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At present, the mainstream solution of edge computing offloading is to model it as a multi-objective optimization problem to minimize energy consumption and delay. Different from the existing research, this paper mainly considered that tasks in different unloading areas had certain similarity in edge computing, which could be used to accelerate the convergence speed and solution effect of the algorithm. Based on this, this paper proposed an evolutionary multi-task multi-objective optimization algorithm to solve task offloading problems in different regions based on evolutionary multi-task optimization. This algorithm considered multiple independent regions to be optimized. It modeled the task offloading system model for each region as a multi-objective optimization problem, dynamically adjusting the communication level of the population by learning the user distribution in different regions and the similarity of the tasks to be processed, accelerating convergence speed, and achieving optimization for two different regions through one evolution. The experimental results show that the proposed algorithm has achieved good results in convergence speed and the uniformity of the optimal solution distribution, and can obtain the unloading deployment optimization scheme under edge computing. [ABSTRACT FROM AUTHOR]

In mobile edge computing research, the edge server can effectively save computing resources by caching task data. But how to allocate cache resources to solve the competitive relationship between edge servers, as well as energy consumption and efficiency issues, and achieve optimal system performance is an NP difficult problem. Therefore, this paper proposes an online potential game resource allocation strategy OPSCO (online potential game strategy based on cache optimization) based on cache optimization, using a new cache replacement strategy CASCU (cache allocation strategy based on cache utility) to maximize the utility of the cache. By optimizing the efficiency indicator function of edge servers, combining factors such as cache replacement cost with Lyapunov optimization, potential game, and EWA (exponential weighting algorithm) algorithm, the competitive relationship of edge servers is modeled, and potential game related proofs and analyses are conducted. The simulation results show that compared with other resource allocation strategies, OPSCO can significantly improve the task completion rate and cache utility, reduce equipment energy consumption and time overhead, and solve the resource allocation and data cache problems in mobile edge computing online cache scenarios. [ABSTRACT FROM AUTHOR]

With the explosive growth of mobile data traffic in wireless networks, cache-enabled unmanned aerial vehicles(UAVs) are being used in mobile computing to serve as edge servers to provide on-demand services to users in the network. To bring better quality of experience to users while satisfying other resource constraints, it jointly optimized UAVs deployment, cache placement and user association to minimize content access latency for all users and provided users with content caching services of varying quality. This paper proposed a joint optimization algorithm based on alternating optimization for the scenario where multiple UAVs and ground base stations collaborate to provide caching services. The algorithm achieved a sub-optimal solution with a convergence guarantee by iteratively solving the three sub-problems obtained from the decomposition of the objective problem. Firstly, a successive convex approximation-based algorithm solved the UAV deployment sub-problem. Secondly, it used a greedy-based algorithm to solve the content caching sub-problem. Then it used a successive convex approximation algorithm based on the penalty function to solve the user access sub-problem. Finally, it repeated the above procedure to obtain a sub-optimal solution to the target problem. Several simulation experiments verified the effectiveness and feasibility of the proposed algorithm. The simulation results show that the proposed joint optimization algorithm performs better regarding average content access latency and cache hit ratio than the benchmark algorithm. [ABSTRACT FROM AUTHOR]

To address the issue of resource wastage and increasing latencies in the placement process of edge servers, this paper analyzed and modeled the user density and average access time of edge server placement schemes as multi-objective optimization problems. It designed an edge server placement scheme based on user density and average access time, and proposed a multi-objective sea-horse genetic algorithm (MOSGA) to solve this problem. The MOSGA algorithm firstly used the idea of multi-objective optimization algorithm to improve the SHO algorithm, so that the SHO algorithm could be applied to multi-objective optimization problems. It used the genetic algorithm to improve the propagation operation of the SHO algorithm, so that the MOSGA could better jump out of the local optimal solution and accelerate the solution of the problem. The proposed algorithm was verified on the data set of Shanghai Telecom, and the simulation experiment results show that MOSGA is obviously better than RA, K-means, NSGA and LMM, which not only effectively solves the problem of server resource waste, but also greatly reduces the time of terminal equipment to access the server. [ABSTRACT FROM AUTHOR]