The reduction of energy consumption in microprocessors can be accomplished without impacting the peak performance through the use of dynamic voltage scaling (DVS). This approach varies the processor voltage under software control to meet dynamically varying performance requirements. This paper presents a foundation for the simulation and analysis of DVS algorithms. These algorithms are applied to a benchmark suite specifically targeted for PDA devices. 2.

Extensible operating systems allow applications to modify kernel behavior by providing mechanisms for application code to run in the kernel address space. Extensibility enables a system to efficiently support a broader class of applications than is currently supported. This paper discusses the key challenge in making extensible systems practical: determining which parts of the system need to be extended and how. The determination of which parts of the system need to be extended requires self-monitoring, capturing a significant quantity of data about the performance of the system. Determining how to extend the system requires self-adaptation. In this paper, we describe how an extensible operating system (VINO) can use in situ simulation to explore the efficacy of policy changes. This automatic exploration is applicable to other extensible operating systems and can make these systems self-adapting to workload demands.

This paper examines the performance of desktop applications running on the Microsoft Windows NT operating system on Intel x86 processors, and contrasts these applications to the programs in the integer SPEC95 benchmark suite. We present measurements of basic instruction set and program characteristics, and detailed simulation results of the way these programs use the memory system and processor branch architecture. We show that the desktop applications have similar characteristics to the integer SPEC95 benchmarks for many of these metrics. However, compared to the integer SPEC95 applications, desktop applications have larger instruction working sets, execute instructions in a greater number of unique functions, cross DLL boundaries frequently, and execute a greater number of indirect calls.

In this paper, we examine the availability and utility of idle memory in workstation clusters. We attempt to answer the following questions. First, how much of the total memory in a workstation cluster can be expected to be idle? This provides an estimate of the opportunity for hosting guest data. Second, how much memory can be expected to be idle on individual workstations? This helps determine the recruitment policy – how much memory should be recruited on individual hosts? Third, what is the distribution of memory idle-times? This indicates how long guest data can be expected to survive; applications that access their data-sets frequently within the expected life-time of guest data are more likely to benefit from exploiting idle memory. Fourth, how much performance improvement can be achieved for off-the-shelf clusters without customizing the operating system and/or the processor firmware? Finally, how long and how frequently might a user have to wait to reclaim her machine if she volunteers to host guest pages on her machine? This helps answer the question of social acceptability. To answer the questions relating to the availability of idle memory, we have analyzed two-week long traces from two workstation pools with different sizes, locations, and patterns of use. To evaluate the expected benefits and costs, we have simulated five data-intensive applications (0.5 GB-5 GB) on these workstation pools. 1

This paper is intended to catalyze discussions on two intertwined systems topics. First, it presents early results from a latency study of Windows NT that identifies some apecific causes of long thread scheduling latencies, many of which delay the diapatching of runnable threads' for tens of milliseconds'. Reasons for these delays', including technical, methodological, and economic are presented and possible solutions are discussed.

... In this paper, we present a self-configuring, scalable benchmark, that generates a server benchmark load based on actual server loads. In contrast to other web benchmarks, our benchmark characterizes request latency instead of focusing exclusively on throughput sensitive metrics. We present our new benchmark, hbench:Web, and demonstrate how it accurately captures the load observed by an actual server. We then go on to show how it can be used to assess how continued growth or changes in the workload will affect future performance. Using existing log histories, we show that these predictions are sufficiently realistic to provide insight into tomorrow's web performance.

Operating systems are complex and their behavior depends on many factors. Source code, if available, does not directly help one to understand the OS’s behavior, as the behavior depends on actual workloads and external inputs. Runtime profiling is a key technique to prove new concepts, debug problems, and optimize performance. Unfortunately, existing profiling methods are lacking in important areas—they do not provide enough information about the OS’s behavior, they require OS modification and therefore are not portable, or they incur high overheads thus perturbing the profiled OS. We developed OSprof: a versatile, portable, and efficient OS profiling method based on latency distributions analysis. OSprof automatically selects important profiles for subsequent visual analysis. We have demonstrated that a suitable workload can be used to profile virtually any OS component. OSprof is portable because it can intercept operations and measure OS behavior from user-level or from inside the kernel without requiring source code. OSprof has typical CPU time overheads below 4%. In this paper we describe our techniques and demonstrate their usefulness through a series of profiles conducted on Linux, FreeBSD, and Windows, including client/server scenarios. We discovered and investigated a number of interesting interactions, including scheduler behavior, multi-modal I/O distributions, and a previously unknown lock contention, which we fixed.

On the vast majority of today’s computers, the dominant form of computation is GUI-based user interaction. In such an environment, the user’s perception is the final arbiter of performance. Human-factors research shows that a user’s perception of performance is affected by unexpectedly long delays. However, most performance-tuning techniques currently rely on throughput-sensitive benchmarks. While these techniques improve the average performance of the system, they do little to detect or eliminate response-time variabilities—in particular, unexpectedly long delays. We introduce a measurement infrastructure that allows us to improve user-perceived performance by helping us to identify and eliminate the causes of the unexpected long response times that users find unacceptable. We describe TIPME (The Interactive Performance Monitoring Environment), a collection of measurement tools that allowed us to quickly and easily diagnose interactive performance “bugs” in a mature operating system. We present two case studies that demonstrate the effectiveness of our measurement infrastructure. Each of the performance problems we identify drastically affects variability in response time in a mature system, demonstrating that current tuning techniques do not address this class of performance problems.

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Experimental computer systems research typically ignores the end-user, modeling him, if at all, in overly simple ways. We argue that this (1) results in inadequate performance evaluation of the systems, and (2) ignores opportunities. We summarize our experiences with (a) directly evaluating user satisfaction and (b) incorporating user feedback in different areas of client/server computing, and use our experiences to motivate principles for that domain. Specifically, we report on user studies to measure user satisfaction with resource borrowing and with different clock frequencies in desktop computing, the development and evaluation of user interfaces to integrate user feedback into scheduling and clock frequency decisions in this context, and results in predicting user action and system response in a remote display system. We also present initial results on extending our work to user control of scheduling and mapping of virtual machines in a virtualization-based distributed computing environment. We then generalize (a) and (b) as recommendations for incorporating the user into experimental computer systems research.