Power provisioning for a warehouse-sized computer

Abstract

Large-scale Internet services require a computing infrastructure that can be appropriately described as a warehouse-sized computing system. The cost of building datacenter facilities capable of delivering a given power capacity to such a computer can rival the recurring energy consumption costs themselves. Therefore, there are strong economic incentives to operate facilities as close as possible to maximum capacity, so that the non-recurring facility costs can be best amortized. That is difficult to achieve in practice because of uncertainties in equipment power ratings and because power consumption tends to vary significantly with the actual computing activity. Effective power provisioning strategies are needed to determine how much computing equipment can be safely and efficiently hosted within a given power budget.In this paper we present the aggregate power usage characteristics of large collections of servers (up to 15 thousand) for different classes of applications over a period of approximately six months. Those observations allow us to evaluate opportunities for maximizing the use of the deployed power capacity of datacenters, and assess the risks of over-subscribing it. We find that even in well-tuned applications there is a noticeable gap (7 -16%) between achieved and theoretical aggregate peak power usage at the cluster level (thousands of servers). The gap grows to almost 40% in whole datacenters. This headroom can be used to deploy additional compute equipment within the same power budget with minimal risk of exceeding it. We use our modeling framework to estimate the potential of power management schemes to reduce peak power and energy usage. We find that the opportunities for power and energy savings are significant, but greater at the cluster-level (thousands of servers) than at the rack-level (tens). Finally we argue that systems need to be power efficient across the activity range, and not only at peak performance levels.

Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

“…Since available models for server power consumption primarily focus on CPU utilization [19], any placement decision that results in lesser number of active PMs compared to others have higher resource utilization across all dimensions and lesser energy consumption. So, we formulate the objective function f as a single minimization function on y:…”

“…Power consumption of servers is dominated by their CPU and can be expressed as a linear expression of CPU utilization [19]. So, we model the energy drawn by a PM p as a linear function of its CPU utilization U CP U p ∈ [0, 1]:…”

“…16) using a probabilistic decision rule (Eq. 15) [line [11][12][13][14][15][16][17][18][19][20][21][22]. If the current PM is fully utilized or there are no feasible VMs left to assign to the PM, a new empty PM is taken to fill in [line [14][15][16].…”

Energy Efficient Data Centers

“…Indeed, a number of architectures have recently been proposed for a processor with very low (approximately zero) idle power consumption to reduce the average power consumption in the case of short idle times [16] and [89].…”

“…In the following paragraphs, reference [16] is reviewed as the typical work in this area. This paper presents the aggregate power usage characteristics of a large datacenter for different applications.…”