Power management is a key component of modern data center design. Power managers must (1) ensure the costand energy-efficient utilization of the data center infrastructure, (2) maintain availability of the services provided by the center, and (3) address environmental concerns associated with the center's power consumption. While several power management techniques have been proposed and deployed in production data centers, there are still many challenges to comprehensive data center power management. This is particularly true in public cloud environments, where different jobs have different priority levels, and where high availability is critical. One example of the challenges facing public cloud data centers involves power capping. As power delivery must be highly reliable and tolerate wide variation in the load drawn by the data center components, the power infrastructure (e.g., power supplies, circuit breakers, UPS) has high redundancy and overprovisioning. During normal operation (i.e., typical server power demands, and no failures in the center), the power infrastructure is significantly underutilized. Power capping is a common solution to reduce this underutilization, by allowing more servers to be added safely (i.e., without power shortfalls) to the existing power infrastructure, and throttling power consumption in the infrequent cases where the demanded power exceeds the provisioned power capacity to avoid shortfalls. However, state-of-the-art power capping solutions are (1) not directly applicable to the redundant power infrastructure used in highly-available data centers; and (2) oblivious to differing workload priorities across the entire center when power consumption needs to be throttled, which can unnecessarily slow down high-priority work. To address this need, we develop CapMaestro, a new power management architecture with three key features for public cloud data centers. First, CapMaestro is designed to work with multiple power feeds (i.e., sources), and exploits server-level power capping to independently cap the load on each feed of a server. Second, CapMaestro uses a scalable, global priority-aware power capping approach, which accounts for power capacity at each level of the power distribution hierarchy. It exploits the underutilization of commonly-employed redundant power infrastructure at each level of the hierarchy to safely accommodate a much greater number of servers. Third, CapMaestro exploits stranded power (i.e., power budgets that are not utilized) in redundant power infrastructure to boost the performance of workloads in the data center. We add CapMaestro to a real cloud data center control plane, and demonstrate the effectiveness of all three key features. Using a large-scale data center simulation, we demonstrate that CapMaestro significantly and safely increases the number of servers for existing infrastructure. We also call out other key technical challenges the industry faces in data center power management.

There are not yet practical and accurate ways to directly measure core power in a microprocessor. This limits the granularity of measurement and control for computer power management. We overcome this limitation by presenting an accurate runtime per-core power proxy which closely estimates true core power. This enables new fine-grained microprocessor power management techniques at the core level. For example, cloud environments could manage and bill virtual machines for energy consumption associated with the core. The power model underlying our power proxy also enables energy-efficiency controllers to perform what-if analysis, instead of merely reacting to current conditions. We develop and validate a methodology for accurate power proxy training at both chip and core levels. Our implementation of power proxies uses on-chip logic in a high-performance multi-core processor and associated platform firmware. The power proxies account for full voltage and frequency ranges, as well as chip-to-chip process variations. For fixed clock frequency operation, a mean unsigned error of 1.8% for fine-grained 32ms samples across all workloads was achieved. For an interval of an entire workload, we achieve an average error of-0.2%. Similar results were achieved for voltage-scaling scenarios, too. We also present two sample applications of the power proxy: (1) per-core power billing for cloud computing services, and (2) simultaneous runtime energy saving comparisons among different power management policies without running each policy separately.

Microprocessor voltage levels include substantial margin to deal with process variation, system power supply variation, workload induced thermal and voltage variation, aging, random uncertainty, and test inaccuracy. This margin allows the microprocessor to operate correctly during worst-case conditions, but during typical conditions it is larger than necessary and wastes energy. We present a mechanism that reduces excess voltage margin by (1) introducing a critical path monitor (CPM) circuit that measures available timing margin in real-time, (2) coupling the CPM output to the clock generation circuit to adjust clock frequency within cycles in response to excess or inadequate timing margin, and (3) adjusting the processor voltage level periodically in firmware to achieve a specified average clock frequency target. We implemented this mechanism in a prototype IBM POWER7 server. During better-than-worst case conditions our guardband management mechanism reduces the average voltage setting 137-152 mV below nominal, resulting in average processor power reduction of 24% with no performance loss while running industry-standard benchmarks.

This demonstration presents a power panel level power monitoring and management (PMM) system developed at IBM Research. The ultimate goal of this project is to develop a low-cost, high accuracy, non-intrusive and retrofittable data center power management system.

The POWER7 processor is the newest member of the IBM POWER®family of server processors. With greater than 4X the peak performance and the same power budget as the previous generation POWER6®, POWER7 will deliver impressive energy-efficiency boosts. The improved peak energy-efficiency is accompanied by a wide array of new features in the processor and system designs that advance IBM's EnergyScaleTMdynamic power management methodology. This paper provides an overview of these new features, which include better sensing, more advanced power controls, improved scalability for power management, and features to address the diverse needs of the full range of POWER servers from blades to supercomputers. We also highlight three challenges that need attention from a range of systems design and research teams: (i) power management in highly virtualized environments, (ii) power (in)efficiency of systems software and applications, and (iii) memory power costs, especially for servers with large memory footprints.

The processor and cooling subsystems of high-performance servers consume a significant portion of total system power. In this paper, we use the server energy-efficiency benchmark SPECpower_ssj2008 to assess dynamic power management strategies for these sub-systems on an IBM POWER 750 platform.