In recent years, the proliferation of renewable energy sources has heightened the focus on networked energy systems. These systems face significant challenges due to the unpredictable nature of energy generation and consumption, as well as the complexity of managing numerous components and parameters. To address the challenges associated with the time-consuming nature of optimization problems and the expansive solution space, we propose an innovative energy management method based on a generative diffusion model applicable to general networked energy systems. This approach aims to balance energy supply and demand while minimizing transmission costs. The efficacy of this method is validated through evaluations on real-world datasets and simulations, demonstrating a 26.6% cost reduction compared to the state-of-the-art model and a 62.8% decrease in execution time compared to existing optimizers. This research highlights the potential of generative diffusion techniques in networked energy management. Code: https://github.com/gale13/GEM.

Personalized Federated Learning (pFL) mitigates the impact of statistical heterogeneity on FL architecture to some extent by allowing participants to use personalized models based on local data distributions. The existing pFL methods optimize from the perspective of model structure, attempting to adopt strategies that maintain model processing or quickly adapt to local data distribution capabilities. Our proposed method draws inspiration from the concept of variational inference, guiding model updates by comparing prior and posterior data distributions, and innovatively applying model variational perturbations to improve robustness. Finally, we conducted multidimensional experiments and the results show that our method outperforms the current baseline. Code: https://github.com/RezinChow/VPFL.

The widespread deployment of cameras has led to an exponential increase in video data, creating vast opportunities for applications such as traffic management and crime surveillance. However, querying specific objects from large-scale video datasets presents challenges, including (1) processing massive and continuously growing data volumes, (2) supporting complex query requirements, and (3) ensuring low-latency execution. Existing video analysis methods struggle with either limited adaptability to unseen object classes or suffer from high query latency. In this paper, we present LOVO, a novel system designed to efficiently handle compLex Object queries in large-scale VideO datasets. Agnostic to user queries, LOVO performs one-time feature extraction using pre-trained visual encoders, generating compact visual embeddings for key frames to build an efficient index. These visual embeddings, along with associated bounding boxes, are organized in an inverted multi-index structure within a vector database, which supports queries for any objects. During the query phase, LOVO transforms object queries to query embeddings and conducts fast approximate nearest-neighbor searches on the visual embeddings. Finally, a cross-modal rerank is performed to refine the results by fusing visual features with detailed textual features. Evaluation on real-world video datasets demonstrates that LOVO outperforms existing methods in handling complex queries, with near-optimal query accuracy and up to 85x lower search latency, while significantly reducing index construction costs. This system redefines the state-of-theart object query approaches in video analysis, setting a new benchmark for complex object queries with a novel, scalable, and efficient approach that excels in dynamic environments.

Graph Neural Networks (GNNs) excel in many applications but struggle when trained with noisy labels, especially as noise can propagate through the graph structure. Despite recent progress in developing robust GNNs, few methods exploit the intrinsic properties of graph data to filter out noise. In this paper, we introduce ProCon, a novel framework that identifies mislabeled nodes by measuring label consistency among semantically similar peers, which are determined by feature similarity and graph adjacency. Mislabeled nodes typically exhibit lower consistency with these peers, a signal we measure using pseudo-labels derived from representational prototypes. A Gaussian Mixture Model is fitted to the consistency distribution to identify clean samples, which refine prototype quality in an iterative feedback loop. Experiments on multiple datasets demonstrate that ProCon significantly outperforms state-of-the-art methods, effectively mitigating label noise and enhancing GNN robustness.

Blockchain enhances the robustness and user engagement of Federated Learning (FL) systems but fails to meet the throughput and real-time requirements for model transmission. While sharding architectures improve system throughput, the latency introduced by mainchain model transmission remains a performance bottleneck, compromising the QoS of FL systems. In this paper, we propose a Mainchain-Free Sharding architecture, MFSChain, featuring an adaptive sharding mechanism based on hierarchical clustering. This mechanism improves shard model performance by eliminating the need for mainchain aggregation (i.e., shard-level global models). We also introduce the Federated Learning State Tree (FLS-Tree) for client management and state migration without a mainchain, alongside a lightweight storage scheme, LiFLS-Tree. Through theoretical analysis and extensive simulations, we demonstrate that MFSChain outperforms traditional blockchain and sharding architectures. Specifically, MFSChain reduces client waiting time by 17% and 24%, increases average model accuracy by 2.5% to 10% compared to traditional global models, and boosts throughput by$464 \times$while reducing transaction processing latency by 99%.

