关键词： Path planning   Cloud computing   5G mobile communication   Quality of service   Task analysis   Resource management   Marine vehicles   Mechanism design   mobile edge computing   path planning   pricing   quality of service   service handoff  

摘要： Many mobile device applications require low end-to-end latency to edge computing infrastructure when offloading their computation tasks in order to achieve real-time perception and cognition for users. User mobility brings significant challenges in providing low-latency offloading due to the limited coverage area of cloudlets. Virtual machine (VM)/container handoff is a promising solution to seamlessly transfer services from one cloudlet to another to maintain low latency as users move. However, an inefficient path planning for the handoff can result in system congestion and consequently poor Quality of Service (QoS). The situation can even worsen by selfish users who intentionally lie about their true parameters to achieve better service at the cost of degrading the whole system's performance. To fill this research gap, we propose an Online Service Handoff Mechanism (OSHM) to provide an efficient path dynamically for transferring VM/container from the current serving cloudlet to a nearby cloudlet at the destination of a mobile user. Our proposed path planning algorithm is based on a label correction methodology, leading to polynomial time complexity. OSHM is accompanied by our proposed payment determination function to discourage misreporting of unknown parameters. We discuss the theoretical properties of our proposed mechanism in implementing a system equilibrium and ensuring truthfulness. We also perform a comprehensive assessment through extensive experiments which show the efficiency of OSHM in terms of workload, handoff time, consumed energy, and other metrics compared to several benchmarks. Experimental results show that OSHM outperforms other algorithms, reducing at least 61% in average workload, 33% in average handoff time, and 29% in average energy consumption.

关键词： Rendering (computer graphics)   Delays   Quality of experience   Wireless communication   Optimization   Streaming media   Real-time systems   FoV prediction   mobile edge computing   proximal policy optimization   rendered tile reuse   virtual reality  

摘要： Interactive real-time wireless Virtual Reality (VR) has become increasingly popular because it provides an immersive experience for VR users anytime, anywhere. Introducing multi-access edge computing (MEC) to wireless VR service can solve the problem that VR devices having insufficient rendering power. However, the problem that some tiles in the overlapping field of views (FoVs) may be rendered repeatedly usually be ignored. Repeated rendering would waste the computing resources of edge nodes and damage users' quality of experience (QoE). This paper proposes a rendered tile reuse scheme based on FoV prediction and 3C (caching, computing, and communication) optimization for the MEC-assisted VR service. Firstly, we model the above scheme as an optimization problem that aims to maximize the total users' QoE value under the motion to photons (MTP) delay constraints. Secondly, we use the recurrent neural network model with gated recurrent unit (GRU) architecture to dynamically predict the users' FoV in the next time slot. Thirdly, we use the proximal policy optimization (PPO)to learn the question's solution iteratively based on the results of FoV prediction. The simulation results show that our proposed algorithm is superior to other algorithms in improving the value of total users' QoE and reducing the MTP delay.

关键词： Mobile edge computing   Offloading   Scheduling   Hybrid optimization   Q -learning  

摘要： Mobile Edge Computing (MEC) has arisen as a promising computing paradigm consisting of three tiers: Smart Mobile Devices (SMDs), fog nodes, and the cloud. The MEC enables computational offloading and execution schedules to cope with the problems of insufficient resources for the SMDs and the computational tasks' dead-lines. The offloading problem determines in what order and source of the network the tasks should be performed to minimize execution time and power consumption. The main aim of the current paper is to reduce execution time and energy consumption by optimizing tasks' offloading and scheduling in MEC networks. As a result, the task scheduling and offloading are modeled as an optimization problem. Then, an enhanced hybridization of Artificial Ecosystem-based Optimization (AEO) and Arithmetic Optimization Algorithm (AOA), named E-AEO-AOA, is presented to optimize it. In the E-AEO-AOA, the AOA and AEO algorithms are initially discretized. Next, the Q-learning strategy is modified and recruited to hybridize the algorithms in a complementary manner. Subsequently, chaos theory is utilized in a local search procedure to enhance the exploitation capability of the E-AEO-AOA. Eventually, the performance of E-AEO-AOA is examined on fifteen MEC networks. In the experiments, the E-AEO-AOA is compared with AEO, AO, AOA, JS, MRFO, STOA, SCA, and TSA algorithms statistically. Be-sides, the algorithms' convergence rate and solutions dispersity are visually compared. Moreover, the algorithms are compared by the Wilcoxon signed-rank test. The experimental results indicate that the E-AEO-AOA surpassed competitor algorithms in 90% of cases. Likewise, in 6% of the cases, the E-AEO-AOA produced the same results as AEO, AOA and MRFO.

