model size constraints to ensure optimal resource utilization. Subsequently, it performs header generation and refines the model using data distribution-based personalized architecture aggregation to match data heterogeneity. Evaluation on different datasets shows that ACME achieves cost-efficient models under model size constraints. Compared to centralized systems, data transmission volume is reduced to 6%. Additionally, the average accuracy improves by 10% compared to the baseline, with the trade-off metrics increasing by nearly 30%.

In the Fifth Generation (5G) era, different operators keep their Base Stations (BS) active in the same area to ensure user service coverage. However, this high level of redundant coverage leads to significant energy waste, especially given the high power consumption of 5G BSs. To address this issue, this paper proposes the Collaborative Operator Partnership based Energy-efficient framework (COPE), a 5G network optimization approach based on multi-operator collaboration. COPE encourages operator cooperation through revenue incentives, combines time series analysis to predict user distribution and service demand, and uses deep reinforcement learning to guide BS leasing, facilitating cross-operator resource collaboration. Extensive experimental results demonstrate that COPE reduces total BS energy consumption by approximately 19% and optimizes costs by about 9% in multi-operator scenarios.

Artificial Intelligence Generated Content (AIGC) services based on text-to-image generation tasks have driven change in the AI industry in recent years. Diffusion models are widely used in AIGC services for generating high-quality images from complex prompts. However, the process of generating diffusion models requires a large number of autoregressive denoising steps, posing significant challenges related to service latency and privacy issues, especially in resource-constrained edge devices. To further improve the quality of service (QoS) of edge AIGC services, we design a multi-edge cooperative caching mechanism based on the idea of reusing early intermediate results of similar prompts to reduce denoising steps. First, a collaborative filtering algorithm based on prompt similarity is developed to analyze user preferences. The designed multi-agent reinforcement learning-based cache management (MARLCM) algorithm utilizes the preference data as input and determines the caches that need to be replaced in the cache space. Secondly, we propose an adaptive edge server cooperation strategy that instructs servers to build efficient cache pools based on server similarity and workload differences. The experiment conducted on a high-resolution generation task with multiple diffusion cache frameworks shows that the proposed method reduces task latency by 23.1 %, and achieves up to 1.57 times higher cache hit rate compared to existing methods.

Crowdsourced edge-cloud clusters utilize idle third-party resources to provide cost-effective, scalable environments. This decentralized model reduces capital expenditures and carbon footprints but introduces hardware instability, as servers may unpredictably join or leave, affecting service availability. While integrating serverless computing enables automated management to lower operational costs, the dynamic nature of server availability still significantly impacts service quality. In this paper, we present RESCUER, a QoS-aware service rescheduling framework for serverless crowdsourced edge-cloud clusters. Partnering with a real-world provider, we collected data from over 10,000 edge servers over 400+ days, enabling a detailed analysis of server availability patterns. Based on these insights, we propose a predictive algorithm that forecasts the future online duration of each node, assigning them labels based on their predicted availability. Additionally, we introduce a rescheduling algorithm that combines these labels with node resources, latency constraints, and other factors to select the most suitable node for deploying containers. Evaluations on the real-world trace show that RESCUER significantly improves service availability and system efficiency compared to existing methods.

The rapid evolution of Industrial Cyber-Physical Systems (ICPS) with cloud-fog automation calls for the deployment of Deep Neural Networks (DNNs) on edge devices to enable intelligent and autonomous decision-making. However, the resource constraints, heterogeneity, and dynamic nature of edge devices pose limitations to the efficient deployment of DNNs. Federated Learning-based Neural Architecture Search (FL-NAS) has been proposed to address these limitations, but achieving an effective balance between the generalized global model and personalized local models remains a non-trivial task due tosuboptimal aggregation of homogeneous neural blocks, knowledge waste of heterogeneous neural blocks, and high communication and energy overhead. In this paper, we proposeF²NAS, an energy-friendly federated neural architecture search framework tailored for ICPS. The fine-grained aggregation strategy adapts weights for each device during aggregation, enhancing the global and personalized local models. The bidirectional knowledge transfer mechanism leverages heterogeneous neural blocks, promoting knowledge sharing among local and global models. The adaptive communication strategy optimizes interactions between edge devices and the cloud server based on model performance, reducing energy costs while maintaining effective model collaboration. Extensive experiments demonstrate thatF²NASoutperforms baselines by up to 30.31% in accuracy on edge devices, 38.75% on the cloud server, and achieves a 65.2% reduction in energy consumption. When applied to the surface defect detection task in ICPS,F²NASsurpasses other baselines by up to 13.38% and 302.46% for edge devices and cloud servers, respectively, and reduces energy consumption by 44.8%.

In edge computing, energy-limited distributed edge clients present challenges such as heterogeneity, high energy consumption, and security risks. Traditional blockchain-based federated learning (BFL) struggles to address all three of these challenges simultaneously. This article proposes a Graph-Partitioning Multi-Granularity Federated Learning method on a consortium blockchain, namely GP-MGFL. To reduce the overall communication overhead, we adopt a balanced graph partitioning algorithm while introducing observer and consensus nodes. This method groups clients to minimize high-cost communications and focuses on the guidance effect within each group, thereby ensuring effective guidance with reduced overhead. To fully leverage heterogeneity, we introduce a cross-granularity guidance mechanism. This mechanism involves fine-granularity models guiding coarse-granularity models to enhance the accuracy of the latter models. We also introduce a credit model to adjust the contribution of models to the global model dynamically and to dynamically select leaders responsible for model aggregation. Finally, we implement a prototype system on real physical hardware and compare it with several baselines. Experimental results show that the accuracy of the GP-MGFL algorithm is 5.6% higher than that of ordinary BFL algorithms. In addition, compared to other grouping methods, such as greedy grouping, the accuracy of the proposed method improves by about 1.5%. In scenarios with malicious clients, the maximum accuracy improvement reaches 11.1%. We also analyze and summarize the impact of grouping and the number of clients on the model, as well as the impact of this method on the inherent security of the blockchain itself.

While diffusion models have demonstrated remarkable success in computer vision tasks, their deployment in Internet of Things environments remains challenging. Edge devices face significant constraints in computational resources and must adapt to dynamic operating conditions. To address these limitations, we propose a novel system that accelerates AIgenerated content (AIGC) collaborative inference in dynamic edge networks. The proposed system introduces a multi-exit vision transformer-based U-Net architecture that enables efficient processing through adaptive exit point selection during the diffusion process, optimizing the trade-off between inference accuracy and computational efficiency. To optimize device-level operations, we develop an innovative generative AI-assisted reinforcement learning framework that determines optimal exit selection and offloading strategies to maximize generation quality and inference speed. Furthermore, we design a fine-tuning approach with policy reuse mechanisms that facilitates rapid reinforcement learning algorithm deployment across diverse environments. Extensive experimental evaluations demonstrate that our system outperforms existing algorithms in terms of balancing inference latency and generation quality, while also exhibiting improved adaptability to environmental variations.

Real-time video detection is essential for many mobile visual applications, which brings the heavy computational burden of deep neural networks. Mobile edge computing offers a promising solution by deploying computational resources near mobile devices. However, achieving efficient video detection on mobile devices requires addressing challenges such as different performance requirements, diverse computing and network conditions, and system dynamics. We propose a realtime video detection framework in mobile edge computing, where multiple video streams from mobile devices are processed while balancing key performance metrics with consideration of grouping. A joint optimization problem of task scheduling, model selection, and resource provisioning is formulated for the system, where decisions are made on two timescales. To this end, we propose a window controller to unify decision-making at the time-slot level. We design an online scheduling algorithm based on multi-agent deep reinforcement learning to enable adaptive and distributed scheduling, while a masking-enhanced attention mechanism enables efficient explicit information exchange between mobile devices. Experimental evaluations across different numbers of mobile devices demonstrate that, in terms of average reward, the proposed algorithm outperforms local processing by 14.600%, fixed offloading by 10.007%, and four learning-based scheduling baselines by an average of 2.267%.

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution; For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets.

Mobile Edge Computing (MEC) and Device-toDevice (D2D) peer offloading are two promising paradigms in the mobile Internet of Things (IoT). In this paper, we study the collaborative task offloading with redundant data and codes in large-scale IoT networks, where computing resource-starved IoT devices can offload their tasks to MEC servers via cellular links or to nearby peer devices (PDs) with idle resources through D2D links for execution. IoT tasks usually consist of a series of dependent and parallel subtasks, and the difficulties in current research are (i) how to eliminate redundancy in data or codes between subtasks, and (ii) how to leverage previous experience to adaptively search a set of collaborative MEC servers and PDs for matching offloading of dependent and parallel subtasks. From this, we propose a redundancy-aware adaptive search offloading (RASO) method based on the deep Q-network (DQN). Specifically, we first design a fine-grained task recombination scheme by judging the consistency of subtask data and codes. After that, we organize the global devices into a spatial index MP-tree to reduce the search solution space, and propose a fast adaptive search method based on the DQN combined with MP-tree, where optimal path-guiding parameters training of inner and outer layers is involved to efficiently help achieve collaborative devices to complete specific tasks with the same type. After finding the collaborative MEC servers and PDs along MP-tree for a certain task, a centralized stable matching algorithm is further developed to give a decision of offloading each of its divided dependent and parallel subtasks to the matched one, thereby optimizing offloading delay and energy consumption. Extensive simulation results show that compared to other counterpart solutions, our proposed method has improved task offloading performance in terms of delay and energy consumption.

The new real-time interactive services, such as virtual and augmented reality, demand significantly higher network bandwidth and quality, which the traditional centralized cloud struggles to meet. In addition, centralized optimization management becomes inefficient as the scale of the scene continues to expand. In response, edge cloud systems have emerged, but distributed geographic locations, burstable billing business models, and large numbers of servers in large-scale scenarios pose new challenges for resource management. In this article, we proposeGeoCC, a novel strategy to save bandwidth overhead in burstable billing edge cloud systems.GeoCCaddresses challenges through a dual approach. First, a geography-aware graph construction and partitioning algorithm is used to organize server resources, and a large number of servers are reasonably divided into multiple server pools for parallel processing. Second, it introduces an enhanced burstable billing optimization mechanism that considers contextual factors and adaptive bandwidth capacity. Experiments based on real data from an edge cloud operator demonstrate the effectiveness ofGeoCC. Compared with the baseline,GeoCCcan effectively reduce bandwidth peaks, decreasing bandwidth costs by an average of 28.30% and up to 81.83% at the 95th percentile billing.

The escalating demands for Artificial Intelligence-generated content (AIGC) services greatly require computing resources. The edge-cloud market offers an effective solution for AIGC services by integrating, managing, and trading distributed computing resources. Within this novel service market, participants contribute idle resources to support AIGC services to earn income, creating a more flexible market environment. Meanwhile, the generation quality and computing resource requirements of AIGC services are related to input prompts. Therefore, this relationship introduces new challenges, such asthe information uncertainty in input prompts, the inability to model resource continuity, and high-dimensional complexity for optimization.In this paper, we propose a resource fluid equilibrium management strategy for supporting AIGC services within edge-cloud market, termedFluE. To address the challenge of information uncertainty in user prompts, we measure the content value of AIGC prompts by information entropy and introduce a redundancy reduction approach to focus on meaningful information in prompts. To tackle the challenge of the inability to model the continuity provision of computing resources, we utilize the fluid model to ensure seamless resource provision and facilitate a more balanced management of computing resources. To address the challenge of high-dimensional complexity of strategy optimization, we develop a diffusion-based algorithm namedReDiffto reconstruct the target strategy distribution and generate precise and effective optimization decisions. We evaluate our proposed scheme under a dynamic resource provisioning environment. Based on the DiffusionDB dataset, the publicly available real trace of AIGC service prompt, ourReDiffalgorithm achieves up to 69.8% and 77.4% improvements in average social welfare compared to LySAC and CD-PPO, respectively.

Federated learning (FL), an advanced variant of distributed machine learning, enables clients to collaboratively train a model without sharing raw data, thereby enhancing privacy, security, and reducing communication overhead. However, in edge computing scenarios, there is an increasing trend towards diversity, heterogeneity, and complexity in clients’ data and models. The fundamental challenges, such as non-independent and identically distributed (non-IID) data and multi-granularity data accompanied by model heterogeneity, have become more evident and pose challenges to collaborative training among clients. In this paper, we refine the FL framework and propose the Multi-granularity Weighted Federated Learning (MGW-FL), emphasizing efficient collaborative training among clients with varied data granularities and diverse model scales across distinct data distributions. We introduce a distance-based FL mechanism designed for homogeneous clients, providing personalized models to mitigate the negative effects that non-IID data might have on model aggregation. Simultaneously, we propose an attention-weighted FL mechanism enhanced by a prior attention mechanism, facilitating knowledge transfer across clients with heterogeneous data granularities and model scales. Furthermore, we provide theoretical analyses of the convergence properties of the proposed MGW-FL method for both convex and non-convex models. Experimental results on five benchmark datasets demonstrate that, compared to baseline methods, MGW-FL significantly improves accuracy by almost 150% and convergence efficiency by nearly 20% on both IID and non-IID data.

Responding to the escalating interest in long-term forecasting within the industry, we introduce EasyTS, a comprehensive toolkit engineered to streamline data collection, analysis, and model creation procedures. EasyTS acts as a unified solution, driving progress in long-term time series forecasting. The platform provides effortless access to various time series datasets, including a newly open-sourced multi-scenario dataset in the electricity domain. Integrated visualization and analysis tools help unveil inherent data features and relationships. EasyTS facilitates a user-friendly model validation approach with versatile evaluation criteria. This toolkit allows researchers to compare their models proficiently against renowned benchmarks. With our ongoing commitment to expanding our dataset collection and enhancing toolkit functionalities, we aspire to contribute significantly to the time series forecasting domain. Code is available at this repository: https://github.com/EdgeBigBang/EasyTS.git.

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE.

The presence of Artificial Intelligence Generated Content (AIGC) has garnered widespread interest. AIGC enables content creation by analyzing big data, leveraging the capabilities of extensive AI models, and substantial AI computing. Computing power networks (CPNs) represent an excellent approach for offering pervasive AI computing resources to AIGC. However, these characteristics have posed unprecedented challenges to the CPNs helped AIGC, including the uncertainty of prompts’ information value, the inability to model the continuity of computing resources, and the incapacity to represent complex multi-dimensional spaces. In this paper, we propose a computing resources equilibrium strategy based on the fluid model for AIGC helped by CPNs, namely FluE. This mechanism obtains information entropy by constructing an AIGC prompt tree to measure the information value of AIGC prompts. In addition, we model the continuity of computing resources by the fluid model. A fluid-stopping equilibrium strategy is formulated to obtain the average fluid level of computing resources based on the Laplace-Stieltjes transform. To solve the equilibrium strategy, we develop a diffusion-based algorithm for FluE to adjust the fluid policy dynamically to maximize resource rewards. Finally, the evaluations demonstrate improvements in average social welfare.

With the rapid increase in user scale and the explosive rise of emerging applications, the contradiction between heavy load pressure and excellent network performance is becoming increasingly prominent, and task processing is gradually shifting towards the edge of the network. However, the resources of edge networks are limited, making it difficult to meet the huge computing and storage needs, and managing and allocating edge nodes is also a huge challenge. The Kubernetes (K8S) framework for deploying and orchestrating containerized applications provides a solution for this. How to improve the adaptability of K8S in edge networks, meet the demand of services for heterogeneous resources, and train decision models with better performance using limited datasets has become an urgent problem to be solved. Based on the above issues, we propose a distributed service migration architecture for multi-user access, and design a service migration algorithm based on imitation learning to achieve resource combination optimization and reduce the impact of insufficient data on model training. Design agent models based on diffusion models to accelerate model convergence and avoid the increase in training costs caused by constantly updating agent models. Our results show that the efficiency of the expert model is 92.0%, and the learning process of the agent model can converge within 100 training cycles with an accuracy of 97.89%. The service processing delay, throughput rate, and model convergence are all significantly better than those of classical algorithms.

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm’s ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content.

With the continuous development of technologies such as the Internet of Things (IoT), scheduling issues in edge-cloud systems are becoming a research focus. Deep reinforcement learning (DRL) has become an effective way to address scheduling issues in edge-cloud systems due to its ability to interact with the environment and engage in adaptive learning to solve complex decision-making problems. However, due to the increasing scale of edge-cloud systems, traditional DRL still faces challenges in scheduling, such as slow convergence and high computational requirements. To address these challenges, we propose a diffusion model-based deep reinforcement learning algorithm called DMaiS, which utilizes the diffusion model as the policy network in the advantage actor-critic (A2C) to expedite policy learning. Additionally, we develop a distributed service orchestration approach utilizing multi-agent advantage actor-critic (MAA2C) to effectively and flexibly manage extensive and intricate cloud service resources. The experimental results using real-world data demonstrate that compared to the baseline algorithms, DMaiS achieves a higher system throughput rate and a lower scheduling latency when managing scheduling for the edge-cloud system. It also exhibits a faster convergence speed compared to traditional DRL algorithms.

