关键词： Mobile edge computing   Microservices   Service composition   Multi-objective optimization   QoS  

摘要： With the development of software technology, some complex mobile and Internet-of-Things (IoT) applications can be constituted by a set of microservices. At present, mobile edge computing (MEC) has been used for microservice provision to achieve faster response speed and less network pressure. Based on container technology, microservices can be easily deployed in the MEC environment, while multiple microservice instances in multiple locations need to be selected to provide services for a large number of users geographically distributed. How to fully consider the service response time, scheduling the startup and running strategies of microservice instances with the least resource cost for multiple mobile edge servers is the core problem of microservice composition. In this paper, we propose a multi-objective evolutionary approach (MSCMOE) based on improved NSGA-III to minimize the service access delay and network resource consumption in the process of microservice composition. In order to maintain the diversity of the population, we use the improved reference point strategy to enhance the computational efficiency of seeking elite solutions in the non-dominated layer. Experimental results based on a real data set of Shanghai Telecom demonstrate that MSCMOE can effectively reduce network resource consumption while reducing service request time.

关键词： Energy harvesting   Mobile edge computing   Energy transmission   Wireless power transmission  

摘要： Mobile edge computing (MEC) can provide rich computing services near mobile terminals, making it possible for computing intensive tasks to be performed at the edge, however, when renewable energy is the main power supply method for MEC servers, due to the unpredictability of renewable energy, the server will generate additional processing delays due to low energy. In this paper, in order to solve this problem, we incorporate renewable energy into the mobile edge computing, and use wireless power transmission technology to realize energy transmission between MEC servers. By optimizing offloading and wireless resource allocation, the total delay of the system is minimized. The main difficulty of this paper is the combination of unloading decision and its strong coupling with wireless resource allocation. In order to solve this problem, we use Lagrange multiplier method to get the optimal allocation of computing resources, and proposes an effective offloading exclusion algorithm (OEA) to determine the optimal offloading decision. Finally, the experimental results show that compared with other comparison schemes, the proposed scheme can effectively improve the performance of MEC and reduce the time consumption of the system.

关键词： Mobile edge computing   Unmanned aerial vehicle   Differential evolution   Clustering algorithm   Tabu search algorithm  

摘要： This paper presents an energy and task completion time minimization scheme for the unmanned aerial vehicles (UAVs)-empowered mobile edge computing (MEC) system, where several UAVs are deployed to serve large-scale users' equipment (UEs). The aim is to minimize the weighted sum of energy consumption and task completion time of the system by planning the trajectories of UAVs. The problem is NP-hard, non-convex, non-linear, and mixed-decision variables. Therefore, it is very challenging to be solved by conventional optimization techniques. To handle this problem, this paper proposes an energy and task completion time minimization algorithm (ETCTMA) that solves the above problem in three steps. In the first step, the deployment updation of stop points (SPs) is handled by adopting a differential evolution algorithm with a variable population size. Then, in the second step, the association between SPs and UAVs is determined. Specifically, a clustering algorithm is proposed to associate SPs with UAVs. Finally, in the third step, a low-complexity tabu search algorithm is adopted to construct the trajectories of all UAVs. The performance of the proposed ETCTMA is tested on seven instances with up to 700 UEs. The results reveal that our proposed algorithm ETCTMA outperforms other variants in terms of minimizing the weighted sum of energy consumption and task completion time of the system.

关键词： Task analysis   Resource management   Trajectory   Wireless communication   Servers   Optimization   Manganese   Mobile edge computing   UAV networks   task offloading   cooperative offloading   deep reinforcement learning  

摘要： Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the trajectories, computation task allocation, and communication resource management of UAVs. Moreover, to solve the above non-convex optimization problem, a Markov decision process is formulated for the multi-UAV assisted mobile edge computing system. To obtain the joint strategy of trajectory design, task allocation, and power management, a cooperative multi-agent deep reinforcement learning framework is investigated. Considering the high-dimensional continuous action space, the twin delayed deep deterministic policy gradient algorithm is exploited. The evaluation results demonstrate that our multi-UAV multi-EC task offloading method can achieve better performance compared with the other optimization approaches.

关键词： Device-to-device communication   Task analysis   Optimization   Servers   Resource management   Energy consumption   Relays   Energy-efficient   social network   mobile edge computing   device-to-device communication   matching theory   Lyapunov optimization  

摘要： The future wireless network will face demands of massive connectivity and intensive computation with the increase of mobile devices. Mobile edge computing (MEC) and Device-to-Device (D2D) have emerged as promising technologies to address the above challenges, and implementing social relationships in D2D-MEC networks can improve the reliability of D2D links. Exploiting these benefits, we investigate the energy-efficient task offloading problem in socially-aware D2D-assisted MEC networks, where the user devices can offload tasks to the nearby device or further forward to the MEC server based on social relationships. Specifically, we design a task partial offloading scheme of joint D2D connection selection, transmit power control and task allocation, to maximize the long-term network utility with considering dynamic system status and random task arrival. First, the social relationship among users is quantified into a social trust matrix. As the formulated socially-aware energy-efficient problem is a long-term stochastic optimization problem that is directly intractable, we thus employ the Lyapunov optimization to transform it into a series of short-term problems, each of which can be solved by the Karush-Kuhn-Tucker method and a pricing-based matching algorithm. Finally, we verify the performance optimality and the long-term network stability through numerical simulations as well as theoretical analysis.

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： Mobile edge computing   Service migration   Three-way decisions   Evaluation function   Mobile edge computing   Service migration   Three-way decisions   Evaluation function  

摘要： In mobile edge computing (MEC), the mobile device cannot always stably connect with the same edge server due to the movement of the user. Considering the complexity and ran-domness of users' movement, most of the existing service migration strategies based on two-way decisions (namely either migrate or not migrate) may make wrong decisions, which will increase the energy consumption and significantly impact users' experience. To address such an issue, we employ three-way decisions for service migration. Specifically, first, based on users' movement trajectory, we allocate users into three regions: migration region, non-migration region and delay region. Afterwards, we execute different operations for users in different regions accordingly. In the migration region, con-sidering the energy consumption and service delay time, we propose a new service migra-tion method which utilizes the latest migration radius and the lowest energy consumption for service migration. In the delay region, the edge server will keep collecting users' move-ment information to prepare for the further decision making. In the non-migration region, no action is required by the edge server. Comprehensive simulation experiments using parameter settings consistent with real-world edge computing environments are con-ducted to show its superior performance compared with other strategies.(c) 2022 Published by Elsevier Inc.

关键词： Task analysis   Resource management   Energy consumption   Optimization   Wireless communication   Reinforcement learning   Internet of Vehicles   6G   B5G   block coordinate descent   computation offloading   energy efficiency   Internet of Vehicles   mobile edge computing  

摘要： The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.

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.