In data center networks, various types of traffic with strict performance requirements operate simultaneously, necessitating effective isolation and scheduling through priority queues. However, most switches support only around ten priority queues. Virtual priority can address this limitation by emulating multi-priority queues on a single physical queue, but existing solutions often require complex switch-level scheduling and hardware changes. Our key insight is that virtual priority can be achieved by carefully managing bandwidth contention in a physical queue, which is traditionally handled by congestion control (CC) algorithms. Hence, the virtual priority mechanism needs to be tightly coupled with CC. In this paper, we propose PrioPlus, a CC enhancement algorithm that can be integrated with existing congestion control schemes to enable virtual priority transmission. PrioPlus assigns specific delay ranges to different priority levels, ensuring that flows transmit only when the delay is within the assigned range, effectively meeting virtual priority requirements. Compared to Swift CC with physical priority queues, PrioPlus provides strict priority for high-priority flows without impacting performance sensibly. Meanwhile, it benefits low-priority flows from 25% to 41% as its priority-aware design enhances CC's ability to fully utilize available bandwidth once higher-priority traffic completes. As a result, in coflow and model training scenarios, PrioPlus improves job completion times by 21% and 33%, respectively, compared to Swift with physical priority queues.

In recent years, compressed domain semantic inference has primarily relied on learned image coding models optimized for mean squared error (MSE). However, MSE-oriented optimization tends to yield latent spaces with limited semantic richness, which hinders effective semantic inference in downstream tasks. Moreover, achieving high performance with these models often requires fine-tuning the entire vision model, which is computationally intensive, especially for large models. To address these problems, we introduce Perception-Oriented Latent Coding (POLC), an approach that enriches the semantic content of latent features for high-performance compressed domain semantic inference. With the semantically rich latent space, POLC requires only a plug-and-play adapter for fine-tuning, significantly reducing the parameter count compared to previous MSE-oriented methods. Experimental results demonstrate that POLC achieves rate-perception performance comparable to state-of-the-art generative image coding methods while markedly enhancing performance in vision tasks, with minimal fine-tuning overhead. Code is available at https://github.com/NJUVISION/POLC.

With the rapid proliferation of crowd mobility data produced by ubiquitous mobile devices equipped with spatial positioning modules, deep neural networks (DNNs) have become widely applied in spatio-temporal trajectory modeling. However, recent studies have shown that DNNs are vulnerable to adversarial examples with strong transferability, which are crafted by introducing small perturbations to original examples but can cause catastrophic mistakes. To mitigate this vulnerability and enhance model robustness, we propose a novel self-supervised collaborative learning framework named Auto-GAN that consists of a generator for automatically learning robust latent features and a discriminator for providing comprehensive guidance to the generator. By leveraging the collaboration between the generator and discriminator, our proposed method significantly improves the denoising performance. Moreover, we combine point-level and feature-level constraints into training processes between original example reconstruction and adversarial example denoising, thereby effectively suppressing the potential “error amplification effect". Extensive experiments conducted on two representative real-world mobility datasets show that our proposed method can significantly enhance the model’s robustness against various adversarial attacks, while preserving the model’s prediction accuracy on original examples.

Simulating human mobility realistically and generating large-scale, high-quality trajectories are crucial for various location-based applications such as traffic management, epidemic spreading analysis, and location privacy protection. While the most popular model-free methods succeed by directly learning distribution of real-world data, they struggle to produce high-quality mobility data without leveraging the domain knowledge of human mobility. Moreover, such model-free methods primarily rely on auto-regressive paradigms, usually accompanied by error accumulation problem. To address the issues, we propose a model-free Domain-Knowledge Enhanced Generative Adversarial Network (DKE-GAN), which efficiently combines domain knowledge of urban context with model-free learning paradigm to generate high-quality mobility data. In addition, we incorporate reinforcement learning into the training process, thereby effectively alleviating the error accumulation. Furthermore, we introduce Trajectory Representation Learning (TRL) to convert noise-carrying raw trajectories into low-dimensional representation vectors for fully mining human mobility patterns. Extensive experiments conducted on two representative real-world mobility datasets demonstrate that our proposed method outperforms six state-of-the-art baselines, significantly achieving performance improvements in simulating human mobility.

This paper proposes ElastiCast, a novel Bluetooth Low Energy (BLE) broadcast mode that reduces the neighbor discovery latency in offline finding networks (OFNs). ElastiCast adapts the broadcast mode of the lost devices to the scan modes of the finder devices, considering their diversity. We start with an overview of OFNs, followed by a detailed analysis of the issues and challenges of existing solutions, which motivates the design of ElastiCast. Then we provide Blender, a simulator that models the neighbor discovery behavior of different broadcasters and scanners. By adopting Blender, ElastiCast can be implemented with three components: Local Optima Estimation, Common Interest Extraction, and Interval Multiplexing, in which we capture the key features of BLE neighbor discovery and globally optimize the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget.