Computation offloading is widely used in vehicular edge computing (VEC) networks to satisfy the computational intensity and latency sensitivity requirements. However, many existing offloading algorithms do not comprehensively consider the dynamically changing characteristics of heterogeneous tasks within a roadside unit (RSU), resulting in tasks timeout and being dropped. In this paper, we design a competitive and cooperative computation offloading (C3O) model to reduce task execution latency. Specifically, when intensive heterogeneous tasks are generated, these vehicles jointly compete for the computing resource of a RSU, or they can also offload tasks to the task vehicle (TaV) with free computing resource. Meanwhile, We analyze the latency model of local execution and offloading to RSU or TaV execution and formulate a sequential task offloading decision problem, NP-hard. To address it, we propose a multi-agent reinforcement learning algorithm based on C3O (MARC3O) to intelligently determine the computation offloading policy for each vehicle according to the state of VEC networks. Simulation results demonstrate that the proposed algorithm can significantly reduce task execution latency and improve task completion rates compared with baseline schemes.

With the rapid development of Internet of Things (loT) device performance, edge-cloud systems are generating vast volumes of complex data. Meanwhile, the distributed and multi-layer structure of edge-cloud systems pose significant challenges to scheduling decision-making and convergence of the algorithm. In this paper, from a vertical and horizontal perspective of edge-cloud systems, we propose a deep reinforcement learning (DRL) algorithm called CrossLearning. We apply a curiosity-driven multi-agent learning method horizontally to accelerate the convergence speed of the algorithm. We introduce an inter-layer decision refinement mechanism vertically to address the challenge of inaccurate decision-making. We also refine the service types and levels to efficiently match the various application needs of users in the big data era. Finally, we implement a prototype system on a network hardware system and conduct experiments using real datasets. The evaluation shows that, in comparison to baseline methods, CrossLearning demonstrates significant im-provements in terms of time efficiency and load balance, with a notable enhancement in algorithm convergence speed.

As a robust platform for mobile edge computing, 5G networks, while delivering high data rates and low latency, face a pressing concern with the escalating energy consumption of 5G Base Stations (BSs). To address this issue, we propose an algorithm called the Environmental Protection Prophet (EPP), based on the clustered and geographically inclined user request patterns. The EPP algorithm groups users according to their proximity to BSs and utilizes edge caching to reduce response times for user requests. This clustering strategy optimizes user allocation while minimizing BSs' energy consumption, all while meeting user Quality of Service (QoS) requirements. Simulation experiments illustrate the potential for energy savings and latency reduction, particularly in densely populated urban areas. The findings provide valuable insights for the design of energy-efficient 5G networks, concurrently addressing environmental concerns and meeting user performance expectations.

To address network congestion and data privacy concerns, federated learning (FL) that combines multiple clients and a parameter server has been widely used in mobile edge computing (MEC) networks to process the abundant data generated by mobile clients. However, the existing client sampling methods do not adequately consider the data heterogeneity and system heterogeneity. Parameter server selects inappropriate clients to participate in the FL training process. This inevitably leads to slower convergence of the global model and higher energy consumption. In this paper, we design a client sampling model with the goal of selecting suitable clients to improve the energy efficiency of FL in heterogeneous MEC networks. Then we propose an energy-efficient client sampling strategy by quantifying the communication capability, computation capability and data quality of clients. Based on the quantization results, clients are assigned with a corresponding sampled probability. Simulation results show that our proposed strategy can effectively accelerate the convergence of the global model and reduce the energy consumption compared with the baseline schemes.

The rapid growth of edge computing calls for fine-tuned deep neural network (DNN) deployment that emphasizes energy-efficient implementation, due to the resource constraints of edge devices. Traditional Federated Learning-based Neural Architecture Search (FL-based NAS) has been instrumental in the complexities of this deployment, particularly in addressing constraints posed by device heterogeneity, limited resources, and privacy preservation. However, it is hindered by issues such as suboptimal aggregation of homogeneous neural blocks, significant knowledge waste in disregarding heterogeneous neural blocks, and excessive communication energy consumption. This paper introduces F2NAS in green edge computing, a novel energy-efficient approach that addresses these limitations by ensuring flexible and energy-efficient model design and training for edge devices. Firstly, F2NAS introduces an innovative aggregation strategy that enhances the integration of homogeneous neural blocks by using inter-block distances to optimize weight allocation. Further, it employs a unique parameter extraction technique that recaptures valuable insights from previously overlooked heterogeneous neural blocks. Finally, F2NAS meticulously calibrates communication energy consumption by balancing loss function and model interaction, setting and refining an upper limit for model communication. Experimental results reveal F2NAS enhances model accuracy by 2.8% to 4.7%, simultaneously reducing the energy consumption by nearly 50% through optimizing the communication cost.

Container technology has revolutionized service deployment, offering streamlined processes and enabling container orchestration platforms to manage a growing number of container clusters. However, the deployment of containers in distributed edge clusters presents challenges due to their unique characteristics, such as bandwidth limitations and resource constraints. Existing approaches designed for cloud environments often fall short in addressing the specific requirements of edge computing. Additionally, very few edge-oriented solutions explore fundamental changes to the container design, resulting in difficulties achieving backward compatibility.In this paper, we reevaluate the fundamental layer-based structure of containers. We identify that the proliferation of redundant files and operations within image layers hinders efficient container deployment. Drawing upon the crucial insight of enhancing layer reuse and extracting benefits from it, we introduce BREAK, a holistic approach centered on layer structure throughout the entire container deployment pipeline, ensuring backward compatibility. BREAK refactors image layers and proposes an edge-oriented cache solution to enable ubiquitous and shared layers. Moreover, it addresses the complete deployment pipeline by introducing a customized scheduler and a tailored storage driver. Our results demonstrate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to state-of-the-art approaches.

As live streaming services skyrocket, Crowdsourced Cloud-edge service Platforms (CCPs) have surfaced as pivotal intermediaries catering to the mounting demand. Despite the role of stream scheduling to CCPs’ Quality of Service (QoS) and throughput, conventional optimization strategies struggle to enhancing CCPs’ revenue, primarily due to the intricate relationship between resource utilization and revenue. Additionally, the substantial scale of CCPs magnifies the difficulties of time-intensive scheduling. To tackle these challenges, we propose Seer, a proactive revenue-aware scheduling system for live streaming services in CCPs. The design of Seer is motivated by meticulous measurements of real-world CCPs environments, which allows us to achieve accurate revenue modeling and overcome three key obstacles that hinder the integration of prediction and optimal scheduling. Utilizing an innovative Preschedule-Execute-Re-schedule paradigm and flexible scheduling modes, Seer achieves efficient revenue-optimized scheduling in CCPs. Extensive evaluations demonstrate Seer’s superiority over competitors in terms of revenue, utilization, and anomaly penalty mitigation, boosting CCPs revenue by 147% and expediting scheduling 3.4× faster.

Measurements for the quality of end-to-end network services (QoS) are crucial to ensure stability, reliability, and user experience for distributed edge clouds. Measuring all QoS data brings significant costs. Existing QoS measurement methods attempt to use sparse measured QoS data to estimate unmeasured QoS data. But they suffer from limited estimation accuracy when facing QoS data with high sparsity or significant volatility. Moreover, they also consume high sampling and training costs during continuously online measurements. Our preliminary analysis reveals that end-to-end QoS is strongly temporal-spatial related. It inspires us to leverage partially measured QoS data to impute temporal-spatial-related unmeasured QoS data for reducing measurement costs. To predict unmeasured QoS data precisely with low computational costs, we propose a novel QoS Measurement framework based on Residual Graph Neural Network (QM-RGNN), which inputs QoS data as a graph and outputs the prediction of unmeasured QoS data. It consists of three core components: 1) an encoder-decoder model QM-GNN with GCN as the encoder and MLP as the decoder is devised for efficient QoS prediction, and a residual module is introduced in QM-GNN to tackle highly sparse and volatile QoS data; 2) a dynamic adaptive sample ratio is proposed to reduce the sampling costs; 3) an online learning pattern is designed to reduce continuous training costs. Experiments on two real-world industrial edge cloud datasets demonstrate the superiority of QM-RGNN in QoS measurement. It obtains at least a 37.5% reduction of relative RMSE between ground-truth and predicted QoS data with up to 90% training cost reduction and 22.7% sampling cost reduction.

The collaboration between clouds and edges unlocks the full potential of edge-cloud systems. Edge-cloud platform has brought about significant decentralization, heterogeneity, complexity, and instability. These characteristics have posed unprecedented challenges to the optimal scheduling problem in the edge-cloud system, including inaccurate decision-making and slow convergence. In this paper, we propose a curiosity-driven collaborative request scheduling scheme in edge-cloud systems, namely Cur-CoEdge. To tackle the challenge of inaccurate decision-making, we introduce a time-scale and decision-level interaction mechanism. This mechanism employs a small-large-time-scale scheduling learning framework, facilitating mutual learning between different decision levels. To address the challenge of slow convergence, we investigate the underlying reasons, such as the sparse reward-setting in reinforcement learning. In response, we develop a curiosity-driven collaborative exploration approach that fosters intrinsic curiosity in the cloud and simultaneously motivates dispatchers to explore the environment both individually and collectively. The effectiveness of this collaborative exploration is also supported by theoretical proof of convergence. Finally, we implement a prototype system on a network hardware system along with two real-world traces. Evaluations demonstrate significant improvements, with up to a 26% increase in time efficiency, a 40% rise in system throughput, and a 71% enhancement in convergence speed.

Federated learning, as a distributed machine learning paradigm, offers promising solutions for vehicular edge computing (VEC) networks. However, federated learning in VEC with classification tasks still faces two key challenges: i) Delayed data labeling hampers supervised training; ii) Dynamic vehicle behavior complicates training scheduling and model uploads to edge servers. In this paper, we propose a semi-asynchronous federated learning algorithm for VEC. Specifically, it utilizes knowledge distillation to generate soft labels from raw data for supervised training, and estimates model training and uploading time through trajectory prediction. We further logically group vehicles based on the characteristics of their dynamic behavior. We then employ synchronous aggregation within groups and asynchronous aggregation between groups to optimize model performance while reducing latency. Finally, we conduct separate comparative experiments for all components, demonstrating that each component possesses unique advantages. Experiment results show that the proposed algorithm outperforms existing schemes in terms of accuracy and latency. The code is available at: https://github.com/dyxcode/Semi-Asynchronous-Federated-Learning.

Giant models have transformed vision-language tasks by mastering consistent representations across text and images, highlighting the critical role of deploying such models in the expanding domain of edge scenarios, such as monitoring and segmentation. However, the deployment is challenged by device heterogeneity, limited computational resources, and privacy concerns. Federated learning (FL) presents itself as a viable solution, facilitating decentralized training on devices and preserving data confidentiality. Despite its potential, FL faces obstacles with fine-tuning efficiency, including complex granularity data, static personalized prompt generation, and high energy consumption. This paper introduces a cross-granular and personalized prompt-based green federated tuning (CP2GFed) approach, aiming to address these issues by enabling giant model deployment on devices. CP2GFed introduces a cross-granularity knowledge transfer mechanism to leverage semantic relationships across varying data granularities. Meanwhile, it pioneers in generating dynamic personalized prompts based on inter-device affinities to improve model performance. In addition, CP2GFed meticulously optimizes energy consumption, including model local learning and interaction, by setting local computing steps and selecting communication devices. Empirical results indicate that CP2GFed elevates accuracy by up to 6.64% on diverse datasets and reduces energy consumption by nearly 60% per unit of accuracy, achieving a superior tradeoff between model performance and energy consumption compared to baselines.

Mobile edge computing (MEC) as a potential technology can offload tasks from user devices (UDs) to network edges to alleviate network congestion and reduce task execution delay. However, computation offloading faces two challenges: 1) Poor wireless channel quality causes high transmission delay; 2) Computing tasks may be obtained by eavesdroppers (Eves) during task offloading. Therefore, we consider deploying intel-ligent reflecting surface (IRS) in MEC networks to increase data transmission rate and ensure data transmission security. This paper investigates the issue of joint computation offloading and resource allocation in a multi-IRS assisted MEC network. Our goal is to minimize task execution delay. To address this problem, we propose a multi-agent deep deterministic policy gradient algorithm to determine the optimal offloading strategy for each UD. Simulation results show that the proposed algorithm can significantly reduce task execution delay and ensure data transmission security.

Mobile edge-cloud computing utilizes the computing resources of edge devices and cloud servers to execute complex deep neural networks (DNNs) for collaborative inference. However, many existing collaborative inference methods do not fully consider the limited resources of edge devices, resulting in high inference latency. In this paper, we design an integrated computational framework that combines model partition and compression to reduce inference latency. Specifically, we partition a DNN model at the middle layer and deploy the previous layer on the edge device and the subsequent layer on the cloud server respectively. We propose a collaborative dual-agent reinforcement learning algorithm called CPCDRL to determine partition point and compression ratios. It enables adaptive adjustments of compression ratios based on various partition points, with the overarching goal of minimizing the inference latency across the entire DNN model. The proposed algorithm can significantly reduce computational latency while minimizing accuracy loss compared to the baseline schemes.

Web 3.0 pursues the establishment of decentralized ecosystems through blockchain technologies, driving digital transformation in commerce and governance. With consensus algorithms and smart contracts grounded in cryptographic technologies, Web 3.0 enables secure and transparent digital services, such as digital identity, asset management, decentralized autonomous organizations (DAOs), and decentralized finance (DeFi), fostering integration between digital and physical economies. As quantum devices rapidly advance, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with enhanced quantum computing and communication capabilities. This article provides a comprehensive overview of blockchain-based Web 3.0, examining its quantum and postquantum advancements from two key perspectives. On the one hand, postquantum migration methods and quantum-resistant signatures offer robust solutions to safeguard blockchain against quantum threats. On the other hand, quantum and postquantum encryption and verification algorithms boost blockchain performance, creating a decentralized, secure, and value-driven system. Additionally, we outline potential applications of quantum blockchain and offer guidance for implementation within the Web 3.0 ecosystem. Finally, we discuss future directions for developing a provably secure and decentralized digital ecosystem.

The Internet of Things (IoT) finds applications across diverse fields but grapples with privacy and security concerns. Blockchain offers a remedy by instilling trust among IoT devices. The development of blockchain in IoT encounters hurdles due to its resource-intensive computation processing, notably in PoW-based systems. Cloud and edge computing can facilitate the application of blockchain in this environment, and the IoT users who want to mine in blockchain need to pay the computation resource rent to the Cloud Computing Service Provider (CCSP) for offloading the mining workload. In this scenario, these IoT miners can form groups to trade with CCSP to maximize their utility. In this paper, a mixed model of the Stackelberg game and coalition formation game is embraced to address the grouping and pricing issues between IoT miners and CCSP. In particular, the Stackelberg game is utilized to handle the pricing problem, and the coalition formation game is employed to tackle the best group partition problem. Moreover, a coalition formation algorithm is proposed to obtain a nearoptimal solution with very low complexity. Simulation results show that our proposed algorithm can obtain a performance that is very near to the exhaustive search method, outperforms other existing schemes, and requires only a small computation overhead.

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verify the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on a quantified latency impact factor, we have proposed several optimization strategies for edge cloud latency and validated their effectiveness. We also propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. Evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5 ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds.

The 5G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5G UDNs. In this article, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76m, with up to 80% of the unknown areas.

Model partitioning is a promising technique for improving the efficiency of distributed inference by executing partial deep neural network (DNN) models on edge servers (ESs) or Internet-of-Things (IoT) devices. However, due to heterogeneous resources of ESs and IoT devices in mobile edge computing (MEC) networks, it is non-trivial to guarantee the DNN inference speed to satisfy specific delay constraints. Meanwhile, many existing DNN models have a deep and complex architecture with numerous DNN blocks, which leads to a huge search space for fine-grained model partitioning. To address these challenges, we investigate distributed DNN inference with fine-grained model partitioning, with collaborations between ESs and IoT devices. We formulate the problem and propose a multi-task learning based asynchronous advantage actor-critic approach to find a competitive model partitioning policy that reduces DNN inference delay. Specifically, we combine the shared layers of actor-network and critic-network via soft parameter sharing, and expand the output layer into multiple branches to determine the model partitioning policy for each DNN block individually. Experiment results demonstrate that the proposed approach outperforms state-of-the-art approaches by reducing total inference delay, edge inference delay and local inference delay by an average of 4.76%, 10.04% and 8.03% in the considered MEC networks.