Legal document review is a time-consuming and highly specialized task, and the capabilities of intelligent legal review systems are limited and insufficient to complete detailed reviews. Traditional methods struggle with cross-references, dependencies, and context-dependent clauses. Our work introduces a multi-round RAG framework for legal document analysis, which iteratively refines queries and aggregates context to improve recall and understanding. Experiments on diverse contracts show a recall of 78.67%, outperforming baseline (57.33%) and single-round RAG (74.67%). Our analysis shows that iterative refinement effectively filters irrelevant results despite reduced precision. The multi-round approach halves missed cross-clause dependencies but reveals limitations in numerical consistency and obligation scope detection. These insights advance RAG for legal applications and provide a foundation for future work on scalable and accurate contract review.

Graph Neural Networks (GNNs) achieve strong performance in node classification tasks but exhibit substantial performance degradation under label noise. Despite recent advances in noise-robust learning, a principled approach that exploits the node-neighbor interdependencies inherent in graph data for label noise detection remains underexplored. To address this gap, we propose GD$^2$, a noise-aware \underline{G}raph learning framework that detects label noise by leveraging \underline{D}ual-view prediction \underline{D}iscrepancies. The framework contrasts the \textit{ego-view}, constructed from node-specific features, with the \textit{structure-view}, derived through the aggregation of neighboring representations. The resulting discrepancy captures disruptions in semantic coherence between individual node representations and the structural context, enabling effective identification of mislabeled nodes. Building upon this insight, we further introduce a view-specific training strategy that enhances noise detection by amplifying prediction divergence through differentiated view-specific supervision. Extensive experiments on multiple datasets and noise settings demonstrate that \name~achieves superior performance over state-of-the-art baselines.

Privacy-preserving federated learning (PPFL) is a promising secure distributed learning paradigm, which enables collaborative training of a global machine learning model through sharing encrypted local models instead of sensitive raw data. PPFL, however, is vulnerable to model poisoning attacks. Most existing Byzantine-robust PPFL solutions typically employ two non-colluding servers to achieve secure model detection and aggregation by executing interactive security protocols, which incur considerable computation and communication overheads. To tackle this issue, we propose an efficient and secure federated learning (ESFL) technique to accelerate the detection of poisonous models in PPFL. First, to improve computational efficiency, we construct a lightweight non-interactive efficient decryption functional encryption (NED-FE) scheme to protect the data privacy of local models. Then, to ensure high communication performance, we elaborately design a non-interactive privacy-preserving robust aggregation strategy, which efficiently detects the blind poisonous models and aggregates benign models. Finally, we implement ESFL and conduct extensive theoretical analysis and experiments. The numerical results demonstrate that ESFL not only achieves the confidentiality and robustness design goals but also maintains high efficiency. Compared with the baseline, ESFL effectively reduces the aggregation latency by up to 88%.

As the adoption of electric vehicles continues to grow, the demand for extensive charging infrastructure in urban areas is concurrently rising. In response to the evolving charging infrastructure shortage, private charging piles have emerged as crucial supplementary energy sources, especially in areas lacking public charging infrastructure. The sharing of private charging piles, however, introduces several challenges. Notably, the variable availability time and extremely limited usage space of private charging piles pose scheduling complexities for charging pile owners. Furthermore, the completely peer-to-peer operation of private charging piles may lead to suboptimal solutions for fulfilling overall charging demand. To comprehensively address these challenges, we explore the potential for cooperation among geographically proximate charging piles. We introduce a novel online booking mechanism paired with specialized scheduling algorithms designed for scenarios involving both multiple private charging piles and single private charging piles. Our objective is to maximize the attained revenue of charging pile owners under fully dynamic conditions on both the supply and demand sides. Through meticulous theoretical proofs, we show that our mechanism achieves advantageous competitive ratios for both scenarios when compared to the offline optimal solutions. Numerous experiments, conducted with real charging sessions, consistently demonstrate that the proposed mechanism achieves the highest revenue, providing substantial evidence for its superior performance.

