Correlation-Aware Virtual Machine Allocation for Energy-Efficient Datacenters
Server consolidation plays a key role to mitigate the continuous power increase of datacenters. The recent advent of scale-out applications (e.g., web search, MapReduce, etc.) necessitate the revisit of existing server consolidation solutions due to distinctively different characteristics compared to traditional high-performance computing (HPC), i.e., user interactive, latency critical, and operations on large data sets split across a number of servers. This paper presents a power saving solution for datacenters that especially targets the distinctive characteristics of the scale-out applications. More specifically, we take into account correlation information of core utilization among virtual machines (VMs) in server consolidation to lower actual peak server utilization. Then, we utilize this reduction to achieve further power savings by aggressively-yet-safely lowering the server operating voltage and frequency level. We have validated the effectiveness of the proposed solution using 1) multiple clusters of real-life scale-out application workloads based web search and 2) utilization traces obtained from real datacenter setups. According to our experiments, the proposed solution provides up to 13.7% power savings with up to 15.6% improvement of Quality-of-Service (QoS) compared to existing correlation-aware VM allocation schemes for datacenters.