As the primary driver of intelligent mobile applications, deep neural networks (DNNs) have gradually deployed to millions of mobile devices, producing massive latency-sensitive and computation-intensive tasks daily. Mobile edge computing facilitates the deployment of computing resources at the edge, which enables fine-grained offloading of DNN inference tasks from mobile devices to edge nodes. However, most existing studies have not systematically considered three crucial performance aspects: scheduling multiple streams of DNN inference tasks, leveraging multi-exit models to hasten task processing, and partitioning inference models for partial offloading. To this end, this paper proposes an adaptive inference framework in mobile edge computing, which can dynamically select the exit point and partition point for multiple inference task streams. We design a dynamic programming algorithm to obtain an efficient solution under the ideal condition that task arrival information is known. Further, we design a learning-based algorithm for online scheduling, whose training efficiency is improved based on historical experience initialization and priority experience replay. Experimental results show that compared with the Greedy algorithm, the online algorithm improves the performance on two environmental parameters by an average of 5.9% and 32%, respectively.

Deep neural networks are the cornerstone of many mobile intelligent systems, and their inference processes bring about computation-intensive tasks. Device-edge cooperative inference in mobile edge computing provides a fine-grained processing method to migrate the burden of inference computation. However, the geographical dispersion of resources and the mobility pattern of devices pose scheduling issues to be considered. In this paper, we propose a task scheduling framework for such device-edge systems to improve the pipeline time of model inference. First, we consider the resource provisioning strategy with a pre-fetching service migration setting in the environment of multiple mobile devices and edge nodes. Then, we leverage game theory to analyze the property of the decision-making process and propose an offline algorithm under complete information. Next, we propose an algorithm based on proximal policy optimization to enable mobile devices to make decisions in a distributed online manner. Further, we adopt a memory mechanism into the online algorithm to improve the decision-makers' understanding of the system environment. Experiments demonstrate the effectiveness of the two algorithms. The average pipeline time of the proposed online algorithm is only 61.44% of that of local processing, which is 1.196 times that of the proposed offline algorithm.

The development of the mobile Metaverse has garnered increasing attention in the next-generation Internet, fueled by the rapid advancements of mobile Internet, communication, and computing technologies. With the resource limitations faced by mobile Metaverse users (MUs), the mobile Metaverse market is flourishing. This market enables MUs to access high-quality immersive experiences by trading resources with Metaverse service providers (MSPs) across geographically distributed resource pools. However, the mobile Metaverse market still faces several challenges, includingthe hierarchical mobile Metaverse service structure, temporal dependencies, and heterogeneous incentive mechanisms. To address these problems, this paper proposes a dual-level resources trading approach for mobile Metaverse based on blockchain. This approach employs a dual-level structure consisting of resource provisioning and heterogeneous auction mechanisms. Specifically, we formulate the resource provisioning as a temporal-dependent average delay minimization problem at the low level. To solve this low-level problem, we introduce a novel algorithm calledLyDif, which leverages Lyapunov optimization techniques and diffusion models. At the high level, we propose a price-guided double dutch auction (PG-DDA) mechanism to match heterogeneous resources and determine pricing strategies. The PG-DDA smart contract is deployed on a consortium blockchain platform, facilitating resource trading management and transaction monitoring. Based on a real trace of edge-cloud service requests, our experimental results demonstrate the effectiveness of our proposed scheme in achieving optimal latency and social welfare.

The popularity of microservices in industry has sparked much attention in the research community. Despite significant progress in microservice deployment for resource-intensive services and applications at the network edge, the intricate dependencies among microservices are often overlooked, and some studies underestimate the importance of system context extraction in deployment strategies. This paper addresses these issues by formulating the microservice deployment problem as a max-min problem, considering system cost and quality of service (QoS) jointly. We first study the attention-based microservice representation (AMR) method to achieve effective system context extraction. In this way, the contributions of different computing power providers (users, edge servers, or cloud servers) in the networks can be effectively paid attention to. Subsequently, we propose the attention-modified soft actor-critic (ASAC) algorithm to tackle the microservice deployment problem. ASAC leverages attention mechanisms to enhance decision-making and adapt to changing system dynamics. Our simulation results demonstrate ASAC's effectiveness, prioritizing average system cost and reward compared to the other state-of-the-art algorithms.

The development of deep learning and edge computing provides rapid detection capability for surface defects. However, components produced in actual industrial manufacturing environments often have tiny surface defects and training data for each specific defect type is limited. Meanwhile, network resources at the edge of industrial networks are difficult to guarantee. It is challenging to train a proper surface defect detection model for each specific surface defect type and provide a real-time surface defect detection service. To address the challenge, in this paper, we propose a real-time surface defect detection framework based on transfer learning with multi-access edge-cloud computing (MEC) networks. Furthermore, we improve the original YOLO-v5s framework by introducing the spatial and channel attention mechanism, and adding an additional detection head to enhance the detection ability on tiny surface defects. Evaluation results demonstrate that the proposed framework has superior performance in terms of improving detection accuracy and reducing detection delay in the considered MEC network.

Edge computing has evolved to enable mobile applications to run in an efficient and cost-effective manner at explosive-growing edge nodes. Under this paradigm, a new business resource trading market has emerged to provide edge-cloud services, offering a convenient way for mobile users to obtain resources from distributed computing power providers (CPPs). Blockchain, as a promising technology, provides a reliable platform for multi-party resource transactions (TXs), enabling secure and reliable computing services. Notably, the distributed CPPs not only offer mobile services but also act as blockchain nodes to maintain the stability of TXs. In this case, there exist certain bottlenecks in the blockchain-enabled edge-cloud resource market, such as limited scalability, inefficient resource allocation, and large system cost. In this paper, assisted by the permissioned blockchain, we study the fundamental problem of resource allocation by minimizing the system cost to handle mobile services and blockchain TXs in parallel. We first partition the Practical Byzantine Fault Tolerant (PBFT) consensus by hierarchical sharding to improve the scalability and ensure the security of the blockchain system. Next, based on the optimal sharding strategies, we formulate the parallel resource allocation as a multi-scale Lyapunov optimization problem, and develop a dual-alternation actor-critic with an attention mechanism (DA3C) algorithm to solve it. We evaluate the performance of theParamartusing trace-driven experiments. Simulation results demonstrate the superiority of our proposed framework as compared with the benchmark algorithms.

Deploying Latency-Critical (LC) services and Best-Effort (BE) services together is expected to improve resource utilization in edge clouds. However, co-locating LC and BE services on edge clouds presents unique challenges. Unlike cloud datacenters, edge clouds are heterogeneous, resource-constrained, and geographically distributed, leading to fiercer competition for resources and greater difficulty in balancing fluctuating co-located workloads. Due to the lack of consideration for the characteristics of edge environments, previous solutions designed for cloud datacenters are no longer applicable. To address these challenges, we introduceTango, a harmonious scheduling framework forKubernetes-based edge cloud systems with mixed services.Tangoincorporates novel components and mechanisms for elastic resource allocation on the edge, as well as two traffic scheduling algorithms that efficiently manage distributed edge resources.Tangofosters harmony not only by supporting compatible mixed services but also by offering collaborative solutions that complement each other. Based on a non-intrusive design forKubernetes,Tangofurther enhances it with automatic scaling and traffic scheduling capabilities. Compared to state-of-the-art approaches, experiments on large-scale hybrid edge clouds, driven by real workload traces, show thatTangoimproves system resource utilization by 36.9%, QoS-guarantee satisfaction rate by 11.3%, and throughput by 47.6%.

Mobile edge-cloud computing networks can provide distributed, hierarchical, and fine-grained resources, and have become a major goal for future high-performance computing networks. The key is how to jointly optimize service caching and computation offloading. However, the joint service caching and computation offloading problem faces three significant challenges of dynamic tasks, heterogeneous resources, and coupled decisions. In this paper, we investigate the issue of joint service caching and computation offloading in mobile edge-cloud computing networks. Specifically, we formulate the optimization problem as minimizing the long-term average service latency, which is NP-hard. To solve the problem, we conduct in-depth theoretical analyses and decompose it into two sub-problems: service caching processing and computation offloading processing. We are the first to propose a novel hierarchical deep reinforcement learning algorithm to solve the formulated problem, where multiple edge agents and a cloud agent collaboratively determine the caching-action and offloading-action, respectively. The results obtained through trace-driven simulations reveal that the proposed framework outperforms several prevailing algorithms concerning the average service latency across diverse scenarios. In a complex real scenario, our framework achieves an approximately 33% convergence improvement and a remarkable 39% reduction in the average service latency when compared to reinforcement learning-based algorithms.

In cross-device horizontal federated learning (FL), the communication cost of transmitting complete models between edge devices and a central server is a significant bottleneck, due to expensive, unreliable, and low-bandwidth wireless connections. As a solution, we propose a novelFLframework namedQSFL, towardsoptimizing FL uplink (client-to-server) communication at both client and model levels. At the client level, we design aQualification Judgment (QJ)algorithm to sample high-qualification clients to upload models. At the model level, we design aSparse Cyclic Sliding Segmentation (SCSS)algorithm to further compress the local model transmitted from the client to the server in the uplink communication. We prove that QSFL can converge over wall-to-wall time, and develop an optimal hyperparameter searching algorithm based on theoretical analysis to enable QSFL to make the best trade-off between model accuracy and communication cost. Experimental results show that QSFL achieves state-of-the-art compression ratios with marginal model accuracy degradation. Since mobile edge devices as FL clients often have heterogeneous system resources, such as communication bandwidth, we propose two noveldynamic segmentation strategies with varied counts or sizesbased on QSFL to enhance the robustness of QSFL to FL system heterogeneity. For some mobile edge devices joining as FL clients with both limited uplink and downlink communication bandwidths, they can not pull up the global model from the server. To tackle it, we propose a novelsymmetric downlink compressionscheme on top of QSFL to further reduce the downlink (server-to-client) communication costs, hence enabling a bidirectional communication-efficient FL. Theory analysis and experiments demonstrate that QSFL with dynamic segmentation or symmetric downlink compression still keeps convergence and takes a better trade-off between model accuracy and communication efficiency than without them.

Edge computing has emerged as a powerful paradigm for efficient scheduling at the network edge to fulfill users' requirements for low latency. Edge servers' and clouds' ability to interact allows them to leverage edge servers' limited computing and storage resources collectively to handle all requests collaboratively. This has unlocked the potential of the edge-cloud system. However, its sophistication (e.g., heterogeneous resources, intertwined dependencies, etc.) also raised unforeseen challenges in request dispatch and service orchestration phases within the scheduling process, includingcomplex hierarchy,limited cooperationandunfocused information. Traditional resource optimization methods for edge-cloud systems cannot address these intricate situations properly, raising concerns over system efficiency, stability, and costs. This paper proposes learning-based methods to accommodate time-varying requests and dynamic service prevalence. Specifically, we employ the multi-agent advantage actor-critic (MAA2C) and the graph convolutional networks-based MAA2C (GCN-MAA2C) in two phases, respectively, to enhance individual intelligence and facilitate optimal dispatch and orchestration decisions. Inspired by the regulations in human society, we proposeSocialEdge, a socialized learning-based scheduling framework for the edge-cloud system. To leverage the hierarchical correlation, we develop an inter-layer socialized refining mechanism. The inference results from one layer guide the training process of the next layer, enabling the abstraction of optimization-critical knowledge. Moreover, we apply intra-layer socialized cooperating, where federated averaging with MAA2C is implemented to ensure the cooperation of individuals over private data for achieving optimal global solutions. Furthermore, we propose a socialized resonance mechanism across the layers to extract high-value information and induce resonance toward the system objective, aiming to improve the efficiency of scheduling. Experimental evaluations on a proof-of-concept testbed and two real traces demonstrate thatSocialEdgereduces scheduling cost by 11.2% while enhancing time efficiency by 77.4%, and system throughput by 17.9%, compared to baseline methods.

Containers are gaining popularity in edge computing due to their standardization and low overhead. This trend has brought new technologies such as container engines and container orchestration platforms (COPs). However, fast and effective container deployment remains a challenge, especially at the edge. Prior work, which was designed for cloud datacenters, is no longer suitable for container deployment in edge clouds due to bandwidth limitations, fluctuating network performance, resource constraints, and geo-distributed organization. These edge features make rapid deployment on the edge difficult. Additionally, integrating with COPs is crucial for successful deployment.

The presence of Metaverse has elicited escalating attention in the next-generation Internet, followed by a large number of computationally intensive tasks, such as augmented reality, virtual reality, artificial intelligence-generated content (AIGC) applications, etc. With the popularity of mobile communication technology, mobile metaverse is becoming increasingly widespread. The resources required for these applications are rapidly growing in parallel with increasing demands from mobile Metaverse users (MUs), putting pressure on Metaverse service providers (MSPs) with limited resources, especially in mobile computing scenarios. Inspired by the burgeoning communication and computing technologies, the mobile Metaverse market between mobile MUs and MSPs is developing vigorously. However, there still remain numerous challenges in this market, including hierarchical mobile Metaverse structure, temporal dependencies, as well as heterogeneous incentive. In this paper, we propose a bi-alternating resource provisioning and heterogeneous auction approach for mobile Metaverse, named Bi-Meta. At the high level, resource provisioning is formulated as a Lyapunov problem minimizing average delay, solved by a novel bi-level based generative adversarial network, i.e., BiGAN. At the low level, a price- guided double dutch auction (PG-DDA) mechanism is presented for heterogeneous resource matching, with the designed PG- DDA smart contract. Based on the realistic edge-cloud company's traces, the experimental results verify that our proposed scheme achieves optimal latency and social welfare.

Federated Learning (FL) has become a common method for edge devices. Due to the limited energy capacity of edge devices, and the vulnerability of FL to malicious attacks from edge devices, vanilla FL still faces several challenges in edge computing, including energy consumption, model heterogeneity, and malicious behavior. To address these challenges, we propose a multi-granularity grouping-based federated learning (MG2FL), which groups and aggregates edge devices with low communication energy consumption and latency to reduce communication costs. Additionally, we introduce a multi-granularity guidance mechanism and a credit model to enhance model accuracy while ensuring security. Experimental results show that compared to the traditional FL algorithms, MG2FL achieves a 5.6% increase in accuracy, with the highest accuracy improvement reaching 11.1% in the presence of malicious edge devices.

Massive device connections in upcoming 6G networks have led to a sharp increase in network traffic volume, posing significant challenges in providing reliable performance guarantees, e.g., low latency. The Computing Power Network (CPN) is a new framework for resource integration involving multiple parties. It integrates the resources of various owners via the network, providing users with efficient and adaptable services. Due to the uncertainty of the signal quality, the majority of existing studies do not adequately organize the topology of user allocation in CPNs when optimizing network resources. Reconfigurable Intelligent Surface (RIS) is a new type of network node for constructing future smart radio environments with high spectral efficiency and nearly zero energy consumption that can offer new access options for user allocation in CPNs. In this paper, we investigate the user access allocation in a RIS-assisted Ad Hoc Edge (RAHE) scenario where the users are with service-intensive demands. To maximize the overall service tasks of the system constrained by a service time threshold, we propose a RIS-assisted interval scheduler strategy (RS3) approach to balancing the whole system service completion and total latency. Specifically, RS3is a graph-theoretic optimization method based on the interval scheduling problem. The numerical simulation results demonstrate that our proposed RS3approach is superior to commonly utilized methods in terms of the number of serves given the service time constraint.

Nowadays, real-time interactive content services have been the most dazzling sector of next-generation Internet. The high-quality perceptions of virtual scenes have given rise to the strict requirements of high bandwidth and low latency, where the edge-cloud system promises several benefits. However, there still remain prominent challenges, when taking the economical efficiency into consideration, including location-heterogeneity, context-directability, and capacity-exploitation. In this paper, we propose a location-context-capacity aware bandwidth cost economizing strategy in the edge-cloud system, i.e., LoCoCa. LoCoCa adopts server pools partition mechanism, then achieving the optimal burstable billing in each pool. Here, a location-aware graph construction and partition algorithm is designed to solve the server pools partition problem. Then an improved burstable billing optimization mechanism, with a context index and an adaptive bandwidth capacity, is also proposed to economize bandwidth costs. Finally, the realistic edge-cloud company's trace-based experimental results verify LoCoCa reduces bandwidth costs by 81.83 %, compared with the baselines.

With the popularization of visual machine learning, intelligent video analytics can automatically analyze and extract information from video streams, yet it brings heavy computing burdens. Edge computing can improve the processing experience by bringing computing resources near users. On top of this, various processing methods and settings have different resource requirements and output different user experiences. How to dynamically select the video processing configuration according to system states becomes a critical problem that remains to be addressed. In this paper, we propose an edge-assisted video analytic framework based on adaptive sampling and detection-based tracking. We design four functional modules to realize a cooperative computing processing flow. We consider two performance metrics, recognition accuracy and processing time to estimate the experience of real-time video analytics. Further, we design an online configuration method based on Double Deep Q-Network, which can adaptively select analytic configurations under the condition of system dynamics. Experimental results based on a real dataset demonstrate the superior performance of the proposed framework on reward, mean Intersection over Union (IoU), and processing time.

