A Survey of Machine Learning Applications for Energy-Efficient Resource Management in Cloud Computing Environments


Demirci M.

IEEE 14th International Conference on Machine Learning and Applications ICMLA, Florida, United States Of America, 9 - 11 December 2015, pp.1185-1190 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/icmla.2015.205
  • City: Florida
  • Country: United States Of America
  • Page Numbers: pp.1185-1190
  • Keywords: Energy Efficiency, Resource Management, Data Centers, Cloud Computing, Machine Learning

Abstract

Ensuring energy efficiency in data centers is a crucial objective in modern cloud computing because it reduces operating costs and complies with the goals of green computing. Researchers strive to develop optimal policies for resource management in the cloud, which has many components such as virtual machine placement, task scheduling, workload consolidation, and so on. Machine learning has a major role to play in these efforts. In this paper, we provide a detailed survey of recent works in the literature which have employed machine learning (ML) to offer solutions for energy efficiency in cloud computing environments. We also present a comparative classification of the proposed methods. Furthermore, we enrich this survey by studying non-ML proposals to energy conservation in data centers, and also how ML has been applied towards other objectives in the cloud.