With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there arc several ...
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ISBN:
(纸本)9781538647271
With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there arc several edge clouds, each of which has limited resource. We aim to maximize the number of offloaded tasks and minimize the energy consumption of all the UEs and edge clouds, by selecting the best edge cloud for each UE to offload. We formulate the problem as a mixed-integer non-convex optimization, which is difficult to solve in general. By transforming this problem into a minimum-cost maximum-flow (MCMF) problem, we can solve it efficiently. The simulation shows that our proposed algorithm has better performance and lower complexity than the conventional solutions.
In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user eq...
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In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user equipments (UEs) in computing. We aim to jointly minimize the system energy consumption and maximize the number of offloaded tasks by optimizing the task scheduling between UEs and BSs. A multiple-objective and mix-integer problem is formulated, which is difficult to solve. To tackle the problem, we combine the ant colony optimization (ACO) algorithm with load balancing, and propose an efficient algorithm. The simulation demonstrates the effectiveness of the proposed algorithm. (C) 2021 The Authors. Published by Elsevier B.V.
In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user eq...
详细信息
In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user equipments (UEs) in computing. We aim to jointly minimize the system energy consumption and maximize the number of offloaded tasks by optimizing the task scheduling between UEs and BSs. A multiple-objective and mix-integer problem is formulated, which is difficult to solve. To tackle the problem, we combine the ant colony optimization (ACO) algorithm with load balancing, and propose an efficient algorithm. The simulation demonstrates the effectiveness of the proposed algorithm.
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