Computing Power Network (CPN) is an evolution of multi-access edge computing. Since the skyrocketing proliferation of CPN s, energy consumption aggravates explosively. However, majority of energy is wasted due to the incomplete analysis of tasks and resources, such as coarse-grained tasks consideration, coarse-grained resources integration, and unfocused complex information. In this paper, we propose a broad attention based fine-grained task offloading approach in green CPNs, i.e., Bat-FG. Specifically, for fine-grained tasks, we establish directed acyclic graphs (DAGs) subtasks offloading problem for green CPNs under the dependency and service constraints. For finegrained resources, decentralized resources are integrated into resource pools. Bridging the gap between fine-grained tasks and resource pools, we design a novel broad attention meta-reinforcement learning approach, i.e., Bat-MRL to focus on the main information for reducing the tasks' latency and energy consumption. Finally, extensive simulations show that Bat-FG significantly reduces 25.6 % task latency and 72.9 % energy consumption.

Deep neural networks are deemed to be the cornerstone of a series of mobile intelligent systems, and their inference processes bring about a mass of computation-intensive tasks. To migrate the burden of inference computation from resource-constrained mobile devices, device-edge cooperative inference in mobile edge computing provides a fine-grained processing method. However, the geographical dispersion of resources and the mobility pattern of devices pose technical issues in the scheduling of co-inference systems, which have not been fully considered. In this paper, we propose a learning-based scheduling framework for such device-edge systems to improve the pipeline time of model inference. First, we consider a resource provisioning strategy based on the number of devices and a pre-fetching service migration setting in the environment of multiple mobile devices and edge nodes. Next, we propose an algorithm based on proximal policy optimization for each device to make the decision independently. Further, we adopt long short-term memory in the algorithm to capture the temporal characteristics of the system state. Experiments using a real-world network and computing trace demonstrate that the proposed algorithm can efficiently sense the mobile system to make decisions at various system scales and two mobility scenes. The average pipeline time of the proposed algorithm is only 67.63% of that of local processing, which is 97.50% of that of the omniscient algorithm.

To break data silos and address the challenge of green communication, federated learning (FL) is widely used at network edges to train deep learning models in mobile edge computing (MEC) networks. However, many existing FL algorithms do not fully consider the dynamic environment, resulting in slower convergence of the model and larger training energy consumption. In this paper, we design a dynamic asynchronous federated learning (DAFL) model to improve the efficiency of FL in MEC networks. Specifically, we dynamically choose a certain number of mobile devices (MDs) by their arrival order to participate in the global aggregation at each epoch. Meanwhile, we analyze the energy consumption model of local update and upload update, and formulate the problem as a dynamic sequential decision problem to minimize the energy consumption, which is NP-hard. To address it, we propose an energy-efficient algorithm based on deep reinforcement learning named DDAFL, to intelligently determine the number of MDs participating in global aggregation according to the state of MEC networks at each epoch. Compared with baseline schemes, the proposed algorithm can significantly reduce energy consumption and accelerate model convergence.

The ability for cloud data centres and edge data centres to collaborate unleashes the potential of the edge-cloud system. However, its sophistication causes unexpected issues in request scheduling, such as Insufficient intelligence, complicated hierarchy and limited cooperation. Traditional resource optimization methods for the edge-cloud system struggle to accommodate such intricate situations. In this paper, inspired by the behaviors and regulations in human society, we propose a socialized learning-based scheduling approach for the edge-cloud system, namely SocialEdge. In order to adapt to time-varying requests and dynamic service prevalence, we propose a learning-based approach, multi-agent advantage actor-critic (MAA2C) and graph convolutional networks-based MAA2C for two phases, respectively, which can improve individual intelligence to achieve optimal dispatch and orchestration decisions. Then, to make use of hierarchy correlation, we develop the socialized refining inter the layers, where inference results from one layer guide the training process of the other layer so that optimization-critical knowledge can be abstracted. Besides, we apply socialized cooperation to each layer, where federated averaging with MAA2C is implemented to ensure cooperation over private data for achieving optimal global solutions. Experimental evaluations on a proof-of-concept testbed along with two real traces demonstrate that baselines have 105% more delay than SocialEdge while being 76.6% less time efficiency and 21.6% less throughput.

The current redundant sequence deduplication algorithms cannot remove structural repetitive DNA short reads such as mirror, reverse, paired, and complementary palindromes in high-throughput genomics sequencing data. Moreover, these methods also cannot construct indexes to recover the original sequences, thus failing to meet the requirements of lossless compression for downstream applications. To address these problems, we propose a data structure called Cycle-Hash-Linkage (CHL) and present a CPU parallelism optimization algorithm named SR2C (Structurally Redundant Short Reads Collapser) based on CHL to improve the compression ratio of DNA sequencing data. Experimental results on actual data from the NCBI database demonstrate that SR2C achieves an average residual sequence percentage improvement of 2.556% compared to the state-of-the-art redundant sequence deduplication algorithm, Minirmd. Furthermore, SR2C cascaded optimization improves the average compression ratios of compression algorithms Pigz, PBzip2, XZ, and 7Z by 92.345%, 78.999%, 10.132%, and 7.434%, respectively. By leveraging multi-core CPU parallel computation, SR2C effectively reduces time consumption, which achieves 2-5X deduplication and recovers acceleration.The same name Linux toolkit is freely available at https://github.com/fahaihi/SR2C.

Co-locating Latency-Critical (LC) and Best-Effort (BE) services in edge-clouds is expected to enhance resource utilization. However, this mixed deployment encounters unique challenges. Edge-clouds are heterogeneous, distributed, and resource-constrained, leading to intense competition for edge resources, making it challenging to balance fluctuating co-located workloads. Previous works in cloud datacenters are no longer applicable since they do not consider the unique nature of edges. Although very few works explicitly provide specific schemes for edge workload co-location, these solutions fail to address the major challenges simultaneously.

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of Quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: (1) First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, and what are the major abnormal behaviors. (2) Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. (3) Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users.

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verified the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on the observations, we propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. The further evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds.

Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital for efficient application deployment and resource provisioning. However, the heterogeneous application patterns, variable infrastructure performance, and frequent deployments in MT-ECP pose significant challenges for accurate and efficient workload prediction. Clustering-based methods for dynamic MT-ECP modeling often incur excessive costs due to the need to maintain numerous data clusters and models, which leads to excessive costs. Existing end-to-end time series prediction methods are challenging to provide consistent prediction performance in dynamic MT-ECP. In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP. Meticulously designed global pooling and information merging mechanisms can effectively identify and utilize global application patterns to drive local workload predictions. The integration of static content-aware mechanisms enhances model robustness in real-world scenarios. Through experiments on five real-world datasets, DynEformer achieved state-of-the-art in the dynamic scene of MT-ECP and provided a unified end-to-end prediction scheme for MT-ECP.

With the development of networking technology, the computing system has evolved towards the multi-tier paradigm gradually. However, challenges, such as multi-resource heterogeneity of devices, resource competition of services, and networked system dynamics, make it difficult to guarantee service-level agreement (SLA) for the applications. In this paper, we propose a multi-tier edge-cloud computing framework, EdgeMatrix, to maximize the throughput of the system while guaranteeing different SLA priorities. First, in order to reduce the impact of physical resource heterogeneity, EdgeMatrix introduces the Networked Multi-agent Actor-Critic (NMAC) algorithm to re-define physical resources with the same quality of service as logically isolated resource units and combinations, i.e., cells and channels. In addition, a multi-task mechanism is designed in EdgeMatrix to solve the problem of Joint Service Orchestration and Request Dispatch (JSORD) for matching the requests and services, which can significantly reduce the optimization runtime. For integrating above two algorithms, EdgeMatrix is designed with two time-scales, i.e., coordinating services and resources at the larger time-scale, and dispatching requests at the smaller time-scale. Realistic trace-based experiments proves that the overall throughput of EdgeMatrix is 36.7% better than that of the closest baseline, while the SLA priorities are guaranteed still.

To support rapidly increasing services and applications from users, multi-tier computing is emerged as a promising system-level computing architecture by distributing computing/caching/communication/networking capabilities between cloud servers to users, especially deploying edge servers at network edges (e.g., base stations). However, due to heterogeneous content requests of users and a high-cost hit manner with direct hits, edge caching is still a most serious issue to be addressed. In this paper, we investigate the issue of recommendation-enabled edge caching in mobile two-tier (edge-cloud) computing networks. Particularly, we integrate recommender systems and edge caching to support both direct hits and soft hits and thus improve the resource utilization of edge servers. We model the factors affecting the user quality of experience as a comprehensive system cost and further formulate the problem as a multi-agent Markov decision process with the goal of minimizing the long-term average system cost. To address the formulated problem, we propose a decentralized recommendation-enabled edge caching framework that leverages a discrete multi-agent variant of soft actor-critic and federated learning. The proposed framework enables each edge server to learn its best policy locally and generate judicious decisions independently. Finally, trace-driven simulation results demonstrate that the proposed framework converges to a better caching policy and outperforms several existing algorithms on average system cost reduction.

With the explosive growth of mobile multimedia services and artificial intelligence applications involving automatic speech analysis (ASA), mobile devices are increasingly unable to handle these computation-intensive tasks generated by users due to the limited computing resource. Besides, the existing cloud computing paradigm is not capable of processing such real-time and delay-sensitive ASA tasks. In this paper, by leveraging mobile edge computing and deep learning (DL), we investigate task offloading for DL-empowered ASA in mobile edge-cloud computing networks to minimize the total time for processing ASA tasks, thereby providing an agile service response. Specifically, to accelerate the processing of ASA tasks, we decompose a convolutional neural network based encoder-decoder model and deploy the encoder at edge servers to extract the features of ASA tasks. Moreover, edge servers derive the user tolerance limit by using a linear regression model for further enhancing the quality of experience of users. Based on some certain network constraints (i.e., user association and edge servers’ storage/computing capacity), we propose a low-complexity and distributed offloading framework to solve the formulated complex problem. Evaluation results demonstrate the effectiveness of the proposed framework on reducing the total time and improving the satisfaction rate of users.

Driven by the burgeoning growth of the Internet of Everything and the substantial breakthroughs in deep learning (DL) algorithms, a booming of artificial intelligence (AI) applications keep emerging. Meanwhile, the advance in existing computing paradigms, i.e., cloud computing and edge computing, provide assorted computing solutions to satisfy the increasingly high requirements for ubiquitous AI services. Nevertheless, there are some non-trivial issues in the computing frameworks, including the underutilization of computing power, the self-interest of computing-power trading mechanism, and the inefficiency of AI services management. To tackle the above issues, we propose a computing-power trading framework based on blockchain, also named AI-Bazaar. In AI-Bazaar, the AI consumers play multiple roles and feel free to contribute the computing power rented from the computing-power provider (CPP) for blockchain mining and AI services. Accordingly, we formulate the computing trading problem as a Stackelberg game. Based on the win or learn fast principle (WoLF), we design a profit-balanced multi-agent reinforcement learning (PB-MARL) algorithm to search the AI-Bazaar equilibrium, while finding the balanced profits for AI consumers and CPP. Numerical simulations are carried out to demonstrate the satisfactory performance and effectiveness of the proposed framework.

In a public blockchain system applying Proof of Work (PoW), the participants need to compete with their computing resources for reward, which is challenging for resource-limited devices. Mobile blockchain is proposed to facilitate the application of blockchain for mobile service, in which the lightweight devices can participate mining by renting resources from the Cloud Computing Service Provider (CCSP), but CCSP usually does not have the information about the demand preference of users. In this article, a contract model is adopted to address the cloud computing resource allocation and pricing problem in the mobile blockchain. In particular, an adverse selection contract solution is proposed to overcome the information asymmetry problem, and resource pooling is introduced to improve the stability of users’ rewards. Simulation results show that the information asymmetry problem is well overcome by adverse selection contract so that CCSP can obtain more utility than linear pricing contracts. Furthermore, the resource pooling could effectively improve the users’ and CCSP's utilities. When the size of the mining pool is large enough, it can achieve an improvement effect of more than 10 times. The effect of pool size and user type distribution on CCSP's utility is also studied.

In the 5 G era, data traffic has been growing rapidly. A small number of popular data files may dominate the network traffic and lead to heavy network congestion. Device-to-Device (D2D) communication can be used for caching and offloading significant data traffic. D2D social networks are instantiated paradigms of D2D communication. Existing studies maximize the performances of caching and offloading in D2D social networks by predicting potential content propagation paths. However, predicting such paths still faces many challenges, such as limitation of user spatial-temporal features, fragility of D2D social networks, and uncertainty of participants. As a solution, we first measure users' multi-dimensional features and content propagation paths to explore the distributions of D2D activities. Then we propose a D2D-LSTM model to predict complete content propagation paths hierarchically and design a prototype-user model for new participants. Experimental results demonstrate the state-of-the-art performances of D2D-LSTM. D2D-LSTM achieves at most 95% and at least 84.6% average precision in predicting terminal prototype-user class. Tree generation tests show that the generated trees have at most 64% and at least 17% similarity with ground-truth trees.

Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edge-cloud networks. Besides, the hierarchical distribution of heterogeneous resources makes the modeling and scheduling of k8s-oriented edge-cloud network particularly challenging. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud network to improve the long-term throughput rate of request processing. First, we design a coordinated multiagent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 15.9% while reducing scheduling cost by 38.4% compared to baselines.

Bandwidth-intensive applications transmit large-scale video data in the network. It causes backhaul bottlenecks and affects user experience. Deploying edge cache on an access point (AP) is a popular method to bring content files closer to end-users, but it faces significant challenges, especially in efficiently predicting and satisfying different users’ future content requests with limited cache capacity. In this article, we propose an intelligent gateway assisted edge cache deployment strategy (GACD), which jointly considers traffic usage patterns in multiple APs and the impact of new content on the cache performance. In GACD, The cache content placement problem is formulated as a many-to-one bidirectional matching problem with a dynamic quota allocation, aiming to improve cache resource utilization and minimize the average delivery latency. To address this problem, we design a heterogeneous information networks based prediction algorithm to predict end-users’ potential preference of new content files. Then, we adapt the seasonal autoregressive integrated moving average model for traffic usage prediction, and propose a many-to-one matching algorithm to achieve dynamic matching quota adjustment and efficient cache content placement. We conduct extensive real-world trace-based experiments to validate the performance of GACD. Compared with six alternative cache strategies, GACD improves the hit rate by 23.9% on average, reduces the average content delivery delay by 19.02%, and increases the accuracy by 31.02% on average.

As the footing stone of artificial intelligence (AI), ubiquitous computing resource is beginning to receive interest. With this trend, a new form of edge-cloud service market dedicated to collecting, trading, and scheduling computing resources is rising. The computing participants in the service market, as providers and demanders of computing resources, are becoming more diversified and open. As such, the intriguing economic phenomenon and the circulation mechanism have emerged. These bring inherent challenges, such as a volatile market, the ossification of pricing, isolation, and inefficiency. In this article, we propose a novel space-time-request trading framework for the edge-cloud service market, namelyCompCube. To ensure stability,CompCubeadopts the dual-circulation futures-spot trading method, including space-time dynamic pricing in the macro-cycle, request intention conversion, and resource matching in the micro-cycle. Among this, an incomplete information game model is designed to determine the long-term trading pricing in the macro-cycle. Then, to tackle the indicator isolation problem due to the inconsistency between the user's requests and the computing-power provider's (CPP’s) resources, we focus on minimizing the rental cost of computing resources while satisfying diverse service level agreements (SLA) of users. To address this problem, a spatiotemporal scale Lyapunov optimization and an alternating actor-critic algorithm, A2SC, are developed. Besides, in the micro-cycle, a discriminatory double auction helps to determine the computing resource matching results efficiently and impersonally. We evaluate theCompCubeof the A2SC algorithm with realistic datasets. Compared to other baselines, i.e., DYRECEIVE, Price Preferred, and Random, A2SC reduces the average rental cost by 30.45%, 5.74%, and 17.57%, respectively. Furthermore,CompCubecan improve SLA satisfaction, as well as promote resource efficiency and social welfare compared with the traditional methods.

Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyzes an attention-based microservice representation approach for extracting system context. The attention-modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm's priorities in terms of average system cost and system reward.

