trends could make energy a dominant factor in the total cost of ownership.3 Besides the server electricity bill, TCO includes other energy-dependent components such as the cost of energy for the cooling infrastructure and provisioning costs, specifically the data center infrastructure's cost. To a first-order approximation, both cooling and provisioning costs are proportional to the average energy that servers consume, therefore energy efficiency improvements should benefit all energy-dependent TCO components.Efforts such as the Climate Savers Computing Initiative (www.climatesaverscomputing.org) could help lower worldwide computer energy consumption by promoting widespread adoption of high-efficiency power supplies and encouraging the use of power-savings features already present in users' equipment. The introduction of more efficient CPUs based on chip multiprocessing has also contributed positively toward more energy-efficient servers.3 However, long-term technology trends invariably indicate that higher performance means increased energy usage. As a result, energy efficiency must improve as fast as computing performance to avoid a significant growth in computers' energy footprint. SERVERS VERSUS LAPTOPSMany of the low-power techniques developed for mobile devices directly benefit general-purpose servers, including multiple voltage planes, an array of energyefficient circuit techniques, clock gating, and dynamic Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use. Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems. Luiz André Barroso and Urs HölzleGoogle E nergy efficiency, a new focus for general-purpose computing, has been a major technology driver in the mobile and embedded areas for some time. Earlier work emphasized extending battery life, but it has since expanded to include peak power reduction because thermal constraints began to limit further CPU performance improvements.Energy management has now become a key issue for servers and data center operations, focusing on the reduction of all energy-related costs, including capital, operating expenses, and environmental impacts. Many energy-saving techniques developed for mobile devices became natural candidates for tackling this new problem space. Although servers clearly provide many parallels to the mobile space, we believe that they require additional energy-efficiency innovations.In current servers, the lowest energy-efficiency region corresponds to their most common operating mode. Addressing this mismatch will require significant rethinking of components and systems. To that end, we propose that energy proportionality should become a primary design goal. Although our experience in the server space motivates these observations, we believe that energy-proportional computing also will benefit other types of computing devices. DOLLARS & CO 2Recent reports 1,2 h...

Software techniques that tolerate latency variability are vital to building responsive large-scale Web services.

No 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.

No abstract