Modern machine learning (ML) models have grown to a scale where training them on a single machine becomes impractical. As a result, there is a growing trend to leverage federated learning (FL) techniques to train large ML models in a distributed and collaborative manner. These models, however, when deployed on new devices, might struggle to generalize well due to domain shifts. In this context, federated domain adaptation (FDA) emerges as a powerful approach to address this challenge. Most existing FDA approaches typically focus on aligning the distributions between source and target domains by minimizing their (e.g., MMD) distance. Such strategies, however, inevitably introduce high communication overheads and can be highly sensitive to network reliability. In this paper, we introduce RF-TCA, an enhancement to the standard Transfer Component Analysis approach that significantly accelerates computation without compromising theoretical and empirical performance. Leveraging the computational advantage of RF-TCA, we further extend it to FDA setting with FedRF-TCA. The proposed FedRF-TCA protocol boasts communication complexity that isindependentof the sample size, while maintaining performance that is either comparable to or even surpasses state-of-the-art FDA methods. We present extensive experiments to showcase the superior performance and robustness (to network condition) of FedRF-TCA.

With the rise of transportation electrification, an increasing number of charging stations have been established, forming a city-scale charging system. These charging stations serve as intermediaries that connectsupplyanddemand, drawing power from the grid and renewable energy sources to provide electricity to electric vehicles. Maintaining a delicate balance between supply and demand has emerged as a significant challenge for the charging system. On amacroscopiclevel, it impacts the power grid's peak load and reliability, whilelocally, it influences electric vehicle detour events. To comprehensively model the spatio-temporal characteristics in the charging system, we partition the charging system by adopting a supply-demand-aware approach and propose OPS, an online power scheduling algorithm based on the regularization technique. OPS aims to achieve a bi-level balance between supply and demand while constraining the power output of the charging system. We substantiate the efficacy of OPS through rigorous theoretical proofs, demonstrating its comparability to the optimal solution. Furthermore, we conduct extensive evaluation experiments with real-world data sets to establish the feasibility of the proposed methodology in alleviating the supply-demand imbalance. The results indicate that OPS attains an empirical competitive ratio of less than 1.2.

Containers have emerged as a pivotal tool for service deployment in edge computing. Before running the container, an image composed of several layers must exist locally. Recent strategies have utilized layer-sharing in images to reduce deployment delays. However, existing research only focuses on a single aspect of container orchestration, like container placement, neglecting the joint optimization of the entire orchestration process. To fill in such gaps, this article introduces an online strategy that considers layer-aware container orchestration, encompassing request scheduling, container placement, and resource provision. The goal is to reduce costs, adapt to evolving user demands, and adhere to system constraints. We present an online optimization problem that accounts for various real-world factors in orchestration, including container and server expenses. An online algorithm is proposed, integrating a regularization-based approach and stepwise rounding to address this optimization problem efficiently. The regularization approach separates time-dependent container placement and server wake-up costs, requiring only current information and past decisions. The stepwise rounding process generates feasible solutions that meet system constraints, reducing computational costs. Additionally, a competitive ratio proof is provided for the proposed algorithm. Extensive evaluations demonstrate that our approach achieves about 20% performance enhancement compared to baseline algorithms.

Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However, there are two challenges existing in serverless edge computing: significant cold start latency and request blocking. Previous studies have not successfully resolved these challenges, which affect the Quality of Experience. In this paper, we formulate the Serverless Function Scheduling (SFS) problem in resource-limited edge computing, aiming to minimize the average response time. To solve this intractable scheduling problem, we first consider a simplified offline form of the SFS problem and design a polynomial-time optimal scheduling algorithm. Inspired by this optimal algorithm, we propose an Enhanced Shortest Function First (ESFF) algorithm, including function creation and function replacement. To avoid frequent cold starts, ESFF selectively decides the initialization of new function instances when receiving requests. To deal with request blocking, ESFF judiciously replaces serverless functions based on the function weight at the completion time of requests. Extensive simulations based on real-world serverless request traces are conducted, and the results show that ESFF consistently and substantially outperforms existing baselines under different settings.