To bridge the gap of conventional single-hop task offloading schemes in infrastructure-free scenarios, multi-hop task offloading schemes for IoT devices in Mobile Edge Computing (MEC) are desired to jointly optimize task offloading decisions and routing paths. In this paper, we investigate a hierarchical multi-hop edge computing framework and propose a joint Task Offloading and Relay Selection (TORS) scheme. It considers real-time computation at each relay node and employs directional searches to facilitate the task execution and results reporting at the fastest speed. However, finding the optimal TORS solution is a formidable challenge due to the time-varying network environments, the strong interdependence of decision sets across different time slots, and the high computational complexity. To address these challenges, we first leverage Lyapunov optimization to transform the stochastic TORS problem into a deterministic per-slot block problem, avoiding the need for extensive system prior knowledge. Subsequently, we propose a Soft Actor-Critic (SAC)-based algorithm, SAC-TORS, to find a satisfactory TORS solution with minimal computational complexity in a distributed manner. Accordingly, each IoT device can independently make self-determined and directional decisions with observable network information. Through extensive experiments, we demonstrate that the SAC-TORS outperforms state-of-the-art solutions, achieving performance improvements of up to 66%.

With the proliferation of cloud services and the continuous growth in enterprises’ demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management.

As one of the pillars in cluster computing frameworks, coflow scheduling algorithms can effectively shorten the network transmission time of cluster computing jobs, thus reducing the job completion times and improving the execution performance. However, most of existing coflow scheduling algorithms failed to consider the influences of concurrent flows, which can degrade their performance under a massive number of concurrent flows. To fill the gap, we propose a unified communication agent named Courier to minimize the number of concurrent flows in cluster computing applications, which is compatible with the mainstream coflow scheduling approaches. To maintain the scheduling order given by the scheduling algorithms, Courier merges multiple flows between each pair of hosts into a unified flow, and determines its order based on that of origin flows. In addition, in order to adapt to various types of topologies, Courier introduces a control mechanism to adjust the number of flows while maintaining the scheduling order. Extensive large-scale trace-driven simulations have shown that Courier is compatible with existing scheduling algorithms, and outperforms the state-of-the-art approaches by about 30% under a variety of workloads and topologies.

Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel ρ-Vision framework to perform high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades. Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained in the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on camera snapshots. Quantitative results reveal that RAW-domain task inference provides better detection accuracy and compression efficiency compared to that in the RGB domain. Furthermore, the proposed ρ-Vision generalizes across various camera sensors and different task-specific models. An added benefit of employing the ρ-Vision is the elimination of the need for ISP, leading to potential reductions in computations and processing times.

Quantitative performance analysis plays a pivotal role in theoretically investigating the performance of Vehicular Edge Computing (VEC) systems. Although considerable research efforts have been devoted to VEC performance analysis, all of the existing analytical models were designed to derive the average system performance, paying insufficient attention to the worst-case performance analysis, which hinders the practical deployment of VEC systems to support mission-critical vehicular applications, such as collision avoidance. To bridge this gap, we develop an original performance analytical model by virtue of Stochastic Network Calculus (SNC) to investigate the worst-case end-to-end performance of VEC systems. Specifically, to capture the bursty feature of task generation, an innovative bivariate Markov Chain is firstly established and rigorously analysed to derive the stochastic task envelope. Then, an effective service curve is created to investigate the severe resource competition among vehicular applications. Driven by the stochastic task envelope and effective service curve, a closed-form end-to-end analytical model is derived to obtain the latency bound for VEC systems. Extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model under different system configurations. Furthermore, we exploit the proposed analytical model as a cost-effective tool to investigate the resource allocation strategies in VEC systems.

Recently, such industrial pioneers as Apple and Samsung have offered a new generation of offline finding network (OFN) that enables crowd search for missing devices without leaking private data. Specifically, OFN leverages nearby online finder devices to conduct neighbor discovery via Bluetooth Low Energy (BLE), so as to detect the presence of offline missing devices and report an encrypted location back to the owner via the Internet. The user experience in OFN is closely related to the success ratio (possibility) of finding the lost device, where the latency of the prerequisite stage, i.e., neighbor discovery, matters. However, the crowd-sourced finder devices show diversity in scan modes due to different power modes or different manufacturers, resulting in local optima of neighbor discovery performance. In this paper, we present a brand-new broadcast mode called ElastiCast to deal with the scan mode diversity issues. ElastiCast captures the key features of BLE neighbor discovery and globally optimizes the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget.

