Peak power management of datacenters has tremendous cost implications. While numerous mechanisms have been proposed to cap power consumption, real datacenter power consumption data is scarce. To address this gap, we collect power demands at multiple spatial and \ufb01ne-grained temporal resolutions from the load of geo-distributed datacenters of Microsoft over 6 months. We conduct aggregate analysis of this data, to study its statistical properties. With workload characterization a key ingredient for systems design and evaluation, we note the importance of better abstractions for capturing power demands, in the form of peaks and valleys. We identify and characterize attributes for peaks and valleys, and important correlations across these attributes that can in\ufb02uence the choice and e\ufb00ectiveness of di\ufb00erent power capping techniques. With the wide scope of exploitability of such characteristics for power provisioning and optimizations, we illustrate its bene\ufb01ts with two speci\ufb01c case studies.<\/p>\n","protected":false},"excerpt":{"rendered":"
Peak power management of datacenters has tremendous cost implications. While numerous mechanisms have been proposed to cap power consumption, real datacenter power consumption data is scarce. To address this gap, we collect power demands at multiple spatial and \ufb01ne-grained temporal resolutions from the load of geo-distributed datacenters of Microsoft over 6 months. 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