TS-Bat: Leveraging Temporal-Spatial Batching for Data Center Energy Optimization.

Data centers that run latency-critical workloads are typically provisioned for peak load even when they are operating at low levels of system utilization. Optimizing energy in data centers with Quality of Service (QoS) constraints is challenging since variabilities exist in job sizes, system utilization, and server configurations. Therefore, it is impractical to have a single configuration for energy management that works well across various scenarios. In this paper, we propose TS-Bat, a new data center energy optimization framework that judiciously integrates spatial and temporal job batching while meeting QoS constraints. TSBat works on commodity server platforms and comprises two major components: a temporal batching engine that batches the incoming jobs and creates opportunities for the processor to enter low power modes, and a spatial batching engine that schedules the batched jobs on to a server that is estimated to be idle. We implement a prototype of TS-Bat on a testbed with a cluster of servers, and evaluate TS-Bat on a variety of workloads. Our results show that pure temporal batching achieves 49% savings in CPU energy compared to a baseline configuration without batching. Through combining temporal and spatial batching, TSBat increases the energy savings by up to 68%.