The quantitative performance analysis plays a critical role in assessing the capability of vehicular edge computing (VEC) systems to meet the requirements of vehicular applications. However, developing accurate analytical models for VEC systems is extremely challenging due to the unique features of intelligent vehicular applications. Specifically, recent work revealed that the tasks generated by intelligent vehicular applications exhibit a high degree of burstiness, rendering the existing models that were designed based on the assumption of the non-bursty Poisson process unsuitable for VEC systems. To fill this gap, we developed an original analytical model to investigate the performance of VEC systems with bursty task arrivals. To facilitate vehicle cooperation, a new priority-based resource allocation scheme is exploited to schedule the tasks of vehicular applications, which are modelled by a Markov Modulated Poisson Process (MMPP). Next, a multi-state Markov chain is established to investigate the impact of load sharing strategy on the performance of VEC systems. Then, the end-to-end transmission latency is derived based on the proposed model. Comprehensive experiments are conducted to validate the accuracy of this analytical model under various system configurations. Furthermore, the developed model is used as a cost-effective tool to investigate the performance bottleneck of VEC systems.

With the explosive increase in mobile data traffic and stringent quality-of-experience requirements of users, mobile edge caching is a promising paradigm to reduce delivery latency and network congestions by serving content requests locally. However, it is extremely challenging to conduct cache replacement when the cache is full and the future request pattern is unknown subject to enormous content volume but limited cache capacity at the network edge. In this paper, we propose a cache replacement algorithm based on the oracle approximation named OA-Cache in an end-to-end manner to maximize the cache hit rate. Specifically, we construct a complex model that uses a temporal convolutional network to capture the long and short dependencies between content requests. Then, an attention mechanism is adopted to find out the correlations between the requests in the sliding window and cached contents. Instead of training a policy to mimic Belady that evicts the content with the longest reuse distance, we cast the learning task into a classification model to distinguish unpopular contents from popular ones. Finally, we apply the knowledge distillation approach to assist in transferring knowledge from a large pre-trained complex network to a lightweight network to readily accommodate to the network edge scenario. To validate the effectiveness of OA-Cache, we conduct extensive experiments on real-world datasets. The evaluation results demonstrate that OA-Cache can achieve the superior performance compared to candidate algorithms.

Remote Direct Memory Access (RDMA) has been widely deployed in datacenters for its high performance. Large-scale high performance cloud services built on geographically distributed datacenters require long-range RDMA for performance requirements. However, existing RDMA solutions can hardly satisfy the stringent requirements of the emerging large-scale high-performance cloud services built on geo-distributed datacenters in terms of throughput and delay. On the one hand, lossless RDMA suffers from a deep buffer and potential suboptimal throughput for inter-datacenter traffic due to delayed response to Priority Flow Control (PFC) messages. On the other hand, lossy RDMA with selective retransmissions suffers from poor performance when multiple flows with different round-trip times (RTTs) coexist in cross-datacenter scenarios. This article proposesSwing, which expands the high-performance lossless RDMA to long-distance links through PFC-Relay.Swingensures the throughput of long-distance links while minimizing the buffer requirement for long-range RDMA. It enables long-range RDMA without making any modifications to existing in-datacenter networks. The evaluation shows thatSwingcan reduce the average flow completion time (FCT) by 14%-66% in a variety of traffic scenarios.

With the advancements in Machine Learning (ML) and edge computing, increasing efforts have been devoted toedge video analytics. However, most of the existing works fail to consider the cooperation of edge nodes for ML model caching and video frame scheduling, thus less efficient in practical scenarios with diverse requirements. In this article, we propose a novel approach named ESMO (joint framEScheduling andMOdel caching) to jointly optimize Frame Scheduling and Model Caching (FSMC), aiming at enhancing the performance of edge video analytics. In detail, we decompose the FSMC as three sub-problems, where the first two sub-problems (i.e., user's transmit power and edge computing resources allocation problems) are proven to be quasi-convex and strictly convex, respectively; while the third main sub-problem (i.e., trade-off among the video analytics (VA) accuracy, service delay and energy consumption) is NP-hard. Therefore, an efficient Two-layers Genetic Algorithm based algorithm (i.e., TGA-FSMC) is designed to find the close-to-optimal frame scheduling and the model caching decisions in an iterative manner. Finally, we deploy a target recognition prototype to comprehensively evaluate the practical performance in diverse edge nodes and CNN models. Extensive experiments demonstrate the empirical superiority of the ESMO over alternatives on real-world edge video analytics platforms, and it achieves 37.5%$\sim$87.2% performance improvement.