关键词： Satellites   Task analysis   Servers   Delays   Low earth orbit satellites   Computer architecture   Computational modeling   Aerial computing   computation offloading   deep reinforcement learning   mobile edge computing   multi-agent system  

摘要： LEO satellite networks have become a necessary supplement to terrestrial networks aiming to provide worldwide, ubiquitous connectivity, especially in complicated areas (e.g., mountains, oceans, and disaster areas) where terrestrial network infrastructures are typically sparingly distributed or unavailable. However, the increasing computation-intensive Internet-of-Things (IoT) applications (e.g., real-time remote monitoring, intelligent transportation) require not only efficient and reliable communication but also massive computing capabilities. Constrained by the battery and computing resources, the computing tasks and data of applications have to be transmitted to remote cloud servers. This bandwidth limitation and high transmission delay in LEO networks will reduce the quality-of-service (QoS) of IoT applications. Recently, the combination of LEO networks and edge computing (i.e., Satellite Mobile Edge Computing, SMEC) offers significant opportunities to address these problems. The IoT devices can directly get the computing resources directly from satellites rather than remote servers, thus avoiding long-distance transmission. Considering the resource constraints on satellites, offloading policy plays a crucial role in whole system performance. In this paper, we design a hybrid offloading architecture, which applies a centralized training and distributed execution framework. Also, we propose a multi-agent actor-critic reinforcement learning algorithm, where a centralized "critic" is augmented with the global network state to ease the training procedure of distributed user equipments (UE) by evaluating the benefits of their decisions, while the UEs can adjust their policies according to the critic's evaluation and choose their own decisions relying on their observations.

关键词： Offloading optimization   Mobile Edge Computing   Swarm intelligence   Binary cuckoo search   Task offloading  

摘要： Computational task offloading facilitates real-time applications on constrained mobile devices that require a large amount of processing resources, high storage capacity and battery power. Mobile Edge Computing (MEC) is a computing paradigm that shifts computing resources closer to the user at the network's edges. Heavy tasks are then offloaded to edge nodes, thereby reducing the computations required on the mobile side. However, offloading computational tasks may result in additional energy consumption and delays, due to network congestion and time spent in server queues. Thus, to minimize completion time and energy consumption, it is essential to optimize offloading decisions, while also considering the financial costs. In this paper, we propose an Offloading Decision-Making Framework based on the Binary Cuckoo Search Algorithm for Mobile Edge Computing (ODM-BCSA). We formulated an offloading problem as a mixed-integer optimization problem to minimize time, energy, and payment costs. We resolved the problem of resource allocation using the Binary Cuckoo Search Algorithm (BCSA). The simulation results revealed that offloading decisions depend on multiple parameters, including the number of mobile devices being handled by the edge server, the bandwidth, and the number of tasks. Decisions made also depended on the priority assigned to each objective. Finally, we compared the ODM-BCSA against a brute force search, proving that the ODM-BCSA is more efficient and greatly minimizes execution time when a huge number of mobile devices are involved (by 99.9 %).

关键词： Energy consumption   Satellites   Costs   Simulation   Precoding   Transforms   Resource management   Lyapunov optimization   mobile edge computing   MIMO   resource allocation   dense satellite  

