Abstract
The huge energy consumption of cloud data centers not only increases costs but also carbon emissions associated with such data centers. Powering data centers with renewable or green sources of energy can reduce brown energy use and consequently carbon emissions. However, powering data centers with these energy sources is challenging, as they are variable and not available at all times. In this work, we formulate the microservices management problem as finite Markov Decision Processes (MDP) to optimise renewable energy use. By dynamically switching off non-mandatory microservices and scheduling battery usage, upon the user’s preference, our proposed method makes a trade-off between the workload execution and brown energy consumption. We evaluate our proposed method using traces derived from two real workloads and real-world solar data. Simulated experiments show that, compared with baseline algorithms, our proposed approach performs up to 30% more efficiently in balancing the brown energy usage and workload execution.
Minxian Xu was with the Faculty of Information Technology, Monash University; he is now with Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. A major part of this work was done while the author was at the Monash University.
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Acknowledgments
This work is partially supported by Monash Infrastructure Research Seed Fund Grant and FIT Early Career Researcher Seed Grant.
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Xu, M., N. Toosi, A., Bahrani, B., Razzaghi, R., Singh, M. (2019). Optimized Renewable Energy Use in Green Cloud Data Centers. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_24
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