Load Balancing Oriented Computation Offloading in Mobile Cloudlet

Danhui Yao, Lin Gui, Fen Hou, Fei Sun, Daihui Mo, Hangguan Shan
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引用次数: 9

Abstract

The limited computing ability of mobile device constrains its performance on complex mobile applications. Mobile cloud computing (MCC) has therefore emerged to migrate computation-intensive tasks to remote clouds or mobile cloudlets. Most strategies allocate tasks with minimal response time yet few consider the load of the nodes. In this paper, we focus on the load balancing problem for nodes when they conduct offloading. We first establish a five-tuple characterized task model to capture the response time of offloaded tasks. Then, we formulate the task allocation problem as an integer linear problem (ILP) under certain conditions. Furthermore, we propose a two-step appointment- driven strategy to solve this problem with minimal task response time. Specifically, a modified genetic algorithm (GA) is adopted to coordinate the load of the nodes. Simulations are conducted to prove the feasibility of our strategy and evaluate the performance of load coordination.
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面向负载均衡的移动Cloudlet计算卸载
移动设备有限的计算能力制约了其在复杂移动应用中的性能。因此,移动云计算(MCC)的出现是为了将计算密集型任务迁移到远程云或移动云。大多数策略以最小的响应时间分配任务,但很少考虑节点的负载。本文主要研究节点卸载时的负载均衡问题。我们首先建立了一个五元组特征任务模型来捕获卸载任务的响应时间。然后,在一定条件下,将任务分配问题表述为整数线性问题(ILP)。此外,我们提出了一种两步预约驱动策略,以最小的任务响应时间来解决这一问题。具体来说,采用一种改进的遗传算法(GA)来协调节点的负载。通过仿真验证了该策略的可行性,并对负载协调的性能进行了评价。
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