关键词： Clustered scheduling   communication pipelining   federated learning   mobile edge computing  

摘要： This article proposes using communication pipelining to enhance the convergence speed of federated learning in mobile edge computing applications. Due to limited wireless subchannels, a subset of the total clients is scheduled in each iteration of federated learning algorithms. On the other hand, the scheduled clients wait for the slowest client to finish its computation. We propose to first cluster the clients based on the time they need per iteration to compute the local gradients of the federated learning model. Then, we schedule a mixture of clients from all clusters to send their local updates in a pipelined manner. In this way, instead of just waiting for the slower clients to finish their computations, more clients can participate in each iteration. While the time duration of a single iteration does not change, the proposed method can significantly reduce the number of required iterations to achieve a target accuracy. We provide a generic formulation for optimal client clustering under different settings, and we analytically derive an efficient algorithm for obtaining the optimal solution. We also provide numerical results to demonstrate the gains of the proposed method for different data sets and deep learning architectures.

关键词： Mobile edge computing   edge caching   task offloading   particle swarm optimization   genetic algorithm  

摘要： Computing-intensive and latency-sensitive user requests pose significant challenges to traditional cloud *** response to these challenges,mobile edge computing(MEC)has emerged as a new paradigm that extends the computational,caching,and communication capabilities of cloud *** caching certain services on edge nodes,computational support can be provided for requests that are offloaded to the ***,previous studies on task offloading have generally not considered the impact of caching mechanisms and the cache space occupied by *** oversight can lead to problems,such as high delays in task executions and invalidation of offloading *** optimize task response time and ensure the availability of task offloading decisions,we investigate a task offloading method that considers caching ***,we incorporate the cache information of MEC into the model of task offloading and reduce the task offloading problem as a mixed integer nonlinear programming(MINLP)***,we propose an integer particle swarm optimization and improved genetic algorithm(IPSO_IGA)to solve the ***_IGA exploits the evolutionary framework of particle swarm *** it uses a crossover operator to update the positions of particles and an improved mutation operator to maintain the diversity of ***,extensive simulation experiments are conducted to evaluate the performance of the proposed *** experimental results demonstrate that IPSO_IGA can save 20%to 82%of the task completion time,compared with state-of-theart and classical ***,IPSO_IGA is suitable for scenarios with complex network structures and computing-intensive tasks.

关键词： Mobile edge computing   Optimal stopping theory   Deep Q network   Task offloading   Sequential decision making  

摘要： Offloading tasks and data is an important use case for Mobile Edge Computing (MEC) that benefits a wide variety of mobile applications on different platforms, including autonomous vehicles and smartphones. A moving vehicle may pass through multiple Roadside Units (RSUs) and the MEC servers located within them as it travels. On-board applications generate structured tasks and can offload them to observable servers. However, since the mobile node cannot determine the information of the global server, choosing the best server becomes a key issue. In this paper, we address the problem of the offloading threshold in traditional Optimal Stopping Theory (OST) based decision models that cannot adapt to the environment. We use Deep Q-Network (DQN) to provide the model with the optimal offloading threshold for adaptive environments and propose a DQN-based Adaptive Threshold Multi-task Offloading approach (DQN-ATMOO). This method uses the principle of OST to solve the offloading order decision problem, divides structured tasks into sub-tasks, and executes offloading in sequence, with the goal of maximizing the probability of offloading to the best server and minimizing the total offloading delay. We experimentally compare and evaluate the proposed model using simulated and real datasets. The results show that the proposed model can be effectively implemented on mobile nodes and significantly reduces the total expected processing time.

关键词： Mobile edge computing   Cooperative computation offloading   Genetic algorithm   Data compression  

摘要： Cooperative computation offloading (CCO) is a technique to improve computation offloading performance in edge networks through collaboration between edge nodes. CCO can achieve better resource utilization, balance the computational load and further reduce delay to improve the service experience of user equipment (UE). In this paper, we investigate the problem of collaborative computing task offloading scheme and computing resource allocation in mobile edge computing and propose a data compression cooperation computing offloading (DCCO) scheme. To reduce the amount of data transmitted on the UE offloading link, we introduce a Data Compression into CCO and present the computational offloading strategy, collaborative offloading and computational resource allocation problems with the goal of minimizing the weighted sum of delay and energy consumption of the UE under the constraints of UE delay and energy consumption. And an improved genetic algorithm is proposed to solve the problem which is a non-convex mixed-integer problem with binary and continuous variables. The offloading strategy and computational resource allocation correspond to the genes in the genetic algorithm chromosome. The simulation results show that the DCCO scheme can reduce the offloading cost up to 11% compared with the existing schemes. It effectively improves the computation offloading performance of edge network computing.

关键词： Mobile edge computing   Collaborative decision-making   Deep reinforcement learning   Multi-agent deep deterministic policy gradient  

摘要： As is widely accepted, mobile edge computing (MEC) is a promising technology to enable wireless devices (WDs) to process computation-intensive tasks. Due to the mutual influence among different WDs, collaborative task offloading is needed in multi-agent environments. In this paper, a multiagent MEC network with delay-sensitive and non-partitionable tasks is considered, as well as the load on MEC servers. The joint optimization problem of offloading decision and resource allocation is formulated to minimize the average delay. To realize the collaborative decision-making, a multi-agent deep reinforcement learning based algorithm is proposed based on the framework of centralized training and decentralized execution. The centralized deep neural networks (DNN) learn from the past experience and the WDs learn policies from the evaluation of the actions from the centralized DNNs. Based on the learned policies, WDs can make offloading decisions with only local information. Simulation results show that the proposed algorithm achieves near-optimal performance and has the advantage of high stability in varying multi-agent environments. (c) 2023 Elsevier Inc. All rights reserved.

关键词： Mobile edge computing   Task offloading   Cooperation   Energy efficiency   Forwarding method  

摘要： Mobile edge computing (MEC) has emerged as a promising paradigm to improve the energy efficiency for latency-constraint computation. This paper proposes a novel user cooperation approach in both computation and communication for MEC, based on the three-node cooperative offloading architecture, which consists of two mobile users and a computing access point (CAP). The mobile application tasks can be executed locally or be offloaded to either a cooperative mobile user or CAP for remote execution. The cooperative task offloading problem is investigated to minimize the energy consumption of mobile users while satisfying the execution delay. The problem is formulated as a mixed integer programming, and the NP-hardness is provided by reducing it to a 0-1 knapsack problem. This paper also provides an optimal algorithm based on dynamic programming and an efficient heuristic approach. Numerical results show that the cooperative offloading scheme outperforms the local computing method by 66.4% on the energy consumption of mobile nodes. Furthermore, the proposed heuristic algorithm can achieve near-optimal performance under different network settings.

关键词： drone edge cluster   mobile edge computing   task scheduling   deep reinforcement learning  

摘要： The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing task requests for the heterogeneous DEC. Benefiting from the latest advances in deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency through a collaborative Advantage Actor-Critic algorithm, which avoids the interference of resource overload and local downtime while ensuring load balancing. To better adapt to the real drone collaborative scheduling scenario, DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance, thus avoiding suboptimal decisions that severely affect Quality of Service (QoS) and Quality of Experience (QoE). With flexible parameter control, DECCo can adapt to various task requests on drone edge clusters. Google Cluster Usage Traces are used to verify the effectiveness of DECCo. Therefore, our work represents a state-of-the-art method for task scheduling in the heterogeneous DEC.

关键词： cloud computing   edge server   mobile edge computing   security   privacy  

摘要： Human-centered systems play an important role in the modern world, for example, driverless car, autonomous and smart vehicles, drones, and robotics. The internet of things environment demands a faster real-time response depending on the applications processed in a particular duration. Mobile edge computing (MEC) allows a user to get a real-time response as compared with cloud computing (CC), although ensuring a number of security attributes in MEC environment remains challenging. In this article, a protocol is designed to achieve mutual authentication, anonymous communication, and security against traceability, as these are very crucial factors to ensure the security of data and user's privacy. Moreover, the proposed scheme ensures mutual authentication between a mobile user and an edge server along with the user's anonymity and untraceability. The proof of security and evaluation of performance of the scheme validates that it ensures security attributes and improves efficiency in terms of communication and computation overheads.

关键词： Mobile edge computing   computation offloading   quality of service   resource allocation   federated learning  

摘要： Mobile edge computing (MEC) driven by 5G cellular systems has recently emerged as a promising paradigm, enabling mobile devices (MDs) with limited computing resources to offload various computation-intensive tasks (such as autopilot, online game) to edge servers to enhance the data processing capabilities of MDs. However, the uncertainty of wireless channel state and data volume of offloading tasks, as well as the data security privacy of offloading tasks, bring serious challenges to computation offloading in MEC. In this article, we consider a time-varying MEC scenario and formalize the delay and energy consumption during the computation offloading process as a joint optimization problem. Then the optimization problem is decomposed into two sub-problems: intelligent task prediction and resource allocation. Different from traditional methods, we improve the federated learning (FL) algorithm and propose a thoughtful cloud-edge-client FL task prediction mechanism based on Bidirectional Long Short-Term Memory. Each participating MD trains the model locally without uploading data to the server, and periodically aggregates the model in the edge and in the cloud. The algorithm both eliminates the need to solve complex optimization problems and ensures user privacy security. Finally, experimental results show that our proposed algorithm significantly outperforms other benchmark algorithms in energy efficiency.

关键词： Mobile edge computing   MEC server deployment   resource placement   profit maximization  

摘要： With the explosive growth of mobile applications, the development of mobile edge computing (MEC) has been greatly promoted since it can ably improve the quality of service for mobile applications by providing low latency and high-quality computation services. Most existing works focus on improving the efficiency of MEC with an assumption that the MEC servers have already been deployed. However, without appropriate deployment of MEC servers, the profitability of the MEC system can be significantly restrained, which hinders the rapid promotion of the MEC. To address this issue, we formulate an MEC server deployment problem for the MEC operator as a revenue maximization problem. Firstly, we model and analyze the various factors that affect the revenue. Secondly, we formulate a revenue maximization problem, which is NP-hard, but it is proved to be convex with respect to the total available computation units. Based on this feature, we propose a three-layer optimization algorithm, named EDM, in which the location, the deployed computation units, and the wholesaled computation resources are determined gradually, to maximize the total revenue. Experimental results demonstrate that the proposed EDM algorithm has significant advantages on revenue improvement compared to competitive benchmarks.