Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter - Gao, Jiechao; Wang, Haoyu; Shen, Haiying (Proceedings - IEEE International Parallel and Distributed Processing Symposium, 2020)
The size and energy consumption of datacenters have been increasing significantly over the past years. As a result, datacenters' increasing electricity monetary cost, energy consumption and energy harmful gas emissions have become a severe problem. Renewable energy supply is widely seen as a promising solution. However, the instability of renewable energy brings about a new challenge since insufficient energy supply may lead to job running interruptions or failures. Though previous works attempt to more accurately predict the amount of produced renewable energy, due to the instability of its influencing factors (e.g., wind, temperature), sufficient renewable energy supply cannot be always guaranteed. To handle this problem, in this paper, we propose allocating jobs with the same service-level-objective (SLO) level to the same physical machine (PM) group, and power each PM group with renewable energy generators that have probability no less than its SLO to produce the amount no less than its energy demand. It ensures that insufficient renewable energy supply will not lead to SLO violations. We use a deep learning technique to predict the probability of producing amount no less than each value of each renewable energy source and predict the energy demands of each PM area. We formulate an optimization problem: how to match renewable energy resources with different instabilities to different PM groups as energy supply in order to minimize the number of SLO violations (due to interruption from insufficient renewable energy supply), total energy monetary cost and total carbon emission. We then use reinforcement learning method and linear programming method to solve the optimization problem. The real trace driven experiments show that our method can achieve much lower SLO violations, total energy monetary cost and total carbon emission compared to other methods.