NIPD: Non-Intrusive Power Disaggregation in Legacy Datacenters

Abstract

Fine-grained power monitoring, which refers to power monitoring at the server level, is critical to the efficient operation and energy saving of datacenters. Fined-grained power monitoring, however, is extremely challenging in legacy datacenters that host server systems not equipped with power monitoring sensors. Installing power monitoring hardware at the server level not only incurs high costs but also complicates the maintenance of high-density server clusters and enclosures. In this paper, we present a zero-cost, purely software-based solution to this challenging problem. We use a novel technique of non-intrusive power disaggregation (NIPD) that establishes power mapping functions (PMFs) between the states of servers and their power consumption, and infer the power consumption of each server with the aggregated power of the entire datacenter. The PMFs that we have developed can support both linear and nonlinear power models via the state feature transformation. To reduce the training overhead, we further develop adaptive PMFs update strategies and ensure that the training data and state features are appropriately selected. We implement and evaluate NIPD over a real-world datacenter with $326$ nodes. The results show that our solution can provide high precision power estimation at both rack level and server level. In specific, with PMFs including only two nonlinear terms, our power estimation i) at rack level has mean relative error of $2.18$ percent, and ii) at server level has mean relative errors of $9.61$ and $7.53$ percent corresponding to the idle and peak power, respectively.