Urban anomaly adversely affects the quality of human life and even the security of urban residents. Effective prediction of urban anomalies is crucial to public safety, traffic management, urban planning, etc. However, the region division, the complex spatial-temporal correlations, and the cross interaction of multiple dynamics make this difficult. In this paper, urban anomaly data collected by crowdsourcing is used to perform anomaly prediction together with external factors. To this end, a Spatial-Temporal Cross Interaction Networks (STCIN) model is proposed, with a novel urban graph construction method and a new mechanism to build the spatial-temporal correlation between regions with multiple dynamics. STCIN is able to exploit both current region features and historical urban information for urban anomaly prediction. Experimental results validate the superiority of the proposed method in comparison with five related works on four real-world datasets. Using the dataset of Street Condition, STCIN can increase the F1-score of anomaly prediction by 5.7% comparing with the state-of-the-art WaveNet (Graph wavenet for deep spatial-temporal graph modeling).

Recent advances in high-speed rails (HSRs), coupled with user demands for communication on the move, are propelling the need for acceptable quality of experience (QoE) in high-speed mobility environments. However, with throughput declining significantly the QoE on existing HSRs is still far from satisfactory. In order to improve QoE on HSRs, this paper seeks to answer the question regarding which is better of two options: the selection of the best cellular carrier applying single-path TCP or the conjunction of multiple carriers applying Multi-path TCP (MPTCP). To this end, we carefully design comparison experiments using the two approaches on HSRs with a peak speed of 310 km/h. Measurement study on MPTCP performance shows that generally carrier conjunction gives similar performance as carrier selection. We take an in-depth analysis of the details of the instances, and for the first time expose the phenomenon called subflow degradation. We further confirm that subflow degradation of MPTCP occurs due to its poor adaptability to frequent handoffs. We believe these insights can provide valuable guidance for the design, implementation, and deployment of transmission protocols in high-speed mobility environments.

The statistical value of latencies between two sets of hosts over a given period, which is referred as to the statistical latency, can benefit many applications in the next-generation networks, for example, Network-in-a-Box-based resource provisioning. However, the existing methods can hardly achieve low measurement cost and high prediction accuracy simultaneously in large-scale scenarios. In this article, we design a light-weight statistical latency measurement platform named DMS (DNS-based statistical latency Measurement platform at Scale). DMS achieves high measurement accuracy by introducing a metric space to select the closest open recursive DNS (Domain Name System) server to a given host, and predicting the end-to-end latency between two hosts via the measured latency between the two corresponding DNS servers. To reduce the overall measurement overhead, DMS clusters the hosts in the metric space with the open recursive DNS infrastructure in the network as the cluster center, thus achieving low measurement cost and good scalability in large scale simultaneously. To evaluate the performance of DMS, we implement a prototype system in the network. Compared to the widely adopted method King, DMS can reduce the relative error by 18.5% for real-time end-to-end latency prediction and 33% for statistical latency prediction.

As an indispensable part of modern critical infrastructures, cameras deployed at strategic places and prime junctions in an intelligent transportation system can help operators in observing traffic flow, identifying any emergency situation, or making decisions regarding road congestion without arriving on the scene. However, these cameras are usually equipped with heterogeneous and turbulent networks, making the real-time smooth playback of traffic monitoring videos with high quality a grand challenge. In this article, we propose a lightweight Deep Reinforcement Learning-based approach, namely, sRC-C (smart bitRate Control with a Continuous action space) , to enhance the quality of real-time traffic monitoring by adjusting the video bitrate adaptively. Distinguished from the existing bitrate adjusting approaches, sRC-C can overcome the bias incurred by deterministic discretization of candidate bitrates by adjusting the video bitrate with more fine-grained control from a continuous action space, thus significantly improving the Quality-of-Service (QoS). With carefully designed state space and neural network model, sRC-C can be implemented on cameras with scarce resources to support real-time live video streaming with low inference time. Extensive experiments show that sRC-C can reduce the frame loss counts and hold time by 24% and 15.5%, respectively, even with comparable bandwidth utilization. Meanwhile, compared to the-state-of-art approaches, sRC-C can improve the QoS by 30.4%.

