URL: A unified reinforcement learning approach for autonomic cloud management
Journal of Parallel and Distributed Computing
Interference and locality-aware task scheduling for MapReduce applications in virtual clusters
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
Two levels autonomic resource management in virtualized IaaS
Future Generation Computer Systems
Toward Informed Resource Management in the Cloud
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Cloud computing has a key requirement for resource configuration in a real-time manner. In such virtualized environments, both virtual machines (VMs) and hosted applications need to be configured on-the-fly to adapt to system dynamics. The interplay between the layers of VMs and applications further complicates the problem of cloud configuration. Independent tuning of each aspect may not lead to optimal system wide performance. In this paper, we propose a framework, namely CoTuner, for coordinated configuration of VMs and resident applications. At the heart of the framework is a model-free hybrid reinforcement learning (RL) approach, which combines the advantages of Simplex and RL methods and is further enhanced by the use of system knowledge guided exploration policies. Experimental results on Xen-based virtualized environments with TPC-W and TPC-C benchmarks demonstrate that CoTuner is able to drive a virtual server system into an optimal or near optimal configuration state dynamically, in response to the change of workload. It improves the systems throughput by more than 30% over independent tuning strategies. In comparison with the coordinated tuning strategies based solely on Simplex or basic RL algorithm, the hybrid RL algorithm gains 30% to 40% throughput improvement. Moreover, the algorithm is able to reduce SLA violation of the applications by more than 80%.