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A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling
2017
2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
A goal of cloud service management is to design self-adaptable auto-scaler to react to workload fluctuations and changing the resources assigned. The key problem is how and when to add/remove resources in order to meet agreed servicelevel agreements. Reducing application cost and guaranteeing service-level agreements (SLAs) are two critical factors of dynamic controller design. In this paper, we compare two dynamic learning strategies based on a fuzzy logic system, which learns and modifies
doi:10.1109/ccgrid.2017.15
dblp:conf/ccgrid/ArabnejadPJE17
fatcat:nh2rs5xknncxllwbtxh2xkge5q