Cloud-edge collaboration Architecture (CEA) enables the co-training of AI models by cloud servers and edge servers, offering a promising solution for large-scale model training. An efficient data trading mechanism helps encourage edges to invest data resources to participate in training while reducing the cost of cloud servers. Existing research on data trading within CEA focuses on static scenarios, either overlooking the dynamics of data demand and the fairness of the selected edges or assuming unknown future communication overheads. To bridge these gaps and consider the long-term fairness constraints, we propose an Online Data Trading mechanism for the CEA, called ODT, to improve the long-term utility. Technically, ODT decouples the long-term fairness constraint into a series of single time-slot sub-problems using the Lyapunov optimization method and applies dynamic programming to solve the single time-slot edge selection sub-problems. We prove the NP-hardness of the sub-problems, the performance bounds, and the computational complexity of the proposed algorithm. Evaluation results demonstrate that the proposed mechanism effectively improves long-term utility and achieves an efficient trade-off between fairness and utility.

The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response delays and incur high costs, thereby impacting the Quality of Services (QoS) at the network edge. Leveraging vector database caching to store LLM request results at the edge can substantially mitigate response delays and cost associated with similar requests, which has been overlooked by previous research. Addressing these gaps, this paper introduces a novel Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework. Firstly, we propose the VELO framework, which ingeniously employs vector database to cache the results of some LLM requests at the edge to reduce the response time of subsequent similar requests. Diverging from direct optimization of the LLM, our VELO framework does not necessitate altering the internal structure of LLM and is broadly applicable to diverse LLMs. Subsequently, building upon the VELO framework, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and devise an algorithm grounded in Multi-Agent Reinforcement Learning (MARL) to decide whether to request the LLM in the cloud or directly return the results from the vector database at the edge. Moreover, to enhance request feature extraction and expedite training, we refine the policy network of MARL and integrate expert demonstrations. Finally, we implement the proposed algorithm within a real edge system. Experimental findings confirm that our VELO framework substantially enhances user satisfaction by concurrently diminishing delay and resource consumption for edge users utilizing LLMs.

Lightweight containers provide an efficient approach for deploying computation-intensive applications in net-work edge. The layered storage structure of container images can further reduce the deployment cost and container startup time. Existing researches discuss layer sharing scheduling theoretically but with little attention paid to the practical implementation. To fill in this gap, we propose and implement a Layer-aware and Resource-adaptive container Scheduler (LRScheduler) in edge computing. Specifically, we first utilize container image layer information to design and implement a node scoring and container scheduling mechanism. This mechanism can effectively reduce the download cost when deploying containers, which is very important in edge computing with limited bandwidth. Then, we design a dynamically weighted and resource-adaptive mechanism to enhance load balancing in edge clusters, increasing layer sharing scores when resource load is low to use idle resources effectively. Our scheduler is built on the scheduling framework of Kubernetes, enabling full process automation from task information acquisition to container deployment. Testing on a real system has shown that our design can effectively reduce the container deployment cost as compared with the default scheduler.

Sharding is one of the prominent solutions to solve the scalability problem of traditional blockchains. By dividing the blockchain network into independent shards, transactions in different shards can be executed in parallel, improving the throughput of the blockchain. However, sharding also brings security issues. In a hash-based transaction sharding system, the output shard of a transaction is determined by the hash value of the transaction. Unfortunately, we show that this hashbased transaction assignment strategy can be easily exploited by attackers to conduct front-running attacks. Attackers can take advantage of the execution differences between different shards and the extra processing time of cross-shard transactions to make the attacker’s transaction executed and committed before the victim’s transaction, thereby obtaining the benefits that originally belonged to the victim. We also propose a flooding front-running attack, which introduces a single-shard flooding attack to enhance the front-running attack. Specifically, injecting a large number of junk transactions into the shard where the victim’s transaction is located can significantly extend the execution time of the victim’s transaction. We demonstrate the feasibility and practical effects of these two attacks through experiments on RapidChain, which show that a single-shard flooding attack can increase the success rate of a front-running attack by 12%. Finally, we discuss two possible mitigation measures and the cost of two proposed attacks.

