关键词： Satellites   Low earth orbit satellites   Delays   Orbits   Internet of Things   Approximation algorithms   Low latency communication   Approximation and online algorithms   mobile edge computing   network function chaining   satellite edge network  

摘要： Satellite-terrestrial networks are emerging as the next-generation networking paradigm for Beyond-5 G (B5G) and 6 G networks. Meanwhile, Mobile Edge Computing (MEC) is envisioned as the key technology to provide network services within the proximity of users, by deploying computing resource in ground locations that are close to users. With the fast deployment of Low-Earth-Oribt (LEO) satellites, a new paradigm of MEC is emerging by enabling LEO satellites serving as edge servers in lower orbits that are close to ground users. In this way, the ground users can be further served by LEO satellites in lower orbits instead of conventional high-orbit satellites. Also, since LEO satellites provide shorter paths from users to services, the performance is enhanced compared with ground MEC networks. In this paper, we aim to enable low-latency network services in a Satellite Edge Computing (SEC) network that integrates the MEC and satellite-terrestrial networks. In particular, we consider that each network service is composed of a sequence of Virtualized Network Functions (VNFs), where the traffic of user requests has to be processed by the VNFs in a service chain in the specified order before reaching its destination. To this end, we first formulate a delay-aware service chaining problem in an SEC network to minimize the average delay of implementing a user request, by jointly placing VNFs to LEO satellites in the SEC network and routing the traffic of each user request from its source to destination. We then devise an approximation algorithm with an approximation ratio for the problem in an SEC network with a single user request, by devising a novel concept of chaining orbit and auxiliary graph construction technique. We also design an online algorithm for the online delay-aware service chaining problem in an SEC network, if user requests arrive into the system without the knowledge of their arrivals and the network delays are uncertain. We finally evaluate the performance o

关键词： Servers   Interference   Partitioning algorithms   Delays   Quality of service   Throughput   Simulation   Mobile edge computing   multiple choice-upper confidence bound   graph partition   edge server placement   interference management  

摘要： The deployment of edge servers make a significant impact on the service quality of a Mobile Edge Computing (MEC) system. This service quality relies on solving two key sub-problems: 1) interference management between servers 2) the placement of MEC servers. To improve the Quality of Service (QoS), we propose a method based on Graph Partition (GP) and Upper Confidence Bound (UCB) for solving these two sub-problems. Regarding interference management, we use an undirected graph to represent the interference between MEC servers so that the overall graph can be divided into multiple subsets of non-interfering MEC servers. Regarding server placement, we propose a Multiple Choice-Upper Confidence Bound (MC-UCB) algorithm that place an collection of interference aware edge servers in each selection. To evaluate the performance, we define a user's QoS function based on transmission delay, throughput, and user density comprehensively and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) from previous work. The simulation results show that the performance of the proposed algorithms is improved by more than 4% compared with the GA algorithm and 6% compared with the PSO algorithm.

关键词： Mobile edge computing   Reconfigurable intelligent surface   Task priority   Deep reinforcement learning  

摘要： In this work, we investigate a reconfigurable intelligent surface (RIS) assisted mobile edge computing (MEC) network, where multiple users offload tasks to edge servers (ES) via RIS for accelerating computation. However, in practical MEC networks, computational tasks often exhibit diverse priorities, resulting in varying degrees of computational utility. This variability poses significant challenges to system optimization. In response to this challenge, we propose a deep reinforcement learning (DRL)-based approach to improve the performance of the RIS-assisted MEC network, where the task priority and resource allocations are jointly considered. Specifically, we exploit functions to evaluate the computational benefit of different tasks. Then, we devise a joint resource allocation scheme, which jointly considers RISs' phase shifts, user-RIS allocation, and task offloading, to maximize the system utility. To solve the problem under the complicated environment of fading channels and various task priorities, we employ the DRL approach to obtain effective resource allocation strategies to improve the system performance. Simulations are finally conducted to validate the effectiveness and superiority of the proposed schemes in this work.

