Towards Thermal-Aware Workload Distribution in Cloud Data Centers Based on Failure Models

Abstract

Increasing workload conditions lead to a significant surge in power consumption and computing node failures in data centers. Existing workload distribution strategies merely are focused on either thermal awareness or failure mitigation, overlooking the impact of node failures on the energy efficiency of cloud data centers. To address this issue, a holistic model is built to characterize the impacts of workloads, computing and cooling costs, heat recirculation, and node failure on the energy efficiency of cloud data centers. Leveraging such a holistic model, we propose a novel thermal-aware workload distribution strategy called HGSA to take into account node failure, thereby aiming to improve the energy efficiency of cloud data centers. Our empirical findings confirm that (i) faulty nodes lead to a large rise in power consumption, and (ii) failure locations play a vital role in the power consumption of data centers. Experimental results unveil that our HGSA is adroit at making near-optimal decisions in workload distribution strategies. In particular, HGSA cuts down the minimum inlet temperature by 5.2%-15%, improves the maximum air temperature of CRAC by 4.2%-26.5%, lowers the cooling cost by 15.4%-50% compared to the existing solutions. Furthermore, HGSA cuts back the total power consumption by 0.65%-78%.