Multiple unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) leverages UAVs equipped with computational resources as mobile-edge servers, providing flexibility and low-latency connections, especially beneficial in smart cities and the Internet of Things (IoT). Maximizing Quality of Services (QoS) while minimizing energy consumption necessitates developing a suitable offloading ratio and trajectory control algorithm for UAVs. However, existing research on UAV control algorithms overlooks significant challenges like the heterogeneity of user equipments (UEs) and offloading failures. Furthermore, there is a dearth of experimental validation in large-scale UAV-assisted MEC scenarios. To bridge these gaps, we introduce a QoS-aware energy-efficient multi-UAV offloading ratio and trajectory control algorithm (QEMUOT). Specifically, 1) a composite UE mobility model is proposed to enhance system heterogeneous modeling, encompassing models for high-speed, low-speed, and fixed UEs; 2) QEMUOT is devised using multiagent reinforcement learning algorithms to determine offloading ratio and trajectory control decisions. To tackle sparse reward space and offloading failures, we employ expert demonstrations for pretraining and enhance reward mechanisms; and 3) experimental simulations illustrate that our algorithm outperforms baseline algorithms in user QoS with reduced energy consumption and demonstrates superior scalability in scenarios with numerous UAVs and UEs.

Extending serverless computing to the edge has emerged as a promising approach to support service, but startup containerized serverless functions lead to the cold-start delay. Recent research has introduced container caching methods to alleviate the cold-start delay, including cache as the entire container or the Zygote container. However, container caching incurs memory costs. The system must ensure fast function startup and low memory cost of edge servers, which has been overlooked in the literature. This paper aims to jointly optimize startup delay and memory cost. We formulate an online joint optimization problem that encompasses container scheduling decisions, including invocation distribution, container startup, and container caching. To solve the problem, we propose an online algorithm with a competitive ratio and low computational complexity. The proposed algorithm decomposes the problem into two subproblems and solves them sequentially. Each container is assigned a randomized strategy, and these container-level decisions are merged to constitute overall container caching decisions. Furthermore, a greedy-based subroutine is designed to solve the subproblem associated with invocation distribution and container startup decisions. Experiments on the real-world dataset indicate that the algorithm can reduce average startup delay by up to 23% and lower memory costs by up to 15%.

Federated learning (FL) is a technique that enables clients to collaboratively train a model by sharing local models instead of raw private data. However, existing reconstruction attacks can recover the sensitive training samples from the shared models. Additionally, the emerging poisoning attacks also pose severe threats to the security of FL. However, most existing Byzantine-robust privacy-preserving federated learning solutions either reduce the accuracy of aggregated models or introduce significant computation and communication overheads. In this paper, we propose a novelBlockchain-basedSecure andRobustFederatedLearning (BSR-FL) framework to mitigate reconstruction attacks and poisoning attacks. BSR-FL avoids accuracy loss while ensuring efficient privacy protection and Byzantine robustness. Specifically, we first construct a lightweight non-interactive functional encryption (NIFE) scheme to protect the privacy of local models while maintaining high communication performance. Then, we propose a privacy-preserving defensive aggregation strategy based on NIFE, which can resist encrypted poisoning attacks without compromising model privacy through secure cosine similarity and incentive-based Byzantine-tolerance aggregation. Finally, we utilize the blockchain system to assist in facilitating the processes of federated learning and the implementation of protocols. Extensive theoretical analysis and experiments demonstrate that our new BSR-FL has enhanced privacy security, robustness, and high efficiency.

In edge computing, applications can be scheduled in the granularity of inter-dependent tasks to proximate edge servers to achieve high performance. Before execution, the edge server must initialize the corresponding runtime environment, named task startup. However, existing studies on dependent task scheduling severely ignore bandwidth constraints during task startups, which is impractical and incurs a long startup latency. To fill in this gap, we first model the task startup process with bandwidth constraints on edge servers. Then, we formulate the dependent task scheduling problem with startup latency in heterogeneous edge computing. To efficiently generate schedules and satisfy the real-time requirements in edge computing, a novel low-complexity list scheduling algorithm integrated with cloud clone, Startup-aware Dependent Task Scheduling (SDTS), is proposed. Constrained by bandwidth and computation resources, SDTS first coordinates task startup, dependent data transmission, and task execution to optimize each task’s finish time. Then, a cloud clone for each task is deployed to utilize scalable resources and initialized runtime environments. Furthermore, task scheduling refinement is designed to release the bandwidth and computation resources consumed by redundant tasks and improve the schedule. Extensive simulations based on real-world datasets show that SDTS substantially reduces 30%-60% makespan compared with existing baselines.