关键词： Routing   Process control   Distributed databases   Cloud computing   Optimization   Metaverse   Data models   Data-intensive services   virtual reality   augmented reality   metaverse   distributed cloud   mobile edge computing   fog computing   caching   network control   stream processing  

摘要： Data-intensive augmented information (AgI) services (e.g., metaverse applications such as virtual/augmented reality), designed to deliver highly interactive experiences resulting from the real-time combination of live data-streams and pre-stored digital content, are accelerating the need for distributed compute platforms with unprecedented storage, computation, and communication requirements. To this end, the integrated evolution of next-generation networks (5G/6G) and distributed cloud technologies (mobile/edge/cloud computing) have emerged as a promising paradigm to address the interaction- and resource-intensive nature of data-intensive AgI services. In this paper, we focus on the design of control policies for the joint orchestration of compute, caching, and communication (3C) resources in next-generation 3C networks for the delivery of data-intensive AgI services. We design the first throughput-optimal control policy that coordinates joint decisions around (i) routing paths and processing locations for live data streams, with (ii) cache selection and distribution paths for associated data objects. We then extend the proposed solution to include a max-throughput data placement policy and two efficient replacement policies. Numerical results demonstrate the superior performance obtained via the novel multi-pipeline flow control and 3C resource orchestration mechanisms of the proposed policy, compared with state-of-the-art algorithms that lack full 3C integrated control.

关键词： Deep reinforcement learning   Mobile edge computing   Internet of vehicles   Task offloading   Asynchronous advantage actor-critic  

摘要： Adopting Internet of Vehicles (IoV) technology has led to many new uses to improve traffic control, safety, and entertainment services. Still, the increasing amount of information these applications produce poses significant obstacles regarding response time, power usage, and other related issues. To enhance the functionality of IoV systems, this paper introduces a new way that utilizes Deep Reinforcement Learning (DRL) and Mobile Edge Computing (MEC) to improve its performance. Specifically, our DRL framework employs a convolutional neural network-based Asynchronous Advantage Actor-Critic (A3C) algorithm, chosen for its efficacy in processing spatial data relevant to IoV systems such as vehicle locations and speeds. The optimization problem considers the vehicle's location and speed, the MEC server's resources, and the IoV application's requirements by formulating it as a Markov Decision Process (MDP). Utilizing the A3C approach, our Deep Neural Network (DNN) method infers an optimal offloading policy. We optimized the proposed algorithm with strategies that include adaptive learning rate, gradient clipping, entropy regularization, and generalized advantage estimation. The optimized algorithm considers factors such as distance, bandwidth, and communication requirements to provide efficient task-offloading solutions, leading to better system utility and performance. The proposed strategy outperforms comparable models through comprehensive simulations, providing an average enhancement of 20.50% in energy consumption, 15.86% in latency, and 11.94% in execution time, emphasizing the effectiveness and superiority of the suggested algorithm in dealing with various workloads while reducing energy consumption, latency, and execution times.

关键词： Resource management   Task analysis   Satellite broadcasting   Servers   Satellites   Optimization   Low earth orbit satellites   Satellite access networks   intelligent transportation systems   mobile edge computing   cooperative multi-agent proximal policy optimization   attention mechanism   deep reinforcement learning  

摘要： The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation services based on SANs. We use low-Earth orbit (LEO) and cube satellites (CubeSats) as independent mobile edge computing (MEC) servers that schedule the processing of data generated by ITS nodes. To optimize offloading task selection, computing, and bandwidth resource allocation for different satellite servers, we formulate a joint delay and rental price minimization problem that is mixed-integer non-linear programming (MINLP) and NP-hard. We propose a cooperative multi-agent proximal policy optimization (Co-MAPPO) deep reinforcement learning (DRL) approach with an attention mechanism to deal with intelligent offloading decisions. We also decompose the remaining subproblem into three independent subproblems for resource allocation and use convex optimization techniques to obtain their optimal closed-form analytical solutions. We conduct extensive simulations and compare our proposed approach to baselines, resulting in performance improvements of 9.9%, 5.2%, and 4.2%, respectively.

关键词： mobile edge computing   vehicular edge computing   computation offloading   deep reinforcement learning   partial offloading  

摘要： Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs' processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs' demanding tasks and MEC servers' limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further affecting VTs' QoS. To address these challenges, this paper proposes a vehicle collaborative partial computation offloading model. This model allows VTs to offload tasks to two types of service nodes: collaborative vehicles and MEC servers. Factors like a vehicle's mobility, remaining battery power, and available computational power are also considered when evaluating its suitability for collaborative offloading. Furthermore, we design a deep reinforcement learning-based strategy for collaborative partial computation offloading that minimizes overall task delay while meeting individual latency constraints. Experimental results demonstrate that compared to traditional approaches without vehicle collaboration, this scheme significantly reduces latency and achieves a significant reduction (around 2%) in the failure rate under tighter latency constraints.