Nowadays, numerous primary technologies, like ultra-dense networks (UDNs) and Base Stations (BSs) sleeping state, are developed in fifth-generation (5G) networks. Due to the UDNs, the number of BSs in 5G networks is proliferating, along with the energy consumption. Therefore, it is necessary to cut down the energy attrition in 5G networks under the assurance of delay. Till now, some researchers have proved that the association of users and the sleeping states of BSs have a significant effect on energy consumption and latency in 5G networks. However, the traditional solutions associate users and select states nonadaptively without the dual consideration of energy-saving and delay. In view of this, we propose a dual-adaptive delay-aware and energy-saving system (DADEs) in 5G networks. To further optimize the energy and delay of 5G BSs, the model is split into two tandem problems: user association and BS state selection. Meanwhile, a tandem deep reinforcement learning (T-DRL) algorithm is presented to make decisions in these problems for optimizing and balancing performance between delay and energy adaptively. Additionally, the real datasets of 5G users and BSs are used and trained in this paper. Finally, simulation results show that the DADEs saves more than 50% of energy with an adaptive and satisfying latency.

As a promising framework for distributed learning in mobile edge computing scenarios, federated learning (FL) allows multiple mobile devices to train a model collaboratively without transferring raw data and exposing user privacy. However, vanilla FL schemes are still facing to problems in edge computing, where the diversity of tasks and devices causes the non-IID and multi-granularity data with model heterogeneity. It becomes a pressing challenge to jointly training edge devices accompanied by these problems, while vanilla FL only discusses them separately. To this end, we consider tailoring FL to adapt to mobile edge environments, which focus on solving the problems of collaborative training of edge devices with multi-granularity heterogeneous models under different data distributions. In particular, we proposed a distance-based FL for the same type of edge devices that provides personalized models to avoid the negative impact of non-IID data on model aggregation. Further, we design a bi-directional guidance method with a prior attention mechanism, which can transfer knowledge among edge devices with multi-granulairty and multi-scale models. The experimental results show that our proposed mechanisms significantly improve training performance compared to other baselines on IID and non-IID data. Furthermore, the bi-directional guidance significantly improves convergence efficiency and accuracy performance for finer and coarser granularity edge devices, respectively.

The edge-cloud system has the potential to com-bine the advantages of heterogeneous devices and truly realize ubiquitous computing. However, for service providers to guar-antee the Service-Level-Agreement (SLA) priorities, the complex networked environment brings inherent challenges such as multi-resource heterogeneity, resource competition, and networked sys-tem dynamics. In this paper, we design a framework for the edge-cloud system, namely EdgeMatrix, to maximize the throughput while guaranteeing various SLA priorities. First, EdgeMatrix introduces Networked Multi-agent Actor-Critic (NMAC) algorithm to redefines physical resources as logically isolated resource combinations, i.e., resource cells. Then, we use a clustering algorithm to group the cells with similar characteristics into various sets, i.e., resource channels, for different channels can offer different SLA guarantees. Besides, we design a multi-task mechanism to solve the problem of joint service orchestration and request dispatch (JSORD) among edge-cloud clusters, significantly reducing the runtime than traditional methods. To ensure stability, EdgeMatrix adopts a two-time-scale framework, i.e., coordinating resources and services at the large time scale and dispatching requests at the small time scale. The real trace-based experimental results verify that EdgeMatrix can improve system throughput in complex networked environments, reduce SLA violations, and significantly reduce the runtime than traditional methods.

With the advancement of the user application service demands, the IoT system tends to offload the tasks to the edge server for execution. Most of the current studies on edge computation offloading ignore the dependencies between components of the application. The few pieces of research on edge computing offloading which focus on the topology of application are primarily applied in single-user scenarios. Unlike previous work, our work mainly solves dependent task offloading with edge computing in multiuser scenarios, which is more in line with reality. In this article, the dependent task offloading problem is modeled as a Markov decision process (MDP) first. Then, we propose an actor–critic mechanism with two embedding layers for directed acyclic graphs (DAGs)-based multiple dependent tasks computation offloading, namely, ACED, by jointly considering the topology of the application and the channel interference between several users. Finally, the results of simulations also show the priorities of the proposed ACED algorithm.

Edge-computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, and plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands, and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space–air–ground-integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, and represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled SAGINs to support various Internet of Vehicles (EC-IoV) services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a preclassification scheme is presented to reduce the size of action space, and a deep imitation learning-driven offloading and caching algorithm is proposed to achieve real-time decision making. The simulation results show the effectiveness of our proposed scheme. Finally, we also discuss some technology challenges and future directions.

Advances in communications and networking technologies are driving the computing paradigm toward the end-edge-cloud collaborative architecture to leverage ubiquitous data and resources. Opposite to centralized intelligence, Hierarchical Federated Learning (HFL) relieves overwhelmed communication overhead and enjoys the advantages of high bandwidth as well as abundant computing resources while retaining privacy-preserving benefits of Federated Learning (FL). It is difficult to balance system overhead and model performance in the HFL framework, while it could be solved by introducing an incentive mechanism. Although the incentive mechanism can alleviate the above anxiety by compensating relevant participants, some limitations (multi-dimensional properties, incomplete information and unreliable participants) will significantly degrade the performance and efficiency of the designed mechanism. To address the challenges caused by the above limitations, we propose InFEDge, a blockchain-based incentive mechanism in the HFL. The InFEDge considers 1) multi-dimensional individual properties to model system participants and proves the uniqueness of Nash equilibrium with the closed-form solution. Meanwhile, 2) we transform the problem under incomplete information into a contract game where we obtain the optimal solution. Moreover, 3) we also leverage the blockchain to provide economic incentives, prevent unreliable participants’ disturbance and further ensure data privacy by implementing the mechanism in the smart contract to offer a credible, faster, and transparent resource trading system. Experimental evaluations on a proof-of-concept testbed along with real traces demonstrate the superiority of our mechanism. Further, our method solves a real-world user allocation problem for future communications and networking.

Cloud gaming is a novel service provisioning paradigm, which hosts video games in the cloud and transmits interactive game streams to game players via the Internet. In such cloud gaming scenarios, the cloud is required to consume tremendous resources for video rendering and streaming, especially when the number of concurrent players reaches a certain level. On the other hand, different game players may have distinct requirements on Quality-of-Experience, such as high video quality, low delay, etc. Under this circumstance, how to satisfy players of different interests by efficiently leveraging cloud resources becomes a major challenge to existing cloud gaming services. In order to meet the overall requirements of players in a cost-effective manner, this article applies game theory to cloud gaming scenarios. It proposes a distributed algorithm to optimize virtual machine (VM) placement in mobile cloud gaming through resource competition. Further, by constructing a potential function, we prove that the resource competition game is a potential game, and the proposed algorithm scales well as the player population increases. We prove theoretically and verify experimentally that, with the proposed distributed VM placement algorithm, players can achieve a mutually satisfying state within a finite number of iterations.

Energy Internet (EI) is developing and booming rapidly with the increase of distributed energy resources, which is beneficial to address the severe condition of industrial energy. However, there are inevitable credit crises and utility optimization challenges in EI that need to be settled. In this article, we propose a blockchain-assisted software defined energy Internet (BSDEI), where a distributed energy market smart contract is designed to ensure transactions executed reliably and participants’ accounts dealt accurately. In order to jointly optimize the utilities of operators, retailers, and industrial prosumers in BSDEI, we formulate the whole trading process as a three-stage Stackelberg game, with the proof of existence and uniqueness for the Stackelberg equilibrium. Then, we design a hierarchical distributed policy gradient algorithm to solve the Stackelberg game under incomplete information. We implement a blockchain-based industrial energy trading system using a middleware platform. The smart contract is deployed on the consortium blockchain, providing website interfaces for participants to operate. Furthermore, we conduct experiments for analyzing economic benefits. Our system prototype demonstrates the feasibility of BSDEI and the algorithm exceeds about 18% in total mean reward than comparing algorithms.

The rapid increase of data traffic has brought great challenges to the maintenance and optimization of 5G and beyond, and some smart critical infrastructures, e.g., small base stations (SBSs) in cellular cells, are facing serious security and failure threats, causing resiliency degradation concerns. Among special smart critical infrastructure failures, the sleeping cell failure is hard to address since no alarm is generally triggered. Sleeping cells can remain undetected for a long time and can severely affect the quality of service/quality of experience to users. To enhance the resiliency of the SBSs in sleeping cells, we design a mobile edge-cloud computing system and propose a semi-supervised learning-based framework to dynamically detect the sleeping cells. Particularly, we consider two indicators, recovery proportion and recovery speed, to measure the resiliency of the SBSs. Moreover, in the proposed scheme, experts’ optimization experience and each period’s detection results can be utilized to iteratively improve the performance. Then we adopt a dataset from real-world networks for performance evaluation. Trace-driven evaluation results demonstrate that the proposed scheme outperforms existing sleeping cell detection schemes, and can also reduce the communication and runtime costs and enhance the resiliency of the SBSs.

The arrival of 5G networks has extensively promoted the growth of content delivery services (CDSs). Understanding and predicting the spatio-temporal distribution of CDSs are beneficial to mobile users, Internet Content Providers and carriers. Conventional methods for predicting the spatio-temporal distribution of CDSs are mostly base-stations (BSs) centric, leading to weak generalization and spatio coarse-grained. To improve the spatio accuracy and generalization of modeling, we propose user-centric methods for CDSs spatio-temporal analysis. With geocoding and spatio-temporal graphs modeling algorithms, CDSs records collected from mobile devices are modeled as dynamic graphs with spatio-temporal attributes. Moreover, we propose a spatio-temporal-social multi-feature extraction framework for spatio fine-grained CDSs hot spots prediction. Specifically, an edge-enhanced graph convolutional block is designed to encode CDSs information based on the social relations and the spatio dependence features. Besides, we introduce the Long Short Term Memory (LSTM) to further capture the temporal dependence. Experiments on two real-world CDSs datasets verified the effectiveness of the proposed framework, and ablation studies are taken to evaluate the importance of each feature.

In microgrid, peer-to-peer (P2P) electricity trading has quickly ascended to the spotlight and gained enormous popularity. However, there are inevitable credit problems and system security problems. Besides, the current model in the electricity trading system cannot balance the utilities of multiple trading entities. In this paper, we propose a blockchain-based distributed P2P electricity trading system. We define elecoins as currency in circulation within our trading system. In order to jointly optimize the utilities of both parties in the elecoins trading, we formulate the elecoins purchasing problem as a hierarchical Stackelberg game. Then, we design a distributed multi-agent utility-balanced reinforcement learning (DMA-UBRL) algorithm to search the Nash equilibrium. Finally, we factually build a blockchain system with a blockchain explorer and deploy an electricity trading smart contract (ETSC) on Ethereum, with a website interface for operating. The numerical results and the implemented realistic system show the advantages of our work.

As a compelling collaborative machine learning framework in the big data era, federated learning allows multiple participants to jointly train a model without revealing their private data. To further leverage the ubiquitous resources in end-edge-cloud systems, hierarchical federated learning (HFL) focuses on the layered feature to relieve the excessive communication overhead and the risk of data leakage. For end devices are often considered as self-interested and reluctant to join in model training, encouraging them to participate becomes an emerging and challenging issue, which deeply impacts training performance and has not been well considered yet. This paper proposes an incentive mechanism for HFL in end-edge-cloud systems, which motivates end devices to contribute data for model training. The hierarchical training process in end-edge-cloud systems is modeled as a multi-layer Stackelberg game where sub-games are interconnected through the utility functions. We derive the Nash equilibrium strategies and closed-form solutions to guide players. Due to fully grasping the inner interest relationship among players, the proposed mechanism could exchange the low costs for the high model performance. Simulations demonstrate the effectiveness of the proposed mechanism and reveal stakeholder's dependencies on the allocation of data resources.

With the rapid development of the Internet of Things (IoT), more and more smart devices are connected to the Internet, implementing Deep Neural Network (DNN) models on edges for collaborative inference via device-edge synergy has become a feasible method for improving application performance in many scenarios. However, when faced with the task stream scenario with latency guarantee such as video surveillance and industrial production line, we need adaptive edge intelligence to make adjustments in real-time according to the changes of the task stream. There are many adaptive edge intelligence technologies in the existing works, such as early-exit mechanism and model selection, but they don’t take the requirements of the task stream scenario into consideration. In this paper, we propose a device-edge collaborative inference system based on the early-exit mechanism to solve the problem of adaptive edge intelligence in the task stream scenario. Then, we design an offline dynamic programming (DP) algorithm and an online deep reinforcement learning (DRL) algorithm to dynamically select the exit point and partition point of the branchy model in the task stream, which aims to balance the number of tasks accomplished and task inference accuracy in the system. Experimental results show that the DRL algorithm can achieve performance close to that of the DP algorithm in the task stream scenario.

Mobile edge computing (MEC) provides a new ecosystem that enables cloud computing capabilities at the edge of mobile networks, which is characterized by ultra-low latency and high bandwidth as well as real-time access to radio network information leveraged by applications. Nevertheless, various challenges, especially the decision-making issues for task offloading, are yet to be properly addressed. In this paper, leveraging the insight from the relative evaluation method, we propose a metric to quantify the benefit on users’ service experience enhancement by task offloading. Meanwhile, by comprehensively considering the energy cost, time cost and users’ service experience enhancement throughout the task offloading process, we formulate the task offloading decision-making problem as a two-dimensional knapsack loading problem to maximize the cost efficiency of task offloading. To solve the optimization problem more efficiently, we propose a suboptimal heuristic algorithm with polynomial-time complexity. Compared with four baseline algorithms, simulation results demonstrate the cost efficiency improvement of our proposed scheme.

With the development of network technology and the surge in demand, the speed and throughput of data and applications are leading to the skyrocketing increase in traffic. The communication and collaboration between heterogeneous edge servers are indispensable. In this scenario with heterogeneous edges, there is a common understanding on the fact that an effective edge caching algorithm could play the role of enabler to reduce the network resource consumption and content fetch delay. However, most of the existing studies on multi-agent caching methods focus more on the overall situation, while ignoring the mutual influence between different agents. In this context, we model the edge caching content replacement problem as a Markov process and deploy attention mechanism based on the Actor-Attention-Critic algorithm to realize a neighboring-aware edge caching (NAEC) strategy. The proposed method makes full use of the communication between base stations to exchange neighboring information, so that we can reduce the pressure on the backbone and further improve user satisfaction. The simulation results have verified the feasibility and effectiveness of the proposed algorithm.

With the rapid increase of data in mobile edge computing (MEC) networks, mobile devices (MDs) have been generating many computation-latency-sensitive tasks. As the MDs are limited by resources in terms of storage, computation, and bandwidth, part of tasks have to be offloaded to the edge of mobile networks or the remote cloud for more efficient processing. Hence, task offloading plays a vital role in this scene. Existing works about task offloading mainly aim at one-shot task offloading and rarely consider the dependencies of tasks. In this paper, we focus on minimizing the maximum delay of processing a series of tasks with dependencies in MEC networks, which supports device-to-device communications. Specifically, we consider task offloading under a hybrid scenario with a small base station (SBS) deployed with an edge server (ES) and several MDs which generate several tasks with dependencies. Then we model the tasks to a weighted directed acyclic graph (DAG) and formulate the optimization problem as minimizing the critical path of the weighted DAG. To tackle this NP-hard problem, we propose a heuristic scheme to iteratively optimize the delay of paths of the weighted DAG under the constraints of the ES. To evaluate the proposed scheme, we perform numerical experiments with different numbers of tasks. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of reducing the system delay and saving the energy consumption of the MDs.

Kubernetes (k8s) has the potential to merge the distributed edge and the cloud but lacks a scheduling framework specifically for edge-cloud systems. Besides, the hierarchical distribution of heterogeneous resources and the complex dependencies among requests and resources make the modeling and scheduling of k8s-oriented edge-cloud systems particularly sophisticated. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud systems to improve the long-term throughput rate of request processing. First, we design a coordinated multi-agent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 14.3% while reducing scheduling cost by 34.7% compared to baselines.

With the emergence of 5G, wireless sensor networks, and related technologies, Internet of Things (IoT) has gained prominence as an emerging paradigm to meet the demands of flexible, agile, and ubiquitous accessibility of cyberspace from physical systems. However, the current centralized IoT architecture is heavily restricted by the problems of single points of failure, data privacy, security, and robustness. Recently, blockchains have been found attractive as potential solutions to some of these problems, due to their ability to maintain immutable open ledgers that are accessible to everyone but are tamper-proof. In addition, rapid development of edge computing has enabled a large range of new IoT applications. Edge computing pushes cloud services from the network core to the network edges in closer proximity to IoT devices. Thus, blockchain and edge computing are attractive technologies to meet new and existing challenges by enabling new IoT applications and services through secure, reliable, flexible, and powerful devices and systems while motivating new business models in the growing digital economies. They can provide attractive solutions, such as schemes for decentralized services, service virtualization, rapid resource optimization, and flexible and reliable management and maintenance.

