Charles R. Lefurgy

Energy management for commercial servers

Servers: high-end, multiprocessor systems running commercial workloads, have typically included extensive cooling systems and resided in custom-built rooms for high-power delivery. Recently, as transistor density and demand for computing resources have rapidly increased, even these high-end systems face energy-use constraints. Commercial-server energy management now focuses on conserving power in the memory and microprocessor subsystems. Because their workloads are typically structured as multiple application programs, system-wide approaches are more applicable to multiprocessor environments in commercial servers than techniques that primarily apply to single-application environments, such as those based on compiler optimizations.

Optimal power allocation in server farms

Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly

Server-Level Power Control

4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm so as to get maximum performance. By giving more power to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given power budget, performance can be maximized by operating servers at their highest power levels. However, it is also conceivable that one might prefer to run servers at their lowest power levels, which allows more servers to be turned on for a given power budget. To fully understand the effect of power allocation on performance in a server farm with a fixed power budget, we introduce a queueing theoretic model, which allows us to predict the optimal power allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter. We find that the optimal power allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum power levels. There are scenarios where it might be optimal to run servers at their lowest power levels or at some intermediate power levels. Our analysis shows that the optimal power allocation is non-obvious and depends on many factors such as the power-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lowest attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than we can implement, including arbitrarily large server farms and different power-to-frequency curves. Importantly, we show that the optimal power allocation can significantly improve server farm performance, by a factor of typically 1.4 and as much as a factor of 5 in some cases.

Critical power slope: understanding the runtime effects of frequency scaling

We present a technique that controls the peak power consumption of a high-density server by implementing a feedback controller that uses precise, system-level power measurement to periodically select the highest performance state while keeping the system within a fixed power constraint. A control theoretic methodology is applied to systematically design this control loop with analytic assurances of system stability and controller performance, despite unpredictable workloads and running environments. In a real server we are able to control power over a 1 second period to within 1 W. Additionally, we have observed that power over an 8 second period can be controlled to within 0.1 W. We believe that we are the first to demonstrate such precise control of power in a real server. Conventional servers respond to power supply constraint situations by using simple open-loop policies to set a safe performance level in order to limit peak power consumption. We show that closed-loop control can provide higher performance under these conditions and test this technique on an IBM BladeCenter HS20 server. Experimental results demonstrate that closed-loop control provides up to 82% higher application performance compared to open-loop control and up to 17% higher performance compared to a widely used ad-hoc technique.

On evaluating request-distribution schemes for saving energy in server clusters

Energy efficiency is becoming an increasingly important feature for both mobile and high-performance server systems. Most processors designed today include power management features that provide processor operating points which can be used in power management algorithms. However, existing power management algorithms implicitly assume that lower performance points are more energy efficient than higher performance points. Our empirical observations indicate that for many systems, this assumption is not valid.We introduce a new concept called critical power slope to explain and capture the power-performance characteristics of systems with power management features. We evaluate three systems - a clock throttled Pentium laptop, a frequency scaled PowerPC platform, and a voltage scaled system to demonstrate the benefits of our approach. Our evaluation is based on empirical measurements of the first two systems, and publicly available data for the third. Using critical power slope, we explain why on the Pentium-based system, it is energy efficient to run only at the highest frequency, while on the PowerPC-based system, it is energy efficient to run at the lowest frequency point. We confirm our results by measuring the behavior of a web serving benchmark. Furthermore, we extend the critical power slope concept to understand the benefits of voltage scaling when combined with frequency scaling. We show that in some cases, it may be energy efficient not to reduce voltage below a certain point.

Mambo: a full system simulator for the PowerPC architecture

Power-performance optimization is a relatively new problem area particularly in the context of server clusters. Power-aware request distribution is a method of scheduling service requests among servers in a cluster so that energy consumption is minimized, while maintaining a particular level of performance. Energy efficiency is obtained by powering-down some servers when the desired quality of service can be met with fewer servers. We have found that it is critical to take into account the system and workload factors during both the design and the evaluation of such request distribution schemes. We identify the key system and workload factors that impact such policies and their effectiveness in saving energy. We measure a web cluster running an industry-standard commercial web workload to demonstrate that understanding this system-workload context is critical to performing valid evaluations and even for improving the energy-saving schemes.

Active management of timing guardband to save energy in POWER7

Mambo is a full-system simulator for modeling PowerPC-based systems. It provides building blocks for creating simulators that range from purely functional to timing-accurate. Functional versions support fast emulation of individual PowerPC instructions and the devices necessary for executing operating systems. Timing-accurate versions add the ability to account for device timing delays, and support the modeling of the PowerPC processor microarchitecture. We describe our experience in implementing the simulator and its uses within IBM to model future systems, support early software development, and design new system software.

SHIP: Scalable Hierarchical Power Control for Large-Scale Data Centers

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.

Introducing the Adaptive Energy Management Features of the Power7 Chip

In todays data centers, precisely controlling server power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasingly high server density. While various power control strategies have been recently proposed, existing solutions are not scalable to control the power consumption of an entire large-scale data center, because these solutions are designed only for a single server or a rack enclosure. In a modern data center, however, power control needs to be enforced at three levels: rack enclosure, power distribution unit, and the entire data center, due to the physical and contractual power limits at each level. This paper presents SHIP, a highly scalable hierarchical power control architecture for large-scale data centers. SHIP is designed based on well-established control theory for analytical assurance of control accuracy and system stability. Empirical results on a physical testbed show that our control solution can provide precise power control, as well as power differentiations for optimized system performance. In addition, our extensive simulation results based on a real trace file demonstrate the efficacy of our control solution in large-scale data centers composed of thousands of servers.

TAPO: Thermal-aware power optimization techniques for servers and data centers

Power7 implements several new adaptive power management techniques which, in concert with the EnergyScale firmware, let it proactively exploit variations in workload, environmental conditions, and overall system use to meet customer-directed power and performance goals. These innovative features include per-core frequency scaling with available autonomic frequency control, per-chip automated voltage slewing, power consumption estimation, and hardware instrumentation assist.