For five years, we collected annual snapshots of file-system metadata from over 60,000 Windows PC file systems in a large corporation. In this article, we use these snapshots to study temporal changes in file size, file age, file-type frequency, directory size, namespace structure, file-system population, storage capacity and consumption, and degree of file modification. We present a generative model that explains the namespace structure and the distribution of directory sizes. We find significant temporal trends relating to the popularity of certain file types, the origin of file content, the way the namespace is used, and the degree of variation among file systems, as well as more pedestrian changes in size and capacities. We give examples of consequent lessons for designers of file systems and related software.

This paper addresses algorithms for dynamically varying (scaling) CPU speed and voltage in order to save energy. Such scaling is usefill and effective when it is immaterial when a task completes, as long as it meets some deadline. We show how to modify any scaling algorithm to keep performance the same but minimize expected energy consumption. We refer to our approach as PACE (Processor Acceleration to Conserve Energy) since the resulting schedule increases speed as the task progresses. Since PACE depends on the probability distribution of the task's work requirement, we present methods for estimating this distribution and evaluate these methods on a variety of real workloads. We also show how to approximate the optimal schedule with one that changes speed a limited number of times. Using PACE causes very little additional overhead, and yields substantial reductions in CPU energy consumption. Simulations using real workloads show it reduces the CPU energy consumption of previously published algorithms by up to 49.5%, with an average of 20.6%, without any effect on performance.

This paper addresses algorithms for dynamically varying (scaling) CPU speed and voltage in order to save energy. Such scaling is useful and effective when it is immaterial when a task completes, as long as it meets some deadline. We show how to modify any scaling algorithm to keep performance the same but minimize expected energy consumption. We refer to our approach as PACE (Processor Acceleration to Conserve Energy) since the resulting schedule increases speed as the task progresses. Since PACE depends on the probability distribution of the task's work requirement, we present methods for estimating this distribution and evaluate these methods on a variety of real workloads. We also show how to approximate the optimal schedule with one that changes speed a limited number of times. Using PACE causes very little additional overhead, and yields substantial reductions in CPU energy consumption. Simulations using real workloads show it reduces the CPU energy consumption of previously published algorithms by up to 49.5%, with an average of 20.6%, without any effect on performance.

Limiting the energy consumption of computers, especially portables, is becoming increasingly important. Thus, new energy-saving computer components and architectures have been and continue to be developed. Many architectural features have both high performance and low power modes, with the mode selection under software control. The problem is to minimize energy consumption while not significantly impacting the effective performance. We group the software control issues as follows: transition, load-change, and adaptation. The transition problem is deciding when to switch to low-power, reduced-functionality modes. The loadchange problem is determining how to modify the load on a component so that it can make further use of its low-power modes. The adaptation problem is how to create software that allows components to be used in novel, power-saving ways. We survey implemented and proposed solutions to software energy management issues created by existing and suggested hardware innovations.

ABSTRACT-The latency between machines on the Internet can dramatically affect users' experience for many distributed applications. Particularly, in multiplayer online games, players seek to cluster themselves so that those in the same session have low latency to each other. A system that predicts latencies between machine pairs allows such matchmaking to consider many more machine pairs than can be probed in a scalable fashion while users are waiting. Using a far-reaching trace of latencies between players on over 3.5 million game consoles, we designed Htrae, a latency prediction system for game matchmaking scenarios. One novel feature of Htrae is its synthesis of geolocation with a network coordinate system. It uses geolocation to select reasonable initial network coordinates for new machines joining the system, allowing it to converge more quickly than standard network coordinate systems and produce substantially lower prediction error than state-of-the-art latency prediction systems. For instance, it produces 90th percentile errors less than half those of iPlane and Pyxida. Our design is general enough to make it a good fit for other latency-sensitive peer-topeer applications besides game matchmaking.