Recently, the term “Internet of Things” (IoT) has elicited escalating attention. The flexibility, agility, and ubiquitous accessibility have encouraged the integration between machine learning (ML) with IoT. However, there are many challenges that present the key inhibitors in moving ML to the public solution, such as centralized training, poor training efficiency, and heavy computing capabilities requirements. Therefore, bringing learning intelligence to edge IoT nodes has been spotlighted for some researches. Meanwhile, how to govern the use of learning results efficiently, reliably, scalably, and safely is hampered by the heterogeneity and nonconfidence among IoT nodes. In this article, we propose a blockchain-based collective Q-learning (CQL) approach to address the above issues, where lightweight IoT nodes are used to train parts of learning layers, then employing blockchain to share learning results in a verifiable and permanent manner. We further improve the traditional Proof of Work (PoW). Instead of solving a meaningless puzzle, we regard the learning process in the IoT node as a piece of work. Accordingly, the winner is the IoT node with the minimum reduced percentage of the learning loss function, referred to as the Proof-of-Learning (PoL) consensus protocol. Specifically, in order to show how the CQL approach works, we use it to address a networking integrated cloud-edge-end resource allocation in IoT. The experimental results reveal the superior performance of the proposed scheme.

Recently, integrated with the advanced communication technologies (e.g., 5G) and artificial intelligence (AI), mobile-edge intelligence (MEI) is regarded as the promising method to deal with the emerging challenges. Specifically, Device-to-Device (D2D) communications have been put forward to reduce the traffic pressure while extending cellular network capacity. However, the stability of the social network is important for the design of efficient and reliable traffic offloading strategy, which is often absent from the related work. Besides, most existing studies merely model the relation between a node pair as a binary or continuous value, neglecting the rich information between users. Moreover, many traditional models are conducted based on small-scale data sets or online Internet services, severely confining their applications in the D2D scenario. Thus, it is necessary to understand the network structure and select the key users to address the aforementioned challenges. In this article, we first propose a network representation model, named MPPT, to regard the multidimensional relations as a probability in a third-order (3-D) tensor space. Then, a mobile D2D social community is derived by integrating an edge base station (BS) and the nearby D2D users, and develop an anchored user selection algorithm to maintain the stability of multiple D2D social communities by choosing and retaining critical users adaptively under the limited network resources. Finally, we devise a probability-based onion layers anchored$(k,r)$-core (P-OLAK) algorithm to identify the anchor users. The large-scale data sets-based experimental results show the superiorities of the proposed methods.

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic.

In recent years, the rapid growth of traffic has become a serious problem of mobile network operators. For effectively mitigating this traffic explosion problem, there have been many efforts to research on offloading the traffic from cellular links to direct communications among users. In this paper, we are motivated by users' sharing activities, and hence propose the framework of Traffic Offloading assisted by Social network services (SNS) via opportunistic Sharing in mobile social networks (MSNs), TOSS, to offload SNS-based cellular traffic by user-to-user sharing. First, a subset of users who are to receive the same content was selected as initial population depending on their content spreading impacts in the online SNSs and their mobility patterns in the offline MSNs. Then users move, encounter and share the content via opportunistic local connectivity with each other, the content via opportunistic local connectivity with each other, e.g., Bluetooth, Wi-Fi Direct, Device-to-Device in LTE. Individual users have distinct access patterns, which potentially allow TOSS to exploit the user-dependent access delay between the content generation time and each user's access time for content sharing purposes. The traffic offloading and content spreading among users are analyzed by taking into account various options in linking SNS and MSN traces. Four mobility traces and online SNS trace for evaluation are analyzed. An extended evaluation over a large-scale data set are further carried out, and the effectiveness of TOSS is further proved.

With the proliferation of mobile device users, the Device-to-Device (D2D) communication has ascended to the spotlight in social network for users to share and exchange enormous data. Different from classic online social network (OSN) like Twitter and Facebook, each single data file to be shared in the D2D social network is often very large in data size, e.g., video, image or document. Sometimes, a small number of interesting data files may dominate the network traffic, and lead to heavy network congestion. To reduce the traffic congestion and design effective caching strategy, it is highly desirable to investigate how the data files are propagated in offline D2D social network and derive the diffusion model that fits to the new form of social network. However, existing works mainly concern about link prediction, which cannot predict the overall diffusion path when network topology is unknown. In this article, we propose D2D-LSTM based on Long Short-Term Memory (LSTM), which aims to predict complete content propagation paths in D2D social network. Taking the current user's time, geography and category preference into account, historical features of the previous path can be captured as well. It utilizes prototype users for prediction so as to achieve a better generalization ability. To the best of our knowledge, it is the first attempt to use real world large-scale dataset of mobile social network (MSN) to predict propagation path trees in a top-down order. Experimental results corroborate that the proposed algorithm can achieve superior prediction performance than state-of-the-art approaches. Furthermore, D2D-LSTM can achieve 95% average precision for terminal class and 17% accuracy for tree path hit.

With the tremendous growth of mobile data traffic generated by various devices such as smartphones, smartpads and wearable devices, it is necessary for mobile network operators to introduce revolutionary networking techniques, thereby satisfying service requirements of mobile users. Recently, mobile edge computing (MEC) has been regarded as an effective technique to alleviate the traffic burden on backhaul networks. In this paper, we investigate the issue of mobility-aware content caching and user association for ultra-dense MEC networks by minimizing the system costs. The problem is formulated as a complex pure integer nonlinear programming, which is NP-hard. To address the original long-term optimization problem, we decompose it into a series of one-slot subproblems, and then optimize the short-term subproblem in two phases (i.e., content caching and user association). We further propose a mobility-aware online caching algorithm to achieve content caching, and a lazy re-association algorithm to determine user association based on matching theory. Trace-driven evaluation results demonstrate that the proposed framework has superior performance on reducing system costs.

The revolutions of computing and communication have opened up demands for the high quality of service (QoS), such as high data transmission, high reliability, and low latency. These new opportunities have spawned numerous studies on edge computing and artificial intelligence (AI), even the cooperation between them, referred to as edge intelligence. However, there are a number of handicaps that prevent edge intelligence from being used as a generic platform. The most intractable one is the heterogeneity and un-credibility among edges, hindering the way of sharing the learning results reliably, flexibly, and efficiently. In this paper, we propose a blockchain-assisted edge intelligence (B-EI) approach to solve the problem. The edge learning nodes train their local intelligence, followed by the improved blockchain to share the local intelligence, constructing edge intelligence among the heterogeneous and uncredible edges. Specifically, the improved blockchain employs a novel learning-measured consensus protocol, named Proof of Learning. The edges, also acted as the blockchain nodes, compete to have more superior local intelligence, instead of solving a hashed result. The superior local intelligence is then shared and distributed with other edges. It is not only beneficial to achieve edge intelligence, but also efficient to employ the computation resource, by replacing the hashing as the intelligence training. In order to show the potential benefits, we then use the proposed B-EI approach to solve a joint resource assignment problem. Simulation results show that our scheme outperforms the other state-of-art solutions, in terms of training episodes, and resource utility.

Explosively increasing multimedia services and applications, e.g., automatic speech recognition (ASR), have aggravated the burden on the cloud server in mobile networks. To address the challenge, mobile edge computing has emerged for partially alleviating the workload of the cloud server and enhancing the quality of service of mobile users. In this paper, we aim to employ the technique of edge-cloud computing to accelerate the processing of ASR tasks generated by users in mobile networks. Particularly, we deploy a convolutional neural network based encoder in each edge server to extract features of the audio data. Based on certain network constraints (i.e., user association and edge servers’ storage/computing capacity), we propose a low-complexity and distributed iterative greedy method to address the formulated nonlinear mixed-integer nonconvex optimization problem. Simulation results demonstrate the effectiveness of the proposed scheme on reducing the total delay in the network.

Sleeping cell problem refers to the degradation or unavailability of network services without triggered alarm, which is one of the most critical issues in current mobile networks. This problem is generally not detectable by the operators but only revealed after users’ complaints occur. Therefore, it leads to the degradations of network performance in the service provision in the long run. To address this problem, we introduce a cloud-based sleeping cell detection platform into radio access networks (RANs) to detect the sleeping cells and deal with them automatically. In the cloud RANs (C-RANs), we combine and improve different methods employed in the pioneering studies in this field, and creatively use labeled training data and ensemble learning method for improving the accuracy. Particularly, we utilize expert optimization experience for further improving the detection framework. To evaluate the proposed ensemble learning based sleeping cell detection framework, we use a time-series dataset of Key Performance Indicator (KPI) in a real-world network. Trace-driven evaluation results show that the proposed framework can achieve up to 14.38% and 20.50% improvements compared with two existing schemes, respectively.

Duplicated download has been a big problem that affects the users' quality of service/experience (QoS/QoE) of current mobile networks. Edge caching and Device-to-Device communication are two promising technologies to release the pressure of repeated traffic downloading from the cloud. There are many researches about the edge caching policy. However, these researches have some limitations in the real scenarios. Traditional methods are lacking the self-adaptive ability in the dynamic environment and privacy issues will occur in centralized learning methods. In this paper, based on the virtue of Deep Q-Network (DQN), we propose a weighted distributed DQN model (WDDQN) to solve the cache replacement problem. Our model enables collaboratively to learn a shared predictive model. Trace-driven simulation results show that our proposed model outperforms some classical and state-of-the-art schemes.

Recently, mobile edge computing has received widespread attention, which provides computing infrastructure via pushing cloud computing, network control, and storage to the network edges. To improve the resource utilization and Quality of Service, we investigate the issue of task offloading for End-EdgeCloud orchestrated computing in mobile networks. Particularly, we jointly optimize the server selection and resource allocation to minimize the weighted sum of the average cost. A cost minimization problem is formulated underjoint the constraints of cache resource and communication/computation resource of edge servers. The resultant problem is a Mixed-Integer Non-linear Programming, which is NP-hard. To tackle this problem, we decompose it into simpler subproblems for server selection and resource allocation, respectively. We propose a low-complexity hierarchical heuristic approach to achieve server selection, and a Cauchy-Schwards Inequality based closed-form approach to efficiently determine resource allocation. Finally, simulation results demonstrate the superior performance of the proposed scheme on reducing the weighted sum of the average cost in the network.

Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the expectation convergence of FADE. Trace-driven simulation results demonstrate the effectiveness of the proposed FADE framework on reducing the performance loss and average delay, offloading backhaul traffic, and improving the hit rate.

General traffic analysis based on deep packet inspection (DPI) techniques at switches cannot grasp the detailed knowledge of network applications going into internal switches, and the statistics-based reports of switches lack flow-level recognition of the traffic. Besides, DPI is generally expensive and has limited performance. Therefore, network-wise accurate flow-awareness by packet sampling is highly desirable for fine-grained quality of service guarantee, internal network management, traffic engineering, security analysis, and so on. In this paper, we propose a Spatial-Temporal Collaborative Sampling (STCS) framework in the flow-aware software-defined networks (SDNs). Particularly, considering the spatial-temporal factors and limits of network resources, the formulated STCS problem aims to maximize the network-wise sampling accuracy of flows including mice flows and elephant flows by characterizing both of the comprehensive influences of switches and the effects on sampling accuracy imposed by the collaborative strategy among switches in the spatial-temporal dimension. We propose a suboptimal approach to address the complex STCS problem in two steps: 1) Top-K switch selection based on the iterative comprehensive influence, and 2) sampling time slot allocation based on the local value maximization. Trace-driven evaluation results demonstrate the effectiveness of the proposed framework on improving the sampling accuracy and reducing redundant packets.

With the development of the Internet of Things (IoT) and the birth of various new IoT devices, the capacity of massive IoT devices is facing challenges. Fortunately, edge computing can optimize problems such as delay and connectivity by offloading part of the computational tasks to edge nodes close to the data source. Using this feature, IoT devices can save more resources while still maintaining the quality of service. However, since computation offloading decisions concern joint and complex resource management, we use multiple Deep Reinforcement Learning (DRL) agents deployed on IoT devices to guide their own decisions. Besides, Federated Learning (FL) is utilized to train DRL agents in a distributed fashion, aiming to make the DRL-based decision making practical and further decrease the transmission cost between IoT devices and Edge Nodes. In this article, we first study the problem of computation offloading optimization and prove the problem is an NP-hard problem. Then, based on DRL and FL, we propose an offloading algorithm that is different from the traditional method. Finally, we studied the effects of various parameters on the performance of the algorithm and verified the effectiveness of both the DRL and FL in the IoT system.

General traffic analysis based on Deep Packet Inspection (DPI) techniques at the gateways or access points cannot grasp the detailed knowledge of network applications going among internal nodes, and the statistics-based reports of routers are also lack of flow-level recognition of the traffic in the form of only five tuple. Therefore, network-wise accurate flow-awareness by packet sampling is highly desired for fine-grained quality of service guarantee, internal network management, traffic engineering, and security analysis and so on. In this paper, we propose a Spatio-Temporal Collaborative Sampling (STCS) problem based on the Software-Defined Networking (SDN) technique. The goal of STCS is to maximize the network-wise sampling accuracy of both elephant and mice flows, which considers both of the comprehensive influences of nodes and the effect on sampling accuracy imposed by the collaborative strategy among nodes in the time dimension. We present a approach to calculate the near optimal solution of STCS in two steps: 1) Top-K nodes selection by iterative comprehensive influence, and 2) spatio-temporal cosampling solution based on the local value maximization strategy. We evaluate the proposed approach by a realistic large-scale topology, and the results show that the sampling accuracy can be effectively improved by the method, especially for mice flows, and the redundant ratio of sampled packets is reduced by 34.4%.

Mobile edge computing enabled vehicular networking has appeared as a promising solution to the emerging resource hungry vehicular applications. In this paper, we study the computation offloading in a cognitive vehicular network that reuses the TV white space (TVWS) bands. We propose to maximize the average economical profit of the service provider by jointly considering communication and computation resource allocation, while guaranteeing network stability and the QoS of TVWS primary users. Based on Lyapunov optimization, we design an per-frame algorithm to tackle the joint optimization problem, where we first derive the closed-form solution for computation resource allocation, and then develop a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for radio resource allocation. Simulation results demonstrate that the proposed algorithm can flexibly balance the profit-delay tradeoff, and can improve the economical profit of the service provider significantly as compared with the existing schemes.

Cloud gaming is a novel service provisioning paradigm, which hosts video games in the cloud and transmits interactive game streaming to players via the Internet. In this model, the cloud is required to consume tremendous resources for video rendering and streaming, especially when the number of concurrent players reaches a certain level. On the other hand, different players may have distinct requirements on Quality-of-Experience, such as high video quality, low delay, etc. Under this circumstance, to ensure an overall satisfaction for all players with finite cloud resources becomes a major challenge to existing cloud services. This paper employs game theory to the cloud gaming scenario and proposes a model to meet players' overall requirements with low cost. This game is proved to be a potential game with determining a devised potential function. Our experiment has shown that, with our algorithm, players can achieve a mutually satisfactory steady state, and the system will reduce the overhead up to 50% within the time complexity of O(Mlog M), where M is the number of physical servers.

In recent years, due to the rapid development of mobile multimedia services integrated with online social networks, how to select influential users (seed users) to promote multimedia content propagation has attracted more and more attention. However, little work has been done for large-scale offline face-to-face (Device-to-Device, D2D) content propagation. Previous studies have much limitations in this scenario due to their small-scale or synthetic data sets. In this paper, we propose the algorithm of Weighted LeaderRank with Neighbors (WLRN) to select seed users in D2D mobile social networks with accuracy to promote offline multimedia content propagation. We consider the importance of users' 2-hop neighbors. The evaluation of our algorithm is carried out on a realistic large-scale D2D data set based on the high performance computing platform of Apache Spark. The experiment results show the efficiency of the algorithm both in terms of content propagation coverage and time cost.

To satisfy rapidly increasing multimedia service requests from mobile users, content caching at the network edges (e.g., base stations) has been regarded as a promising technique in future mobile networks. In this paper, by virtue of Deep Reinforcement Learning (DRL) with respect to solving complicated control problems, we propose a framework on Double Deep Q-Network for cooperative edge caching in mobile networks. Particularly, we aim at minimizing the long-term average content fetching delay of mobile users without requiring any priori knowledge of content popularity distribution. Trace-driven simulation results show that our proposed framework outperforms some existing caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU) and First-In First-Out (FIFO) caching strategies by 7%, 11% and 9% improvements, respectively. Besides, our proposed work is further shown that only average 4% performance loss exists compared to an omniscient oracle algorithm.