关键词： Servers   Mobile applications   Mobile computing   Fans   Wireless communication   Urban areas   Predictive models   Mobile edge computing   predictive service provisioning   spatio-temporal data mining   submodular optimization  

摘要： Mobile Edge Computing (MEC) technology can be implemented at cellular base stations, enabling flexible and configurable provisions of services for mobile users to access. Nevertheless, the conventional solutions mainly focus on statical service provisioning, which ignores the dynamic nature of the arriving service requests. In this work, we first conduct comprehensive data-driven observations on over 4 million service requests throughout 9,800 base stations. Our key findings suggest that users' demands intrinsically exhibit spatial and temporal patterns, which inevitably lead to performance degradation in statical service provisioning. Motivated by that, we design and implement MobiEdge, a predictive service provisioning system with online learning in wireless edge networks. We propose a graph embedding learning-based model for representation learning, thus to achieve accurate request prediction at different base stations. Then, based on the prediction of incoming service requests, we study the service provisioning reconfiguration problem, i.e., how to jointly optimize service placement and corresponding request scheduling across dual timescales, under constraints of network resources and the total budget. By leveraging the submodular technique, we transform the research issue into a submodular function maximization problem under the q-independence system constraint, where q is a positive constant related to the ratio of coefficients in constraint conditions. On this basis, we propose a 1/(1+q)approximation algorithm with rigorous theoretical analysis on the bounded maximum utility. Extensive trace-driven evaluations are conducted over networks of different scales, and MobiEdge shows remarkable performance enhancements by achieving the accuracy of up to 98% in service prediction and an average utility of 92.9% to the optimal solution in service provisioning.

关键词： Computation offloading   deep reinforcement learning   environment adaptation   mobile edge computing  

摘要： One of the key issues in mobile edge computing (MEC) is computation offloading, most policies of which are developed based on mathematical programming (MP). Due to the high computational complexity of iterative programming in MP-based policies, recent years have seen a popular trend to develop offloading policies based on deep reinforcement learning (DRL). However, on account of the poor generalization ability of DRL models in MEC environments with different network sizes and settings, it is difficult to directly apply DRL-based offloading policies in unseen MEC environments. Motivated by this, we propose a DRL-based environment-adaptive offloading framework (DEAT), including a size-adaptive scheme (SIED) and setting-adaptive component (SEAL). SIED leverages the idea of 'time division multiplexing' to adapt to varying MEC network sizes and order-unaware feature extraction to mitigate impacts of different size-changing orders. SEAL adopts system dynamics embedding and offloading policy embedding, which guide the finding of the closest pre-training MEC environment and offloading policy, respectively, to achieve fast setting-adaptation with only few exploring interactions in unseen MEC environments. Extensive experiments are conducted via both simulation and testbed to demonstrate the adaptation performance advantages of DEAT in unseen MEC environments compared to the state-of-the-art offloading approaches.

关键词： Servers   Inspection   Data integrity   Quality of service   Distributed databases   Costs   Time complexity   Mobile edge computing   edge data integrity   smart contract   corruption localization   inspection algorithm  

摘要： The emerging Mobile Edge Computing (MEC) paradigm reforms the way of data caching by motivating App vendors to store latency-sensitive data on distributed edge servers. In volatile MEC environments, ensuring Edge Data Integrity (EDI) is a major concern for App vendors. Existing EDI solutions only consider the scenario with a single App vendor and multiple edge servers, neglecting more complex multi-vendor and multi-server cases. If multiple App vendors check their data replicas cached on the same edge server simultaneously, integrity verification efficiency will drop exponentially. To mitigate this challenge, we make the first attempt to develop a Smart Inspection Algorithm (SIA) to pre-select unreliable data replicas for different App vendors in each verification round by jointly considering cache services' QoS (Quality-of-Service) and data replicas' unverified time. By implementing this approach, edge servers can merely verify the selected data replicas, greatly reducing computation and communication overheads in EDI verification. Theoretically, SIA can achieve O(n) expected time complexity. Supported by SIA, we expand the EDI problem in multi-vendor and multi-server MEC environments (referred to as the MVMS-EDI problem) and propose a smart contract-based approach entitled MVMS-SC to tackle the problem efficiently and impartially. We provide a rigorous theoretical analysis of the correctness, security, and efficiency of MVMS-SC. Both large-scale and small-scale experiments with real-world datasets are correspondingly performed on a single machine and a real platform to validate the superiority of MVMS-SC in terms of computation and communication efficiencies.