Experiments with Smart Workload Allocation to Cloud Servers

Lan Wang, E. Gelenbe
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引用次数: 14

Abstract

We present experiments that compare three on-line real time techniques for task allocation to different cloud servers: an adaptive random neural network (RNN) based on reinforcement algorithm, an algorithm based on "sensible routing'', one which uses a simple analytical model to select the server is estimated to give the best response as a function of workload, and round-robin task allocation. Measurements indicate that the RNN based algorithm can make accurate decisions when it exploits frequent measurement updates.
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智能工作负载分配到云服务器的实验
我们提出的实验比较了三种在线实时技术的任务分配到不同的云服务器:一种基于强化算法的自适应随机神经网络(RNN),一种基于“感知路由”的算法,一种使用简单的分析模型来选择服务器,估计给出最佳响应作为工作量的函数,以及循环任务分配。测量结果表明,基于RNN的算法在利用频繁的测量更新时可以做出准确的决策。
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