Temperature Aware Workload Management in Geo-distributed Datacenters

For geo-distributed datacenters, lately a workload management approach that routes user requests to locations with cheaper and cleaner electricity has been shown promising in reducing the energy cost. We consider two key aspects that have not been explored before. First, the energy-gobbling cooling systems are often modeled with a location-independent efficiency factor. Yet, through empirical studies, we find that their actual energy efficiency depends directly on the ambient temperature, which exhibits a significant degree of geographical diversity. Temperature diversity can be used to reduce the overall cooling energy overhead. Second, datacenters run not only interactive workloads driven by user requests, but also delay tolerant batch workloads at the backend. The elastic nature of batch workloads can be exploited to further reduce the energy consumption.

In this paper, we propose to make workload management for geo-distributed datacenters temperature aware. We formulate the problem as a joint optimization of request routing for interactive workloads and capacity allocation for batch workloads. We develop a distributed algorithm based on an m-block alternating direction method of multipliers (ADMM) algorithm that extends the classical 2-block algorithm. We prove the convergence of our algorithm under general assumptions. Through trace-driven simulations with real-world electricity prices, historical temperature data, and an empirical cooling efficiency model, we find that our approach is consistently capable of delivering a 15%–20% cooling energy reduction, and a 5%–20% overall cost reduction for geo-distributed clouds.