With the rapid growth of networked multimedia services in the Internet, wireless network traffic has increased dramatically. However, the current mainstream content caching schemes do not take into account the cooperation of different edge servers, resulting in deteriorated system performance. In this paper, we propose a learning-based edge caching scheme to enable mutual cooperation among different edge servers with limited caching resources, thus effectively reducing the content delivery latency. Specifically, we formulate the cooperative content caching problem as an optimization problem, which is proven to be NP-hard. To solve this problem, we design a new learning-based cooperative caching strategy (LECS) that encompasses three key components. Firstly, a temporal convolutional network driven content popularity prediction model is developed to estimate the content popularity with high accuracy. Secondly, with the predicted content popularity, the concept of content caching value (CCV) is introduced to weigh the value of a content cached on a given edge server. Thirdly, an novel dynamic programming algorithm is developed to maximize the overall CCV. Extensive simulation results have demonstrated the superiority of our approach. Compared with the state-of-the-art caching schemes, LECS can improve the cache hit rate by 8.3%-10.1%, and reduce the average content delivery delay by 9.1%-15.1%.

Network function virtualization (NFV) is a promising paradigm that network functions can be deployed on commodity servers instead of dedicated servers to enhance the resource utilization and reduce the management difficulty. Based on the NFV technology, a complex network service can be composed of a series of ordered virtual network functions, known as service function chain (SFC). In this context, how to efficiently place SFCs in acceptable running time to improve resource utilization and service quality while meeting the constraints of the physical network is a critical issue for infrastructure providers. In this paper, we propose a deep reinforcement learning-based approach called DRL-SFCP for adaptive SFC placement. DRL-SFCP maximizes the long-term average revenue by combining both the graph convolution network which extracts the features of the physical network and sequence-to-sequence model which captures the ordered information of the SFC request to generate placement strategies. It learns to make SFC placement decisions via observations of the corresponding performance of past decisions rather than a hypothetical environment. Extensive experimental results show that our DRL-SFCP can achieve 11.6% and 9.6% improvement in terms of the acceptance ratio and the long-term average revenue, compared with existing benchmarks.

Network function virtualization (NFV) simplies the coniguration and management of security services by migrating the network security functions from dedicated hardware devices to software middle-boxes that run on commodity servers. Under the paradigm of NFV, the service function chain (SFC) consisting of a series of ordered virtual network security functions is becoming a mainstream form to carry network security services. Allocating the underlying physical network resources to the demands of SFCs under given constraints over time is known as the SFC deployment problem. It is a crucial issue for infrastructure providers. However, SFC deployment is facing new challenges in trading off between pursuing the objective of a high revenue-to-cost ratio and making decisions in an online manner. In this paper, we investigate the use of reinforcement learning to guide online deployment decisions for SFC requests and propose a Policy network Assisted Monte Carlo Tree search approach named PACT to address the above challenge, aiming to maximize the average revenue-to-cost ratio. PACT combines the strengths of the policy network, which evaluates the placement potential of physical servers, and the Monte Carlo Tree Search, which is able to tackle problems with large state spaces. Extensive experimental results demonstrate that our PACT achieves the best performance and is superior to other algorithms by up to 30% and 23.8% on average revenue-to-cost ratio and acceptance rate, respectively.

Built on the explosive advancement of cloud and telecommunication technologies, Gaming-as-a-Service (GaaS) or cloud gaming system is expected to revolutionize the traditional multi-billion video game market in the near future. This wave is analogous to the rise of live-video-streaming-based-Netflix to replace conventional DVD rental business for movies and TVs. In practice, a successful GaaS platform need to operate in a transparent mode without requiring substantial efforts from both content providers and end users, and offer the pristine quality of experience (QoE) at an affordable cost. Our analysis suggests that GaaS provisioning cost can be reduced significantly by enforcing the game video rendering and streaming at a lower resolution (so as to increase the user concurrency in the cloud and reduce the streaming bandwidth over the network). However, streaming video at a lower resolution may deteriorate the QoE. To maintain the client QoE at the level using the default-native resolution for streaming or even enhance it, we introduce the learned resolution scaling (LRS), which leverages the computational capabilities at clients/edges to restore/improve the reconstructed image/video quality via stacked deep neural networks (DNN). We integrate this LRS into a commercialized GaaS platform - AnyGame, to study its efficiency and complexity quantitatively. Extensive real-life experiments have shown that LRS-powered AnyGame offers the state-of-the-art performance, and the lower operational cost, paving the road for a potential success of GaaS over the Internet. Additionally, we dive into proposed LRS via ablation studies to further demonstrate its consistent performance, including the discussions on trade-off between efficiency and complexity, alternative training sets, etc.

The decision of request mapping-which server to handle user request and response routing-which transit route to carry response back to user has great impact on the performance and cost of Content Delivery Networks (CDNs). Request mapping and response routing are traditionally treated independently. The information invisibility and inconsistent objectives may lead to worse performance and high cost. However, the rapid globalization of Internet eXchange Points (IXPs) has facilitated the cooperation between CDN and ISP. In this paper, we consider request mapping and response routing jointly. We formulate the joint problem to navigate the performance and cost tradeoff. To solve the large-scale optimization, we develop a distributed tide algorithm based on Gauss-Seidel. The joint problem can be decomposed to sub-problems which allows for a parallel implementation. Experiment result shows that the relative error between our distributed tide algorithm that iterates within 50 rounds and theoretical optimum is about 0.7 percent. Furthermore, the parallel runtime demonstrates the efficiency of our algorithm.

