Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing
Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing
复制标题
移动边缘计算中多个异构服务器的计算卸载策略优化
DOI：
10.1109/tsusc.2019.2904680
复制
发表时间：
2019-03
影响因子：
3.9
通讯作者：
Keqin Li
中科院分区：
文献类型：
--
作者：
Keqin Li
从用户设备（UE）到移动的边缘云（MEC）的计算卸载是减轻移动的设备的计算负担、提高移动的应用的性能、降低能耗以及延长移动的用户设备的电池寿命的有效方式。本文研究了移动的边缘计算中多个异构服务器的计算卸载策略优化问题。针对UE和来自不同MEC的多个异构服务器建立调度模型，并严格分析UE和每个MEC服务器的平均任务响应时间以及UE上生成的所有离线和非离线任务的平均响应时间。三个多变量优化问题，即，在平均功耗约束下的平均响应时间最小化、在平均响应时间约束下的平均功耗最小化、以及成本性能比最小化，使得计算卸载策略优化、功率-性能折衷以及功率-时间乘积都可以在负载平衡的上下文中被研究。针对平均功耗约束下的平均响应时间最小化、平均响应时间约束下的平均功耗最小化和性能价格比最小化问题，提出了一种有效的数值方法（由一系列快速数值算法组成）。数值例子和数据也证明了我们的方法的有效性，并显示功率性能的权衡，功率时间的产品，和各种参数的影响。据作者所知，这是文献中的第一项工作，分析解决了移动的边缘计算中多个异构服务器的计算卸载策略优化。
Computation offloading from a user equipment (UE) to a mobile edge cloud (MEC) is an effective way to ease the computational burden of mobile devices, to improve the performance of mobile applications, to reduce the energy consumption and to extend the battery lifetime of mobile user equipments. In this paper, we consider computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing. Queueing models are established for a UE and multiple heterogeneous servers from different MECs, and the average task response time of the UE and each MEC server and the average response time of all offloadable and non-offloadable tasks generated on the UE are rigorously analyzed. Three multi-variable optimization problems are formulated, i.e., minimization of average response time with average power consumption constraint, minimization of average power consumption with average response time constraint, and minimization of cost-performance ratio, so that computation offloading strategy optimization, power-performance tradeoff, as well as power-time product can all be studied in the context of load balancing. An efficient numerical method (which consists of a series of fast numerical algorithms) is developed to solve the problems of minimization of average response time with average power consumption constraint, minimization of average power consumption with average response time constraint, and minimization of cost-performance ratio. Numerical examples and data are also demonstrated to show the effectiveness of our method and to show the power-performance tradeoff, the power-time product, and the impact of various parameters. To the best of the author's knowledge, this is the first work in the literature that analytically addresses computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing.
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DOI：
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发表时间：
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期刊：
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影响因子：
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发表时间：
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影响因子：
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