Containers have gained popularity in Edge Computing (EC) networks due to their lightweight and flexible deployment advantage. In resource-constrained EC environments, overbooking container resources can substantially improve resource utilization. However, existing work overlooks the complex interplay between resource provisioning and container scheduling, which may result in performance degradation or inefficient resource utilization due to highly dynamic resource heterogeneity in EC. To address this issue, this paper presents a novel joint Resource Overbooking and Container Scheduling (ROCS) algorithm. Our approach accounts for resource heterogeneity and the geographical distribution of edge nodes, and we formulate the ROCS problem to consolidate various costs and revenues into a single profit metric for service providers. To enhance resource utilization and maximize the profit of the service providers, we develop an efficient algorithm that operates within a hybrid action space scheme by leveraging soft actor-critic reinforcement learning. Furthermore, we introduce a risk assessment mechanism to mitigate overbooking risks. Large-scale simulations with real-world data traces demonstrate the efficacy of our proposed ROCS algorithm, validating its advantage of improving resource utilization within EC networks.

In edge-assisted vehicular networks, containers are very suitable for deploying applications and providing services due to their lightweight and rapid deployment. To provide high-quality services, many existing studies show that the containers need to be migrated to follow the vehicles’ trajectory. However, it has been conspicuously neglected by existing work that making full use of the complex layer-sharing information of containers among multiple users can significantly reduce migration latency. In this paper, we propose a novel online container migration algorithm to reduce the overall task latency. Specifically: 1) we model the multi-user layer-aware online container migration problem in edge-assisted vehicular networks, comprehensively considering the initialization latency, computation latency, and migration latency. 2) A feature extraction method based on attention and long short-term memory is proposed to fully extract the multi-user layer-sharing information. Then, a policy gradient-based reinforcement learning algorithm is proposed to make the online migration decisions. 3) The experiments are conducted with real-world data traces. Compared with the baselines, our algorithms effectively reduce the total latency by 8% to 30% on average.

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Cluster software upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers during edge cluster upgrades to minimize the impact on running tasks. This article proposes a latency-aware container scheduling algorithm for efficient edge cluster upgrading. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm that addresses this problem by considering the characteristics of MEC, including heterogeneous resources, image distribution, and low-latency requirements. Subsequently, a location feature extraction method based on self-attention is designed to fully extract and utilize edge node distribution. 3) Experiments based on simulated and real-world data traces demonstrate that our algorithm reduces total task latency by approximately 30% compared to baseline algorithms.

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

In Mobile Edge Computing (MEC), latency-sensitive mobile applications comprising dependent tasks can be scheduled to edge or cloud servers to reduce latency and execution costs. However, existing algorithms based on deadline distribution can hardly satisfy tight application deadlines in heterogeneous MEC due to lacking a global view of the future impacts on descendant tasks. To fill in this gap, we formulate the deadline-constrained cost optimization problem for dependent task scheduling in MEC and propose a low-complexity scheduling algorithm that considers a single task's future impacts in two stages. Specifically: (1) In the edge scheduling stage, each task is scheduled according to its successors’ latest start times instead of its sub-deadline to alleviate the lateness of its successors. An edge-only schedule plan is generated by scheduling tasks only on edge servers to save execution costs. (2) In the cloud offloading stage, in order to utilize the powerful cloud resources to satisfy the deadline, the edge-only schedule plan missing the deadline is efficiently modified by properly offloading multiple successive tasks to the cloud. Simulation results show the substantial advantage of the proposed algorithm over baselines in both online and offline scenarios.

Due to the features of lightweight and easy deployment, the use of containers has emerged as a promising approach for Mobile Edge Computing (MEC). Before running the container, an image composed of several layers must exist locally. However, it has been conspicuously neglected by existing work that task scheduling at the granularity of the layer instead of the image can significantly reduce the task completion time to further meet the real-time requirement and resource efficiency in resource-limited MEC. To bridge the gap, considering the complex dependency between layers and images, a novel layer dependency-aware container scheduling algorithm is proposed to reduce the total task completion time. Specifically: 1) We model the online layer dependency-aware scheduling problem for containers in a heterogeneous MEC, considering the layer download time and task computation time. 2) A policy gradient algorithm is proposed to solve this problem, and the high-dimensional and low-dimensional relations for layer dependencies are extracted with improved action selection. 3) Experiments based on the real-world data trace show that the proposed algorithm outperforms the image-based and layer-based baseline algorithms by 54% and 19% on average, respectively.

