Transitive Power Modeling for Improving Resource Efficiency in a Hyperscale Datacenter

Abstract

Maintaining efficient utilization of allocated compute resources and controlling their capital and operating expenditure is important for running a hyperscale datacenter infrastructure. Power is one of the most constrained and difficult to manage resources in datacenters. Accurate accounting of power usage across clients of multi-tenant web services can improve budgeting, planning and provisioning of compute resources. In this work, we propose a queuing theory based transitive power modeling framework that estimates the total power cost of a client request across the stack of shared services running in Facebook datacenters. By capturing the non-linearity of power vs load relation, our model is able to estimate marginal change in power consumption of a system upon serving a request with a mean error of less than 4% when applied on production services. In view of the fact that datacenter capacity is planned for peak demand, we test this model at peak load to report up to 2x improvement in accuracy compared to a mathematical model. We further leverage this framework along with a distributed tracing system to estimate power demand shift for serving particular product features within fraction of a percentage and guide the decision to shift their computation at off-peak time.