Mobile video streaming systems typically apply the forward error correction (FEC) at the application layer to cope with packet-level transmission errors, which complements the bit-level correction mechanisms at the physical layer. However, most existing works fail to exploit the block-level dependencies in both intra and interframe coding modes of a single-layer compressed video, and thus are less efficient for the prevailing H.264/AVC and/or H.265/HEVC compatible single-layer video application. To this end, we propose a low-complexity FEC, i.e., context-aware RaptorQ (CA-RQ) with unequal error protection (UEP), to improve the error recovery performance of the singlelayer mobile video streaming, through incorporating the blocklevel dependencies in the compressed video data. We use a packet-level video transmission distortion model that considers the dependencies in both spatial and temporal domains, to quantify the importance of video packets within a group of pictures (GoP). The compressed video packets are categorized and grouped into several classes according to their importance to construct the CA-RQ code with the UEP property. We provide a theoretical analysis on redundancy allocation bounds to demonstrate the superior performance of proposed CA-RQ over the standard RaptorQ code. In the meantime, extensive simulations have shown that our scheme not only offers much better subjective visual quality with less than 50% additional redundant symbols as compared to the Macroblock-Based UEP (MB-UEP) scheme, but also outperforms the MB-UEP and classical equal error protection (EEP)-based schemes, by a 0.45%'5.71% and 0.94%'6.78% margin, respectively, in reconstructed quality evaluated using the structural similarity (SSIM) index, across a reasonable range of redundancy proportions.

Knowledge of node locations is essential to Wireless Sensor Networks (WSNs) in a wide range of potential applications and their function-dependent network protocols. A number of localization approaches have already been proposed to fulfill this requirement, but few of them can be applicable to mobile sensor networks, due to their low-dimensional embeddings, Euclidean distance representation limitations, frequent node mobility and additional measurement overhead in the network. In this paper, a resilient range-based d-dimensional localization (RRDL) approach is proposed for mobile WSNs to resolve the issues. RRDL distinguishes itself from previous work with three remarkable characteristics: (1) it works for mobile networks embedded in d-dimensional Non-Euclidean space; (2) it allows static ordinary nodes with pre-known locations to act as the alternative anchor nodes, thus tolerating the motion of the original anchor nodes to ensure that other ordinary nodes can obtain their locations in an efficient manner; and (3) it introduces an efficient path-learning approach, with the knowledge of the existing paths, to represent the real network distances as far as possible, thereby eliminating additional measurement overhead and tolerating node mobility in localization. With these characteristics, RRDL exploits the iterative factorization of the random distance matrix, formed by the distances to and from a set of k-hop static neighbors, to assign each current node d-dimensional Non-Euclidean coordinate in a distributed manner. Simulation results demonstrate that RRDL achieves higher localization accuracy with a moderate communication cost in mobile sensor networks.

Understanding the statistical latency between two groups of hosts in a period of time is of great significance to a wide variety of Internet applications and services, such as Service-Level Agreement (SLA) compliance monitoring and Virtual Network Function (VNF) placement. However, direct latency measurement methods are not always applicable to large-scale situations while the existing indirect methods often incur extra deployment costs or security problems. To address this challenge, we design an indirect method based on widely-distributed clients calledSSL(Surrogate-based method for large-scale Statistical Latency measurement).SSLestimates the latency between two arbitrary hosts using the measured latencies from several selected clients near one end host, which are called the host's surrogates, to the other end host. To overcome the limited capacity of the volatile clients with unstable CPU, memory, and bandwidth resources, we propose an innovative two-step measurement task assignment mechanism forSSLthat can achieve high accuracy measurement results while satisfying the resource constraints simultaneously. Moreover,SSLadopts a sampling technique to reduce the overhead in large-scale measurements, and a resampling technique to determine the confidence interval. Simulation experiments show thatSSLcan achieve more than 90 percent accuracy in most situations with 10 percent client density and 15 percent sampling rate.