Edge caching is a promising method to deal with the traffic explosion problem towards future network. In order to satisfy the demands of user requests, the contents can be proactively cached locally at the proximity to users (e.g., base stations or user device). Recently, some learning-based edge caching optimizations are discussed. However, most of the previous studies explore the influence of dynamic and constant expanding action and caching space, leading to unpracticality and low efficiency. In this paper, we study the edge caching optimization problem by utilizing the Double Deep Q-network (Double DQN) learning framework to maximize the hit rate of user requests. Firstly, we obtain the Device-to-Device (D2D) sharing model by considering both online and offline factors and then we formulate the optimization problem, which is proved as NP-hard. Then the edge caching replacement problem is derived by Markov decision process (MDP). Finally, an edge caching strategy based on Double DQN is proposed. The experimental results based on large-scale actual traces show the effectiveness of the proposed framework.

Content transmission over Mobile Opportunistic Networks is in the manner of “store-carry-forward” due to opportunistic contacts between node pairs. Most of the existing works focus on the “forward” process rather than how to “store” content in the network. Moving contents to the edge of network can significantly offload network traffic while satisfying content requests from mobile users locally. In this paper, considering the preference of nodes for different content items, we propose Fetcher Selection Greedy Algorithm (FSGA), a novel strategy to select a subset of nodes for cooperative caching scheme by evaluating each node utility in the network. To improve the cooperative caching opportunity and reduce the traffic pressure of core network, we introduce the Heterogeneous Mobile Opportunistic Networks (HMONs)architecture by deploying an Access Point (AP)which is acting as a bridge to connect the core network and mobile nodes. The mobile nodes in HMONs are segmented into two kinds, one is called$Fetchers$which are responsible for caching content from AP then forwarding them to the other ones, and the other mobile nodes are$Regulars$. We formulate and analyze the average content transmission delay in different transmission conditions. Finally, our experiment results indicate that the cooperative caching scheme improves the network performance.

Caching at the edge of mobile networks can significantly offload network traffic while satisfying content requests from mobile users locally. The contents can be requested from the proximity users via Device-to-device (D2D) communications while proactive caching the popular content to local users. However, the assumptions that content popularity is equal to user preference in several existing studies, which are invalid and not rigorous due to the fact that content popularity is calculated by the statistic of user requests within a certain period while user preference reflects the probability of a content requested by the individual user. Motivated by this, in this paper, we study the edge caching optimization of hierarchical wireless networks. Our aiming is to maximize the size of content offload by D2D communications. In particular, the edge caching policy with D2D sharing model based on the analysis of user mobility and social relationship is derived. We first prove the problem is NP-hard and then formulate it as a Markov Decision Process (MDP) problem, finally a Q-learning based distributed content replacement strategy is proposed. The large-scale real trace based experiment results show the effectiveness of our proposed framework.

The technology of Internet of Things (IoT) has been gaining great popularity in recent years, as it provides an effective and immediate bridge between the physical world and the virtual objects in the cyber space, which can lead to innovative applications and services with high efficiency and productivity. However, IoT is just at the beginning stage of a longer journey. In-depth research and development efforts on systems, networks and architectures of IoT for efficient large-scale deployments are still required to fill the gaps between the current performance and service requirements, particularly with the predicted importance of IoT in the upcoming years, improved connectivity and communication among numerous devices will become necessary and critical.

The explosive growth of content requests from mobile users is stretching the capability of current mobile networking technologies to satisfy users' demands with acceptable quality of service. An effective approach to address this challenge, which has not yet been thoroughly studied, is to offload network traffic by caching popular content at the edges (e.g., mobile devices and base stations) of mobile networks, thus reducing the massive duplication of content downloads. In this paper, we address the system modeling, large-scale optimization, and framework design of hierarchical edge caching in device-to-device aided mobile networks. In particular, taking into account the analysis of social behavior and preference of mobile users, heterogeneous cache sizes, and the derived system topology, we investigate the maximum capacity of the network infrastructure in terms of offloading network traffic, reducing system costs, and supporting content requests from mobile users locally. Our proposed framework has a low complexity and can be applied in practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

As the game industry matures, processing complex game logics in a timely manner is no longer an insurmountable problem. However, current cloud-based mobile gaming solutions are limited by their relatively high requirements on Internet resources. Also, they typically do not consider the geographical locations of nearby mobile users and thus ignore the potential cooperation among them. Therefore, inspired by existing cloud computing techniques, we propose an ad hoc mobile-cloudlet-cloud based approach to implement cooperative gaming architecture. In this paper, two modules of the architecture are introduced: (1) progressive game resources download, by which mobile users can adaptively download gaming resources from cloud servers or nearby mobile users, (2) ad-hoc mobile based cooperative task allocation, by which gaming components can be executed dynamically on local devices, nearby devices, stationary cloudlet(s), or cloud servers. The mechanisms of both modules are formulated as optimization problems and algorithms are proposed to solve them. Simulations results based on real mobility traces show that our system's performance depends highly on the ad-hoc network environment. Our scheme has lower system resource usage while utilizing resources of nearby devices, compared to the cloud-based gaming architecture; and performs better with short on-device task duration compared to code-offloading based architecture.

With the rapid development of various real-time services, there is urgent need for application-aware capabilities in realtime network to meet the higher demand for network's quality of service (QoS), security policy and so on. Accurate and cost-effective collection of flow information is needed. However, the contradiction between the real-time, accuracy and performance cost in the passive traffic information collection mode of old board makes it difficult to build application-aware realtime network of high-accuracy. In this paper, we propose an agile application-awareness network (A3N) for software-defined networks (SDN). A3N implements a flow-based self-adaptive sampling strategy (FSS) for the incoming traffic and combines a parallel deep packet inspection (DPI) with high-performance to perceive network traffic changes. Experimental results demonstrate the proposed A3N can gain good performance with low costs, and also provide real-time application-aware services with deep operational visibility.

To address the challenge arising from mobile users' increasing demands for multimedia services, applying content caching in heterogeneous networks (HetNets) is regarded as an effective way to offload traffic and improve the capacity of mobile networks. In this paper, we aim at designing novel content caching strategies in HetNets to offload the network traffic and support users' requests locally. Specifically, based on some practical network constraints (i.e., patterns of user requests, link capacity and heterogeneous cache sizes) and the derived network topology, we propose a low-complexity and practicable distributed collaborative hierarchical caching framework by decomposing the formulated large-scale optimization problem into a series of simpler subproblems. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

Recently many studies demonstrate that exploiting the Device-to-Device (D2D) content sharing in offline Mobile Social Networks (MSNs) is a promising solution to offload cellular data to local connectivities in proximity to reduce the duplicated cellular transmissions via the backbone network demanded by nearby users and hence to improve users' quality of service (QoS). However, related D2D-based social sharing and offloading proposals are based on either assumptions and theoretical models, or limited data-driven analysis caused by small scale of data sets (e.g. hundreds of MSN users) or single-dimensional feature (e.g. human mobility only), which severely restricts applications in practice. In this paper, we perform the world-first large-scale measurement and analytics on D2D-based content sharing groups from the perspective of social networks via the platform of Xender, a leading global D2D sharing platform in Asia. We analyze the behaviours of about 30 million users with 443 million D2D transmissions of 17 million files in 884 thousand social groups, and unveil the details of social structure properties, network motifs, cascade trees of friendship and propagation, which are helpful for improving the service of social D2D sharing. Finally we discuss challenges and opportunities for improving social D2D sharing services.

The heavy multimedia traffic produced by mobile users poses great challenges for the mobile network operators, especially in the areas with large user densities but limited cellular network capacities (e.g. India). Recently, many studies demonstrate that exploiting the device-to- device(D2D) content sharing in offline Mobile Social Networks is a promising solution to cellular data offloading. However, such approaches are based on either unrealistic assumptions, or limited data analytics caused by small data size (e.g. hundreds of MSN users) or single-dimensional feature (e.g. human mobility only), which severely restricts their applications in practice. To address this issue, this paper performs the first large-scale data measurement and multi-feature analytics of D2D content sharing. Specifically, by using Apache Spark over a 20-server cluster, we analyze the behaviors of 30 million users (with 40 billion D2D transmissions and 16 million content files) of Xender, a leading global D2D sharing platform. Several important features are studied, including performance basics, content properties, location relations, meeting dynamics, and social characteristics. Furthermore, as a proof-of-concept study of our analytics, we also develop a multi-feature learning based framework, which demonstrates the large potentials of predicting and recommending D2D sharing activities using machine learning methods.

To deal with explosively growing demands for multimedia contents from mobile users, content caching in base stations has been considered as an effective solution to improve the network performance by, e.g., offloading network traffic and improving users' Quality of Service (QoS). Moreover, the proactive caching policy in a cache- enabled system needs to be optimized taking into account of content delivery by wireless transmissions. Thus, in this paper, we investigate and propose an efficient resource allocation scheme for min-rate guaranteed content delivery in the downlink multiuser cache-enabled orthogonal frequency division multiple access small cell networks (OFDMA-SCNs). Our aim is to maximize the weighted sum of data rates in an OFDMA-SCN based on the constraints of the caching method, users' QoS, subcarrier reuse and small base stations' transmit power. We employ the alternating direction multiplier method to decompose the formulated complex nonconvex optimization problem into a series of simpler subproblems for which optimal solutions can be easily obtained, and propose corresponding low-complexity methods to solve the subproblems and then the whole problem. Numerical results demonstrate the effectiveness of the proposed resource allocation scheme.

Information communication technology has become more and more critical in every domain of the human society. Large-scale networking systems are deployed everywhere, like, transportation, telecommunications, health care, government, entertainment, education, military, and so on. However, while people's lives severely rely on the networked services in the mixed cyber and physical world, the emerging various types of networks are particularly vulnerable to numerous privacy and security threats, and there has been an increasing number of serious attacks, shutdowns, leaks or blockages on the Internet, particularly in those systems containing a huge amount of critical information and important service protocols, which inevitably need to remain secure and protected all the time. Therefore, it is surely important to ensure that the networking systems should be developed according to the user demands with satisfying performance, but it is seriously important as well to make sure that these networking systems are secure in certain with regards to the standards. And hence, how to protect the systems and users from being compromised on the Internet has become a very important research issue in recent years. The objective of this special issue is to collect, present, and integrate the advanced research studies on network security and innovative techniques on communication techniques concerning the safety of information of systems and users. Also, it is important to discuss the balance of security techniques with the required system performance and efficiency. After careful review of a number of highly qualified submissions, the editorial committee accepted 8 outstanding drafts to be published in this special issue. To make sure of the security of programs, generally, Address Space Layout Randomization (ASLR) is used, but it has a strong weakness on the address space of the program being randomized only once at loading. Therefore, it cannot prevent those advanced code reuse attacks. Chen et al1 discusses an approach on instantaneous and continual address space re-randomization, called JIT-ASR (Just-In-Time Address Space Re-randomization) for thwarting the attack by utilizing the virtual memory management and changing ceaselessly the address space, and thus, the attacker always has to use stale addresses for failing in getting the correct payload. The SPEC CPU2006 benchmark is used to demonstrate its effectiveness as well as low run-time performance overhead. There is another study also focusing the security issues for Intrusion Detection System (IDS)2 based on various classifiers using system calls, executed by the inspected code as feature, and thus a camouflage algorithm that is used to modify malicious code to be classified as benign, while preserving the code's functionality, for decision tree and random forest classifiers. The research shows that it is not enough to provide a decision tree–based classifier with a large training set to counter malware. Turning the focus to the security space for keys and encryptions, we would like to introduce an interesting paper,3 which increases secret key capacity in OFDM systems based on a geometric program approach and solves the open problem of key capacity. Furthermore, an underlying propagation protocol is designed in the paper to realize the power allocation scheme. The proposed scheme is evaluated to achieve greater key capacity, especially at low signal-to-noise ratio region. And hence, the OFDM system becomes more capable and secure for mobile users. For the purpose of enhancing the authentication encryption schemes used in many scenarios such as access control, encryption, enhancing trust between multiple parties, and assuring the originality of a message, the study in Mazumder et al4 proposes a simple scheme instead for Internet-of-Things devices, which have limited resource, memory, power, and processors. And thus, a simple construction of IV-based AE where block cipher compression function is used as encryption function. The proposed scheme's efficiency rate is with reasonable privacy security bound. In addition, it can encrypt arbitrary length of message in each iteration without padding. Networking systems can formulate a strong infrastructure to support public security as well, and the study5 focuses the user-to-user (device-to-device, D2D) communications, which explicitly need to utilize the private storage for caching and forwarding and thus has serious security problem and proposes a Markov-chain analytical framework for sharing public-safety videos of city surveillance. The paper develops a new Traffic Burden Switching Markovian (TBSM) model to evaluate the performance of transmitting real-time FGA video in wireless networks with D2D communications, and finally, the performance results show the significant varying performance among D2D areas with different geographical locations in D2D enabled wireless networks, which is referred to as multi–D2D-area diversity. The balance between security and efficiency exists in many areas, and aiming at the high efficiency multicore architecture, the study in Xu and Leppanen6 proposes, PEN, a power-law enhanced network design. Because authors measure and investigate the characteristics of several parallel applications, it is found that the distribution of traffic is similar as the power law. Therefore, the proposed network is based on a dual-net concentrated mesh, where two physical networks are implemented for processing different messages and improve throughput. By evaluating the proposed design with synthetic traffic and real applications by using a simulator, the network latency of the proposed design has reduced by 32% compared with regular mesh. Also concerning efficiency when executing applications, the average energy delay product of the proposed design is significantly better than mesh and concentrate mesh. There is another paper discussing networking systems, for application-specific wormhole Network-on-Chip architecture, in Zhuansun et al,7 which proposes adaptive multipath routing algorithm for guaranteeing the deadlock free. The methodology presents an analytical model for overall average delay of wormhole network on chip by queueing theory and uses a Linear Programming-based methodology to minimize overall average delay. The methodology guarantees deadlock freedom by efficient branch-and-bound algorithm, along with some simulation results. The last paper in this special issue8 turns to focus on the energy efficiency with the network stability and security in heterogeneous distributed systems. The study solves the problem of minimizing the schedule length of an energy consumption-constrained parallel application in heterogeneous distributed systems based on a dynamic voltage and frequency scaling (DVFS) energy-efficient design technique, which is divided into two subproblems in this study, namely, satisfying energy consumption constraint and minimizing schedule length. From experiments, the actual energy consumption values do not always exceed but are close to the given energy consumption constraints. In addition, the minimum schedule lengths are generated using the proposed algorithm. On behalf of the editorial team, we would like to express our sincere thanks to all the involved draft authors and reviewers for the great support and contribution to the special issue. Also, we hope to show great thanks to the editorial committee members for their excellent effort. Finally, we are very grateful to the editorial staff for their help. Without the effort of all aforementioned people's dedication, it would have been impossible to publish this special issue.

Summary Because of the exponential growth of mobile users' demand for multimedia services in recent years, the increasing network traffic load gets a close attention of the mobile network operators. For the mobile traffic explosion issue to be solved, there are many efforts trying to offload the mobile traffic from infrastructure cellular links to direct local short‐range communications among groups of users, which is called device‐to‐device sharing (D2D) in mobile social networks. Although there have been a number of studies for improving the exploitation of friends, contents, and sharing performance, there is no any large‐scale measurement‐based study to analyze the realistic D2D sharing service. We focus on the empirical trace fromXender, a popular mobile application for D2D sharing, and implement an effective big data processing platform based onSparkwith customized algorithms. Extensive analysis and discussions are carried out from the perspectives of general time series statistics, content properties, and social graph basics. The trace‐driven analysis exploits a number of implications regarding power law distribution for content popularity disparity, clustering effects of user relationships, and so on. We further discuss the potentials of improvingXender's quality of service and optimizing its system resource, and hopefully, our study can offer useful guidelines for not onlyXenderbut also those growing global social D2D sharing services.

To deal with the explosive growth in multimedia service requests in mobile networks, caching contents at the cells (base stations) is regarded as an effective emerging technique to reduce the duplicated transmissions of content downloads, while heterogeneous networks (HetNets) are regarded as an effective technique to increase the network capacity. Yet, the combination of content caching and HetNets for future networks (i.e., 5G) is still not well explored. In this paper, we propose an efficient collaborative multi-tier caching framework in Het-Nets. In particular, based on patterns of user requests, link capacities, heterogenous cache sizes, and the derived system topology, we focus on exploring the maximum capacity of the network infrastructure so as to offload the network traffic and support users' content requests locally. Due to the NP-hardness of the complex multi-tier caching problem, we approximately decompose it into some subproblems that focus on the caching cooperation at different tiers by utilizing the derived system topology. Our proposed framework is low-complexity and distributed, and can be used for practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

Due to explosive demands of multimedia services from mobile users, the growing network traffic load becomes a severe challenge for mobile network operators (MNOs). To address this problem, content caching is regarded as an effective emerging technique to reduce the duplicated transmissions of the content downloads demanded by mobile users, while heterogeneous networks (HetNets) are regarded as an effective technique to increase the network throughput. Thus, this paper focuses on content caching in HetNets to offload the weighted network traffic, in which we consider the problem of minimizing the weighted expected sum of traffic load of accessing the requested contents. By transforming the irregular problem into a binary integer linear programming problem, we propose a novel suboptimal heuristic algorithm with polynomial-time complexity to solve the problem, instead of using the existing optimal branch-and-bound method with exponential-time complexity. Numerical results demonstrate that our proposed content caching framework can reduce the weighted expected sum of traffic load significantly.

