摘要：Software resource allocation has a significant impact on the quality of service and the performance of multi-tier web systems. It poses a great challenge to compute the allocation of different software resources in order to meet performance requirements under dynamic workloads conditions. To this end, this paper proposes an iterative feedback mechanism to optimize software resource allocation of multi-tier web systems. Specifically, we propose a Q-learning network-based approach for performance prediction. The predictor involves a deep Q-learning network for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the RUBiS benchmark system, and the experimental results demonstrate its significant advantages.
© 2020 IEEE.

卷、期、页：p802-809

发表日期：2020-05-01

收录情况：EI（工程索引）

发表期刊名称：Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020

参与作者：殷小静,刘磊,何伟,崔立真

第一作者：黄霁崴

论文类型：会议论文

论文概要：殷小静,黄霁崴,刘磊,何伟,崔立真，An Iterative Feedback Mechanism for Auto-Optimizing Software Resource Allocation in Multi-Tier Web Systems，Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020，2020，p802-809

论文题目：An Iterative Feedback Mechanism for Auto-Optimizing Software Resource Allocation in Multi-Tier Web Systems