As the prevalence of computing-intensive and delay-sensitive Internet of Things (IoT) applications, IoT service providers (SP) begin to deploy micro data centers in the edge and offload functions to them. However, more and more complex IoT applications require an ordered sequence of services across geographically distributed infrastructure to fulfil their functions, which poses grand challenges for IoT SP to deploy applications with low costs and high efficiency. To the best of our knowledge, no existing works have studied the deployment for an application with multilevel services (referred to as application deployment with multilevel services (ADMS) problem). To fill in the gap, we formulate the ADMS problem as an optimization problem with the aim of minimizing the overall deployment cost under the latency/computation/storage/bandwidth requirements and the infrastructure capacity limitations. We design a workflow-based heuristic algorithm called AMS, which can determine how many virtual machines (VMs) should be placed for each type of service and where to place them. AMS supports the services to scale up or scale down on demand in real time. Simulation experiments based on real network measurement demonstrate that AMS can reduce the number of deployed VMs by 28.4% and the deployment cost by 33.9% subject to comparable satisfied user ratio.

Webpage segmentation is the basic building block for a wide range of webpage analysis methods. The rapid development of Web technologies results in more dynamic and complex webpages, which bring new challenges to this area. To improve the performance of webpage segmentation, we propose a two-stage segmentation method that can combine visual, logic, and semantic features of the contents on a webpage. Specifically, we devise a new model to measure the similarities of the elements on webpages based on both visual layout and logic organization in the first stage, and we propose a novel block regrouping method using semantic statistics and visual positions in the second stage. This two-stage method can effectively conduct webpage segmentation on complicated and dynamic webpages. The performance and accuracy of the method are verified by comparing with two existing webpage segmentation methods. The experiment results show that the proposed method significantly outperforms the existing state of the art in terms of higher precision, recall, and accuracy.

Cloud gaming (CG) system could pursue both high-quality gaming experience via intensive computing, and ultimate convenience anywhere at anytime through any energy-constrained mobile devices. Despite the abundance of efforts devoted, state-of-the-art CG systems still suffer from multiple key limitations: expensive deployment cost, high bandwidth consumption and unsatisfied quality of experience (QoE). As a result, existing works are not widely adopted in reality. This paper proposes a Transparent Gaming framework called T-Gaming that allows users to play any popular high-end desktop/console games on-the-fly over the Internet. T-Gaming utilizes the off-the-shelf consumer GPUs without resorting to the expensive proprietary GPU virtualization (vGPU) technology to reduce the deployment cost. Moreover, it enables prioritized video encoding based on the human visual feature to reduce the bandwidth consumption without noticeable visual quality degradation. Last but not least, T-Gaming adopts adaptive real-time streaming based on deep reinforcement learning (RL) to improve user's QoE. To evaluate the performance of T-Gaming, we implement and test a prototype system in the real world. Compared with the existing cloud gaming systems, T-Gaming not only reduces the expense per user by 75 percent hardware cost reduction and 14.3 percent network cost reduction, but also improves the normalized average QoE by 3.6-27.9 percent.

Geographic routing has been considered as an attractive approach for resource-constrained wireless sensor networks (WSNs) since it exploits local location information instead of global topology information to route data. However, this routing approach often suffers from the routing hole (i.e., an area free of nodes in the direction closer to destination) in various environments such as buildings and obstacles during data delivery, resulting in route failure. Currently, existing geographic routing protocols tend to walk along only one side of the routing holes to recover the route, thus achieving suboptimal network performance such as longer delivery delay and lower delivery ratio. Furthermore, these protocols cannot guarantee that all packets are delivered in an energy-efficient manner once encountering routing holes. In this paper, we focus on addressing these issues and propose an energy-aware dual-path geographic routing (EDGR) protocol for better route recovery from routing holes. EDGR adaptively utilizes the location information, residual energy, and the characteristics of energy consumption to make routing decisions, and dynamically exploits two node-disjoint anchor lists, passing through two sides of the routing holes, to shift routing path for load balance. Moreover, we extend EDGR into threedimensional (3D) sensor networks to provide energy-aware routing for routing hole detour. Simulation results demonstrate that EDGR exhibits higher energy efficiency, and has moderate performance improvements on network lifetime, packet delivery ratio, and delivery delay, compared to other geographic routing protocols in WSNs over a variety of communication scenarios passing through routing holes. The proposed EDGR is much applicable to resource-constrained WSNs with routing holes.

Although mobile application ecosystems have experienced tremendous growth in recent years, retrieving content of mobile applications that serves a key to mobile content search engines still faces grand challenges. Compared to web content retrieval, it is much more difficult to capture content in mobile applications due to the diversity of applications and the lack of Uniform Resource Locator indices. In this study, we propose and implement a user interaction-driven mobile content retrieval (UMCR) system to address such issues, which is the first mobile content crawler in the current literature. UMCR is a distributed system that contains many measurement nodes, each of which combines the user interaction path traversing (UIPT) and Deep Package Inspection (DPI) together to obtain mobile content. UIPT determines the events of user interactions in various applications to capture the static content such as text and images, in which a traversal depth termination scheme and an optional cut-off component are adopted to balance the content coverage and traversing efficiency. Meanwhile, the analysis based on DPI is responsible for extracting the videos as well as digging the infrastructural information and performance metrics. In addition, a distributed traversal scheduling method is designed for UIPT tasks to improve the throughput and scalability in large-scale content retrieval. Experiments on retrieving content of 64 real mobile applications demonstrate that UMCR can handle diverse mobile applications efficiently. The scheduler can improve throughput by 3 times compared to the legacy arbitrary task assignment strategy.