摘要： A dense satellite-terrestrial integrated mobile-edge computing network (SATIMECN) architecture is developed to meet the computing demands for next generation networks. We formulate an average weighted sum energy consumption minimization problem by jointly considering task ratio allocation of computing or offloading at local and the gateway (GW), ground user terminal (GUT)-satellite association relation, GUT multiple-input and multiple-output (MIMO) precoding, and computation resource allocation at local and the GW. Due to the stochastic property of the optimization problem, we adopt Lyapunov optimization theory to transform it into a deterministic one. Then, we decompose the optimization problem into four subproblems and solve each one iteratively. Specifically, task ratio allocation of computing or offloading at local and the GW is obtained in a closed-form expression using the delay constraint. Then, the binary GUT-satellite association subproblem is solved by the weighted minimum mean-squared error and quadratic transform based fractional programming (QTFP) methods. Moreover, the MIMO precoding subproblem is solved by QTFP and interior point methods. Finally, the computation resource allocation subproblem for local and edge computing is derived in closed-form expressions. Simulation results demonstrate that the tradeoff between the average weighted sum energy consumption and the average queue length can be realized by adjusting the Lyapunov control parameter. Moreover, the proposed MIMO communication and frequency reuse schemes for dense satellite network can realize efficient computation offloading with relative low cost.

关键词： Servers   Memory   Cloud computing   Redundancy   Indexes   Multi-access edge computing   Low latency communication   Data deduplication   edge storage system   mobile edge computing   optimization problem   storage resource balance  

摘要： In the mobile edge computing (MEC) environment, edge servers with storage and computing resources are deployed at base stations within users' geographic proximity to extend the capabilities of cloud computing to the network edge. Edge storage system (ESS), is comprised by connected edge servers in a specific area, which ensures low-latency services for users. However, high data storage overheads incurred by edge servers' limited storage capacities is a key challenge in ensuring the performance of applications deployed on an ESS. Data deduplication, as a classic data reduction technology, has been widely applied in cloud storage systems. It also offers a promising solution to reducing data redundancy in ESSs. However, the unique characteristics of MEC, such as edge servers' geographic distribution and coverage, render cloud data deduplication mechanisms obsolete. In addition, data distribution must be balanced over edge storage systems to accommodate future data demands, which cannot be undermined by data deduplication. Thus, balanced edge data deduplication (BEDD) must consider deduplication ratio, data storage benefits, and resource balance systematically under the latency constraint. In this article, we model the novel BEDD problem formally and prove its NP-hardness. Then, we propose an optimal approach for solving the BEDD problem exactly in small-scale scenarios and a sub-optimal approach to solve large-scale BEDD problems with a theoretical performance guarantee. Extensive and comprehensive experiments conducted on a real-world dataset demonstrate the significant performance improvements of our approaches against four representative approaches.

关键词： Federated learning   Mobile edge computing   Fairness   Reliable  

摘要： Federated learning-enabled mobile edge computing implements privacy-preserving collaborative machine learning of complex models. However, mobile end devices have high mobility and are vulnerable to be corrupted due to exposuring to an open network environment, which not only affect the performance of federated learning models but also bring serious security issues. To solve the above problem, we propose a reliable and fair federated learning mechanism for mobile edge computing. To begin with, in order to select end nodes as reliable as possible, a reputation-based end nodes selection scheme is proposed, which includes the construction of the reputation model and the concealment of selected end nodes. Then, corresponding counter strategies are proposed for the possible attack behaviors launched by corrupted end nodes during the training process. To avoid the impact of poisoning attacks on the model performance, a new global model aggregation strategy is proposed to maintain the model performance by meritocratic campaigning. To solve the fairness problem caused by end nodes' escape, a reward-penalty scheme using blockchain is proposed. Finally, the numerical results clearly show that the proposed mechanism is effective for reliable and fair federated learning for mobile edge computing, maintaining high accuracy under attacks from malicious end nodes while penalizing them to ensure fairness.

关键词： Mobile edge computing   Offloading decision   Response time   Energy consumption   Load balance   Multi-objective optimization immune algorithm  

摘要： In the mobile edge computing scenario, a large number of computing tasks are offloaded to the edge computing server, which may cause the tasks not to be processed on time. In this paper, the response time of the computing tasks, the energy consumption of the mobile terminal device and the load balance of the server were regarded as three optimization objectives, a multi-objective optimization model were set up, and an offloading decision scheme based on multi-objective optimization immune algorithm was proposed. A large number of comparative experiments are done to verify the effectiveness of proposed scheme. Experimental results show that the proposed scheme can make the whole server system achieve a better load balancing state while meeting the requirements of response time and energy consumption.

关键词： Computation offloading   Dynamic multi-objective optimization   Mobile edge computing   Multi-objective evolutionary algorithm   (MOEA)  

摘要： For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory -based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms.