关键词： Mobile edge computing   Energy-aware systems   Task scheduling   Heuristic algorithms  

摘要： Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile *** edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission *** nodes are usually distributed in edge environments,enabling crucially efficient task scheduling among those nodes to achieve reduced processing ***,it is imperative to conserve edge server energy,enhancing their *** this end,this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization(EA-DFPSO)that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution *** proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for *** experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.

关键词： Mobile edge computing   Carbon neutralization   Power monitoring and dispatching   Innovation path  

摘要： In the process of gradual development of power grid, the role of service support and technical support of dispatching secondary system is becoming increasingly prominent, and the operation and maintenance work is also increasing with the number and types of dispatching secondary system, which makes the limitations of traditional monitoring methods more prominent. In this paper, a task unloading and resource allocation optimization (TORA) algorithm is proposed, which decomposes the task unloading decision and resource allocation problem into two subproblems and solves two optimization subproblems, respectively. The feasibility of the algorithm is demonstrated, and the calculation method of the theoretical lower bound of the original problem is given. Experimental results show that TORA, RTO and UTO algorithms can get results close to the theoretical lower bound. When the number of unit tasks is too large, the average task completion time of TORA algorithm is more than 20% less than that of RTO algorithm. When the user task uploads a large amount of data, the average completion time of TORA algorithm task is more than 15% less than that of RTO and UTO algorithms.

关键词： Optimization   Autonomous aerial vehicles   Task analysis   Statistics   Sociology   Computational modeling   Servers   Mobile edge computing   UAV   nature-inspired algorithms   computational task offloading   discrete optimization   convergence analysis  

摘要： Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computational tasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon's test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs.

关键词： mobile edge computing   collaboration   task offloading   service caching   resource allocation   fairness   load balance  

摘要： Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance.

关键词： mobile edge computing   D2D communication   partial offloading   Q learning   deep Q-network  

摘要： Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number of devices under the maximum delay constraints of the application and the limited computing resources. In the considered system, each user can offload its tasks to an edge server and a nearby D2D device. We first formulate the optimization problem as an NP-hard problem and then decouple it into two subproblems. The convex optimization method is used to solve the first subproblem, and the second subproblem is defined as a Markov decision process (MDP). A deep reinforcement learning algorithm based on a deep Q network (DQN) is developed to maximize the amount of tasks that the system can compute. Extensive simulation results demonstrate the effectiveness and superiority of the proposed scheme.

关键词： Mobile edge computing   Computation offloading   Task correlation   Submodular optimization  

摘要： In this paper, we first study the problem of Correlation-aware Task computation offloading (CoTask) in mobile edge computing. Specifically, considering the correlation among multiple computation tasks, we study how to determine a joint task offloading decision and resource allocation strategy under the constraints of feasible offloading decisions and edge servers' computation capacity, such that the overall task latency is minimized. Before addressing the challenging CoTask problem, we first investigate the case without considering task correlation, namely, NonCoTask. We prove that, NonCoTask with two combinatorial integer-continuous optimization variables (i.e., integral offloading decision variable and continuous resource allocation variable) can be equivalently solved by solving a 0-1 integer programming problem with respect to the offloading decision variable only, while the closed-form optimal resource allocation can be efficiently obtained under any given offloading decision. The 0-1 integer programming problem further falls into the realm of minimizing a supermodular set function with a matroid base constraint. We then propose a performance-guaranteed algorithm for NonCoTask. Next, we rigorously analyze the performance gap between CoTask and NonCoTask, and develop a correlation-aware offloading decision and resource allocation algorithm with theoretic performance guarantee for CoTask, via a correlation-aware exchange search based on the solution to NonCoTask. Extensive evaluation results show that our proposed algorithm outperforms several state-of-the-art algorithms as well as their enhanced counterparts with correlation in terms of overall task latency.

关键词： NOMA   Optimization   Delays   Time division multiple access   Task analysis   Interference   Costs   Mobile edge computing   cognitive capacity harvesting networks   non-orthogonal multiple-access  

摘要： In this paper, we investigate the resource allocation of mobile edge computing (MEC) in cognitive capacity harvesting networks (CCHNs) when non-orthogonal multiple-access (NOMA) technique is adopted. Different from traditional studies for NOMA-MEC networks, we aim at minimizing the total cost of CCHN while satisfying the quality-of-service (QoS) of secondary users (SUs). We adopt the mechanism of time division multiple access (TDMA) when several NOMA groups use the same spectrum, and consider both the waiting delay and transmission delay during data offloading with the optimization of transmission order of NOMA groups. We formulate the considered problem as a mixed integer non-linear programming (MINLP). We show that the transmit power and the allocated computing resource for each SU can be derived when the transmission time and transmission order of the NOMA groups are given. Based on this, the considered problem can be decomposed into a transmission time and order optimization subproblem, a cellular resource block (CRB) selection subproblem and a cognitive radio (CR) router selection subproblem. To solve the transmission time and order optimization subproblem, we first simplify the delay constraint via theoretic analysis, and then propose a binary segmentation (B-Seg) algorithm and a transmission order adjustment (TOA) algorithm to find the optimal transmission time and transmission order of NOMA groups, respectively. To solve the CRB selection subproblem and the CR router selection subproblem, a bigger requirement first (BRF) algorithm and a game-based iteration (GBI) algorithm are respectively proposed. Simulation results show that the proposed algorithms can significantly improve the system performance.

关键词： UAV communications   Communications security   Reverse auction   Mobile edge computing   Data offloading   Jamming  

摘要： In recent years, the security of wireless communication has received more and more attention. However, with the increasing requirements for security performance in the process of wireless communication, the cost of improving security also increases. Therefore, how to improve the security of wireless communication while reducing costs has become a serious challenge. Aiming at the security problems in the UAV-assisted wireless communication link based on the premise of low cost and high profit, this paper studies how to motivate non-offloading users to send jamming power to interfere with eavesdropping UAVs, so efficiently and economically to improve the security performance of wireless communication link. Firstly, the relationship between the security performance of the communication link and the interference power is revealed through the theoretical model. Then, we propose a scenario consisting of a legitimate UAV, a ground user group, and an eavesdrop UAV. In this scenario, we divide the ground users into offloading users and non-offloading users. At the same time, the cost functions for offloading users and utility functions for non-offloading users are defined, and non-offloading users are encouraged to help to offload users unload data safely to a legitimate UAV by obtaining rewards for offloading users. Considering the selfishness of users, a reverse auction algorithm based on multi-user groups is proposed to achieve the optimal mapping between non-offloading users and offloading users. Finally, the effectiveness of the proposed scheme is verified by a large number of simulations. The research results show that the scheme innovative designs a method for users to cooperate to interfere with eavesdroppers, which can reduce costs and improve utility while maximizing the improvement of wireless communication link security.(c) 2022 Elsevier B.V. All rights reserved.

关键词： 5G   Mobile edge computing   Graph embedding   Blockchain   Stackelberg game   Resource management  

摘要： In the 5G scenario of the convergence of information technology (IT) and communication technology (CT), multi-operators collaborate to form edge computing, which makes the problem of resource optimization more complicated than ever. Users may access resources deployed by various MEC's operators to achieve ultra-low latency. However, traditional resource management methods consider only a single operator failure to handle profit allocation and privacy security issues among different operators. To address this problem, we proposed a resource management framework named GBRM based on graph embedding and blockchain. Specifically, we use the Stackelberg game model to solve MEC servers' cache-offloading problem;non-indexed content sharing by Deepwalk graph embedding between MECs ensures the privacy of different operators' content. Consortium blockchain assists in the trusted profit allocation of services across various operators. Experiments show in the virtual network scenario that our work performance is significantly better than the RandomSelect and the LocalIndex method in global latency and close to the global index's ideal situation. Multi-operators collaborate to form edge computing, which makes the problem of resource optimization more complicated than ever.

关键词： Internet of things   Mobile edge computing   Microservice   Edge caching   Deep reinforcement learning  

摘要： The ubiquitous Internet of Things (IoTs) devices spawn growing mobile services of applications with computationally-intensive and latency-sensitive features, which increases the data traffic sharply. Driven by container technology, microservice is emerged with flexibility and scalability by decomposing one service into several independent lightweight parts. To improve the quality of service (QoS) and alleviate the burden of the core network, caching microservices at the edge of networks empowered by the mobile edge computing (MEC) paradigm is envisioned as a promising approach. However, considering the stochastic retrieval requests of IoT devices and time-varying network topology, it brings challenges for IoT devices to decide the caching node selection and microservice replacement independently without complete information of dynamic environments. In light of this, a MEC-enabled di stributed cooperative m icroservice ca ching scheme, named DIMA, is proposed in this paper. Specifically, the microservice caching problem is modeled as a Markov decision process (MDP) to optimize the fetching delay and hit ratio. Moreover, a distributed double dueling deep Q-network (D3QN) based algorithm is proposed, by integrating double DQN and dueling DQN, to solve the formulated MDP, where each IoT device performs actions independently in a decentralized mode. Finally, extensive experimental results are demonstrated that the DIMA is well-performed and more effective than existing baseline schemes.