Hotspot-Aware Workload Scheduling and Server Placement for Heterogeneous Cloud Data Centers

Abstract

Data center servers located in thermal hotspot regions receive inlet air at a higher than the set temperature and thus generate comparatively high outlet temperature. Consequently, there is a rise in energy that is consumed to cool down the servers that otherwise would undergo reliability hazards. The workload deployment across the servers should be resilient to thermal hotspots to ensure smooth performance. In a heterogeneous data center environment, an equally important fact is the placement of the servers in a thermal hotspot-aware manner to lower the peak outlet temperatures. These approaches can be applied proactively with the help of outlet temperature prediction. This paper presents the hotspot adaptive workload deployment algorithm (HAWDA) and hotspot aware server relocation algorithm (HASRA) based on thermal profiling regarding outlet temperature prediction. HAWDA deploys workload on servers in a thermal-efficient manner and HASRA optimizes the server location in thermal hotspot regions to lower the peak outlet temperatures. Performance comparison is carried out to analyze the efficacy of HAWDA against the TASA and GRANITE algorithms. Results suggest that HAWDA provides average peak utilization of the servers similar to GRANITE and TASA without additional burden on the cooling mechanism, with and without server relocation, as HAWDA minimizes the peak outlet temperature.