Zero-cost, fine-grained power monitoring of datacenters using non-intrusive power disaggregation
Proceedings of the 16th Annual Middleware Conference, 2015•dl.acm.org
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 …
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 …
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. 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 the rack level, with mean relative error of 2.63%, and the server level, with mean relative error of 10.27% and 8.17% for the estimation of idle power and peak power, respectively.
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