Due to environmental concerns of rising energy consumption caused by explosive growth in the demands of wireless multimedia services, energy efficiency has become an important consideration in the design of future wireless communication systems. In this paper, we investigate and propose an energy-efficient scheme of joint antenna-subcarrier-power allocation for min-rate guaranteed services in the downlink multiuser orthogonal frequency division multiplexing distributed antenna systems (OFDM-DASs) with limited backhaul capacity. Our aim is to maximize the energy efficiency in an OFDM-DAS based on the constraints of users' Quality of Service, subcarrier reuse, backhaul capacity, and remote antenna units' transmit power. By exploring the properties of the complex nonconvex energy efficiency optimization problem, we transform the problem into an equivalent problem based on fractional programming, and then, use the alternating direction multiplier method to decompose the problem into a series of simpler subproblems, where their optimal or suboptimal solutions can be easily achieved. We propose the corresponding low-complexity methods to solve the subproblems and, then, the whole problem. The numerical results demonstrate the effectiveness of the proposed low-complexity energy-efficient scheme and illustrate the fundamental tradeoff among energy consumption, spectral efficiency, and energy efficiency.

Due to the exponentially increasing demands for multimedia services over recent years, the growing network traffic load becomes a severe concern for the mobile network operators (MNOs). However, the wireless link capacity, the radio access networks, and MNOs' backhaul networks cannot deal with the traffic load effectively. To solve this problem, content caching is regarded as an effective emerging technique to reduce the duplicated transmissions of the content downloads demanded by mobile users and improve users' quality of service (QoS). Therefore, in this paper, we mainly focus on the cooperative cell caching for future mobile networks, where each cell (e.g., base station) can cache popular contents for improving QoS especially on the overall delay performance of users. The task is formulated as a problem of minimizing the expected overall user delay of accessing the demanded contents. Instead of rewriting it as a non-linear and non-convex problem based on an approximate transformation, we convert it to a linear programming problem by using a novel equivalent transformation. To solve the problem, rather than using the existing branchand-bound (BNB) method, which suffers from exponential-time and exponential-space complexity, we propose a new distributed suboptimal algorithm, which has polynomial-time and linearspace complexity. Numerical evaluation results demonstrate that our proposed cooperative cell caching framework can reduce the expected overall delay significantly.

To solve the mobile traffic explosion problem, there have been many efforts to try to offload the mobile traffic from infrastructured cellular links to direct local short-range communications among users. In this paper, we propose a novel framework of traffic offloading by Tag-Assisted Social-Aware opportunistic sharing in mobile social networks, TASA, to offload traffic by device-to-device sharing. Based on the evaluation of the tags of users and contents, we select a subset of users who are likely to receive the same content as initial seeds depending on their spreading impacts in online SNSs and their mobility patterns in offline MSNs. Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, LTE D2D) with each other. The observation from SNS activities reveals that individual users have distinct access patterns, which allows TASA to further exploit the user-dependent access delay between the content generation time and each users access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TASA can reduce up to 78.9% of the cellular traffic.

Contrary to conventional gaming-on-demand services that stream gaming video from cloud to players' terminals, a decomposed cloud gaming platform supports flexible migrations of gaming components between the cloud server and the players' terminals. In this paper, we present the design and implementation of the proposed decomposed gaming system. The cognitive resource optimization of the system under distinct targets, including the minimization of cloud, network, and terminal resources and response delay, subject to quality of service (QoS) assurance, is formulated as a graph partitioning problem that is solved by exhaustive searches. Simulations and experimental results demonstrate the feasibility of cognitive resource management in a cloud gaming system to efficiently adapt to variations in the service environments, such as increasing the number of supported devices and reducing the network bandwidth consumption of user terminals, while satisfying different QoS requirements for gaming sessions. We also suggest two heuristic algorithms based on local greedy and genetic algorithm approaches, which can potentially provide scalable but suboptimal solutions in large-scale implementations.

Cloud gaming systems host the game in the cloud, while Gameplays and views are streamed to the players' terminals in the form of encoded video frames. To address the high-bandwidth issue of real-time gaming video transmission, we have proposed a cloudlet-assisted multiplayer cloud gaming system to encourage cooperative video sharing, which exploits the similarities of video frames among multiple players in the same crowd playing the same game via a secondary ad hoc network. In this paper, we provide a detailed modeling of the proposed system, including the correlation between video frames, mobility of terminal devices, and diversity of network quality of service for distinct players. With necessary mathematical formulations, we study the players' behaviors regarding the cooperative sharing patterns to optimize the system performance in terms of the quality of users' experience. Also, heuristic algorithms are proposed to reduce the computational complexity. Empirical study and trace-driven simulation results illustrate the impact of mobility on the system performance and show that the proposed solution is able to provide better quality of experience compared with the existing platform.

Online games have been played mainly in desktop computers over wired networks because of high speed and intensive computation requirements. The advances in mobile devices and ever increasing wireless link bandwidth motivate us to study whether players can enjoy online gaming over broadband wireless networks such as mobile Worldwide Inter-operability for Microwave Access WiMAX. In this paper, we carry out comprehensive measurements of the World of Warcraft WoW over the mobile WiMAX in Seoul, Korea, and analyze the network performance focusing on two aspects: 1 network layer dynamics such as round trip time, jitter, and packet loss and 2 WiMAX link layer statistics such as the radio signal strength, handovers, and piggyback mechanism. From the empirical data, we set up performance models and evaluate the performance of WoW over WiMAX. We also discuss how to improve the service quality of online gaming over WiMAX.Copyright © 2013 John Wiley & Sons, Ltd.

As the game industry matures, processing complex game logics in a timely manner is no longer an insurmountable problem. Many researchers are now trying to find ways to optimize the gaming system regarding the network usage, local resource utilization, and energy consumption. However, current cloud-based mobile gaming solutions are limited by their relatively high requirements on Internet resources. Also, they typically do not consider the geographical locations of nearby mobile users and thus ignore the potential cooperation among users. Therefore, inspired by existing cloud computing techniques and the concept of ad-hoc cloudlet computing, in this paper, we propose an ad-hoc cloudlet based gaming architecture. Two modules of the architecture are introduced: 1) progressive game resources download, by which mobile users can adaptively download gaming resources from cloud servers or nearby mobile users according to the gaming progress, and 2) ad-hoc cloudlet-based cooperative task allocation, by which gaming components can be executed dynamically over local devices, nearby devices, or cloud servers. We also formulate the mechanism for both modules as an optimization problem and propose several algorithms for both modules, which are later used for evaluation purposes. We carry out simulations based on real mobility traces, and the results show that our system's performance depends highly on the ad-hoc network environment (the more concurrent and balanced connections within the ad-hoc network, the lower the energy costs). Also, regardless of the network environment, our system has lower energy costs while utilizing resources of nearby devices, compared to the cloud-based gaming architecture.

The ever increasing traffic demand becomes a serious concern of mobile network operators. To solve this traffic explosion problem, there have been many efforts to offload the traffic from cellular links to direct communications among users. In this paper, we propose the framework of Traffic Offloading assisted by Social network services (SNS) via opportunistic Sharing in mobile social networks, TOSS, to offload SNS-based cellular traffic by user-to-user sharing. First we select a subset of users who are to receive the same content as initial seeds depending on their content spreading impacts in online SNSs and their mobility patterns in offline mobile social networks (MSNs). Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, Device-to-device in LTE) with each other. The observation of SNS user activities reveals that individual users have distinct access patterns, which allows TOSS to exploit the user-dependent access delay between the content generation time and each user's access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TOSS can reduce up to 86.5% of the cellular traffic while satisfying the access delay requirements of all users.

Content Centric Networking (CCN) is a content name-oriented approach to disseminate content to edge gateways/routers. In CCN, a content is cached at routers for a certain time. When the associated deadline is reached, the content is removed to cope with the limited size of content storage. If the content is popular, the previously queried content can be reused for multiple times to save bandwidth capacity. It is, therefore, critical to design an efficient replacement policy to keep popular content as long as possible. Recently, a novel caching strategy, named Most Popular Content (MPC), was proposed for CCN. It considers the high skewness of content popularity and outperforms existing default caching approaches in CCN such as Least Recently Used (LRU) and Least Frequency Used (LFU). However, MPC has some undesirable features, such as slow convergence of hitting rate and unstable hitting rate performance for various cache sizes. In this paper, a new caching policy, dubbed Fine-Grained Popularity-based Caching (FGPC), is proposed to overcome the above-mentioned weak points. Compared to MPC, FGPC always caches coming content when storage is available. Otherwise, it keeps only most popular content. FGPC achieves higher hitting rate and faster convergence speed than MPC. Based on FGPC, we further propose a Dynamic-FGPC (D-FGPC) approach that regularly adjusts the content popularity threshold. D-FGPC exhibits more stability in the hitting rate performance in comparison to FGPC and that is for various cache sizes and content sizes. The performance of both FGPC and D-FGPC caching policies are evaluated using OPNET Modeler. The obtained simulation results show that FGPC and D-FGPC outperform LRU, LFU, and MPC.

With the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience. However, it is a serious issue to purchase medicine blindly without necessary medication guidance. In this paper, we propose a novel cloud-based medicine recommendation, which can recommend users with top-N related medicines according to symptoms. Firstly, we cluster the drugs into several groups according to the functional description information, and design a basic personalized medicine recommendation based on user collaborative filtering. Then, considering the shortcomings of collaborative filtering algorithm, such as computing expensive, cold start, and data sparsity, we propose a cloud-based approach for enriching end-user Quality of Experience (QoE) of medicine recommendation, by modeling and representing the relationship of the user, symptom and medicine via tensor decomposition. Finally, the proposed approach is evaluated with experimental study based on a real dataset crawled from Internet.

Cloud gaming systems host the game in the cloud, and stream players' gaming videos to the terminals in the form of encode video frames. To address the high bandwidth issue of real-time gaming video transmission, a cloudlet-assisted multiplayer cloud gaming system was proposed to encourage cooperative video sharing via a secondary ad-hoc network, on the purpose of exploiting the similarities of video frames among multiple players in a same game. However, the video cooperative sharing among players also introduces fairness problems. In this paper, we complete the ad hoc cloudlet-assisted cloud gaming system by further considering the mobility of terminal devices and the diversity of network quality for distinct players. With mathematical formulation, we study the players' behavior in cooperative sharing patterns and propose a reputation-based multiplayer fairness scheme in terms of frame encoding. Experimental results illustrate the impact of mobility on the system performance and evaluate that the proposed solution provides better fairness gaming ecosystem compared to the existing platform.

Wireless Body Area Network (WBAN) includes a set of body sensor nodes which are placed around human body, collecting data while sending them to medical center. In order to deliver the body signal to remote terminals in timely fashion, an extended communication architecture dubbed “beyond-BAN communication” was proposed. However, existing architectures are not suitable for the scenarios with high mobility of both patients and physicians due to the fluctuation of wireless links. Furthermore, when the amount of healthcare content is large, the quality of delivery is hard to be guaranteed. To address these challenging issues, we propose a novel network architecture, which integrates WBAN with the Long Term Evolution (LTE) networking and Named Data Networking (NDN). The integration with LTE is to enlarge the radio coverage and guarantee the quality of wireless transmissions, while the integration with NDN is to leverage edge router caching technique to enhance the capacity of the WBAN coordinator, and to avoid the packets loss by adapting to dynamic wireless link conditions with the adaptive streaming technique. The experimental results conducted by OPNET Modeler prove that our solution improves the Quality of Service (QoS) performance of WBAN transmission significantly.

In pervasive computing environment, due to the mobility feature of mobile terminals, the mobile service needs to dynamically adapt execution behavior to the changing computing environment as mobile user moves. However, previous researches mainly focused on deploying a service adaption module on mobile terminals or local central server to support the adaptive execution of mobile services, which brings huge overhead to mobile terminals or can hardly meet user's personalized requirements. Therefore, we propose a cloud based framework, called CAMSPF, which includes three parts: RMC (resource management cloud), AMSPC (adaptive mobile service provision cloud), and MSM (mobile service middleware). The CAMSPF deploys the service resources in RMC for realizing efficient resource management and provision, and constructs a PMSAA (private mobile service adaption agent) for each mobile user in AMSPC in order to efficiently support personalized adaptive execution of mobile service. In addition, the MCM is a lightweight software installed on mobile terminals by which CAMSPF can collect user's realtime context and monitor service request from mobile user. Our prototype implementation of CAMSPF verifies that the adaptive execution of mobile services can be performed more efficiently than other traditional approaches, with lower energy consumption on mobile terminals.

Sensing coverage is one of the most important performances of sensor networks, which characterizes how well a sensing area is monitored. Due to the limited energy supply, a minimized subset of sensor nodes should be selected to meet the requirements of coverage. Meanwhile to acquire accurate and rich information, hybrid sensor networks are designed to monitor multi-targets separately or cooperatively. In this paper, we consider the energy-efficient K-cover problem in hybrid sensor networks. First, the K-cover problem is investigated in the situation that each node is equipped with various types of sensors. Then, it is appropriately formulated as a coverage game and proved that the optimal solution is a pure Nash equilibrium. Finally, a new K-cover algorithm is designed based on game theory, where the different sensing ranges of sensors are fully considered. Simulating results validate that the proposed algorithm has high performance in coverage and can extend the network lifetime.

While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: adaptive mobile video streaming (AMoV) and efficient social video sharing (ESoV). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis.

The The ever increasing traffic demand is a serious concern of mobile network operators, and the conventional pull-based (or request-based) communication model may not be able to handle this data explosion problem. To reduce the traffic load on cellular links for disseminating content, we propose to push the content to a subset of subscribers via cellular links, and to allow the subscribers to share the content via opportunistic local connectivity (i.e. Wi-Fi ad-hoc mode). We theoretically model and analyze how the content can be disseminated by both pushing via cellular links and sharing via Wi-Fi links, where handovers are modeled based on the multi-compartment model. We also formulate a mathematical framework to optimize the content dissemination, by which the trade-off between the dissemination delay and the energy cost is explored.

Over the last decade, mobile agent (MA) systems for surveillance applications in wireless sensor networks (WSNs) has gained much attention. However, a conventional MA-based WSN may have the issues of energy efficiency and task duration as the scale of the network is increased. In order to overcome the drawbacks of using a single MA, dispatching two or more MAs for data collection simultaneously is a promising alternative in a WSN. The authors first discuss the itinerary planning issues for multiple MAs: deciding the number of MAs to be dispatched, grouping of source nodes for each MA, routing of each MA for its assigned source nodes. The authors then survey the existing algorithms for these issues, and evaluate their performance by OPNET.

Nowadays, online gaming is one of the emerging industry on the Internet. Massively Multiplayer Online Games (MMORPG) is one of the most important type of online games. Research on MMORPGs always pay attention to the network situations, such as flow imbalance, system optimization and traffic identification. The results help the game designers and network protocol engineers to improve user game experience. In this paper, we perform traffic analysis and modeling in three distinct game scenarios over two different wireless network connections in World of Warcraft(WoW), which is one of the most popular MMORPGs among the world. In addition, we contribute a random traffic generator base on ns-2 which could be a open development platform for the MMORPGs.

As mobile Internet environments are becoming dominant, how to revamp P2P operations for mobile hosts is gaining more and more attention. In this paper, we carry out empirical traffic measurement of BitTorrent service in various settings (static, bus and subway) in commercial WiMAX networks. To this end, we analyze the connectivity among peers, the download throughput/stability, and the signaling overhead of mobile WiMAX hosts in comparison to a wired (Ethernet) host. We find out the drawbacks of BitTorrent operations in mobile Internet are characterized by lower connection ratio, unstable connections amongst peers, and higher control message overhead.

In this paper, we seek to enhance the poor performance of original TCP in wireless multi-hop environments due to the intra-flow contention between TCP-DATA and TCP-ACK packets. Assuming multi-radio multi-channel networks, where each station is equipped with multiple radios and the same number of orthogonal channels, we propose to use virtually different paths with different channel assignments for TCP-DATA and TCP-ACK transmissions. Simulations show that TCP performance can be improved significantly since TCP-DATA and TCP-ACK paths hardly interfere with each other.