Energy-efficient task scheduling in data centers is a critical issue and has drawn wide attention. However, the task execution times are mixed and hard to estimate in a real-world data center. It has been conspicuously neglected by existing work that scheduling decisions made at tasks’ arrival times are likely to cause energy waste or idle resources over time. To fill in such gaps, in this paper, we jointly consider assignment and migration for mixed duration tasks and devise a novel energy-efficient task scheduling algorithm. Task assignment can improve resource utilization, and migration is required when long-running tasks run in low-load servers. Specifically: 1) We formulate mixed duration task scheduling as a large-scale Markov Decision Process (MDP) problem; 2) To solve such a large-scale MDP problem, we design an efficient Deep Reinforcement Learning (DRL) algorithm to make assignment and migration decisions. To make the DRL algorithm more practical in real scenarios, multiple optimizations are proposed to achieve online training; 3) Experiments with real-world data have shown that our algorithm outperforms the existing baselines 14% on average in terms of energy consumption while keeping the same level of Quality of Service (QoS).

Containers are becoming a popular way of running applications in edge computing. Before running the application, the edge node must download the application’s container image consisting of multiple layers. However, given the limited bandwidth in edge computing, the container startup latency due to long image download time seriously affects the real-time performance. In this article, we jointly determine the container assignment and the layer download sequence to reduce the total startup latency. We formulate the Container Assignment and Layer Sequencing (CALS) problem and prove its NP-hardness. A Layer-Aware Scheduling Algorithm (LASA) is proposed, fully considering layer sharing among images. First, layers shared by the same set of images are grouped to reduce CALS’s problem scale without affecting the optimal result. Second, considering both layer sharing and existing layer size on edge nodes, a layer-aware algorithm is designed to assign containers to appropriate edge nodes. Finally, to determine the layer download sequence on each edge node, an approximation algorithm is proposed. We further analyze the approximation ratio of LASA in the case of identical edge nodes with sufficient capacity. Extensive experiments based on real-world data show the effectiveness of LASA, which reduces the total startup latency by 40% to 60%.

The dependent task offloading problem for one single job in edge computing (EC) has drawn attention widely. Unlike most existing approaches that only focus on a single job, we aim to solve the dependent task offloading problem for multiple jobs, which is more general in the real world. To solve this problem, we propose a deep reinforcement learning (DRL) based multi-job dependent task offloading algorithm. Specifically, 1) we model edge nodes, jobs, and tasks in a resource-limited EC scenario, where the dependent tasks of multiple jobs are offloaded to the nodes to be processed. Then we model the task offloading decision as a Markov decision process (MDP) problem to minimize the transmission cost and computation cost. 2) To represent the state space of MDP and to accelerate decision-making in EC, we propose a DRL-based algorithm with the aid of graph convolutional network (GCN) to extract the dependency information of different tasks and then improve the action selection process. 3) We conduct experiments with real-world trace, demonstrating our algorithm outperforms the baseline algorithms 13.78% on average in regarding to offloading cost.

In the context of Edge Computing (EC) and Internet of Things (IoT), numerous tasks are offloaded from mobile users and sensor devices to edge nodes for further processing to reduce delay and solve the problem of insufficient local computation resources. These tasks can be mainly divided into delay-sensitive and computation-oriented tasks. The former tasks depend on the service provided by the container, while the latter tasks are submitted as a batch with task dependencies. Considering the heterogeneity of edge nodes, joint task scheduling can effectively improve resource utilization. However, relatively few researches consider the different characteristics of tasks like container constraints and task dependencies in joint task scheduling in EC. In order to fill in this gap, we propose a deep deterministic policy gradient (DDPG) based online joint task scheduling (OJTS) algorithm. Specifically, 1) We first model the problem of joint scheduling of delay-sensitive and computation-oriented tasks in resource-constrained EC scenario with the goals of maximizing system utility and minimizing system cost (weighted sum of the number and duration of unfinished tasks). 2) Then, we propose a deep reinforcement learning (DRL) algorithm to solve the above problem and make appropriate adjustments to the original network structure according to the scheduling decision. 3) Through validation on real-world trace, OJTS can improve the system utility by 26.0% and overall reward by 51.2% compared with baselines and meet real-time decision-making requirements.