Today's content delivery is characterized by key trends such as converged media delivery over HTTP, increasing volumes of multimedia content delivered over IP, and elevated user expectations on quality-of-experience. In this respect, server provisioning is a critical phase of CDN management, which affects both incumbent and entrant CDN operators as well as internet service providers. However, existing tools and approaches to solve server placement problems have serious shortcomings: they offer only coarse tuning knobs and limit servers to a set of candidate sites givena priori. Our conversations with CDN operators reveal that a new provisioning mechanism is necessary to take advantage of emerging opportunities such as faster speed to roll out new locations and more access networks. In this paper, we present the design of DISC, a decision support system to help CDN operators systematically investigate different design tradeoffs and evaluate what-if scenarios. The key enabler underlying DISC is a network coordinate-based data analysis workflow that can flexibly embed different cost, performance, and workload characteristics without sacrificing the fidelity. We describe practical use cases and experiences in applying DISC to a large country-wide deployment. The results show that DISC significantly reduces average latency, deployment cost, and interdomain traffic.

We present$\sf {Tentacle}$, a decision support framework to provision edge servers for online services providers (OSPs).$\sf {Tentacle}$takes advantage of the increasingly flexible edge server placement, which is enabled by new technologies such as edge computing platforms, cloudlets and network function virtualization, to optimize the overall performance and cost of edge infrastructures. The key difference between$\sf {Tentacle}$and traditional server placement approaches lies on that$\sf {Tentacle}$can discover proper unforeseen edge locations which significantly improve the efficiency and reduce the cost of edge provisioning. We show how$\sf {Tentacle}$effectively identifies promising edge locations which are close to a collection of users merely with inaccurate network distance estimation methods, e.g., geographic coordinate (GC) and network coordinate systems (NC). We also show how$\sf {Tentacle}$comprehensively considers various pragmatic concerns in edge provisioning, such as traffic limits by law or ISP policy, edge site deployment and resource usage cost, over-provisioning for fault tolerance, etc., with a simple optimization model. We simulate$\sf {Tentacle}$using real network data at global and county-wide scales. Measurement-driven simulations show that with a given cost budget$\sf {Tentacle}$can improve user performance by around 10-45 percent at global scale networks and 15-35 percent at a country-wide scale network.

Effective media server placement strategies are critical for the quality and cost of multimedia services. Existing studies have primarily focused on optimization-based algorithms to select server locations from a small pool of candidates based on the entire topological information and thus these algorithms are not scalable due to unavailability of the small pool of candidates and low-efficiency of gathering the topological information in large-scale networks. To overcome this limitation, a novel scalable framework called NetClust is proposed in this paper. NetClust takes advantage of the latest network coordinate technique to reduce the workloads when obtaining the global network information for server placement, adopts a new$K$-means-clustering-based algorithm to select server locations and identify the optimal matching between clients and servers. The key contribution of this paper is that the proposed framework optimizes the trade-off between the service delay performance and the deployment cost under the constraints of client location distribution and the computing/storage/bandwidth capacity of each server simultaneously. To evaluate the performance of the proposed framework, a prototype system is developed and deployed in a real-world large-scale Internet. Experimental results demonstrate that 1) NetClust achieves the lower deployment cost and lower delay compared to the traditional server selection method; and 2) NetClust offers a practical and feasible solution for multimedia service providers.

For P2P-VoD streaming, an effective lookup algorithm for appropriate data suppliers is required to support the user's operation of random jump on the video. Existing lookup algorithms mainly adopt a centralized, flooding based, or DHT-based method. Facing the highly dynamic Internet environments, the centralized method incurs a single point of failure, the flooding-based method lacks scalability, and the DHT-based method is not resilient. Motivated by these problems, we propose a novel lookup algorithm, named "RELookup", which places peers on a resilient super node-based overlay and meanwhile utilizes the play point distance to efficiently locate candidate data suppliers. Besides, deliberate measures (i.e., special design of message format and node state) have been taken to reduce the coordination costs between super nodes to very little. Results of trace-driven simulations confirm the effectiveness of our proposed RELookup algorithm.