Predictable network computing

Polze, Andreas; Werner, M.; Fohler, Gerhard

doi:10.1109/icdcs.1997.598076

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…Cloud is the latest take on the idea of getting processing resources—as a utility—from somewhere else . Questions on programming models, seamless integration of internal and external services, security, as well as monitoring and (self‐adaptive) management techniques need to be answered for OSs and middleware acting as the foundation of a cloud datacenter.…”

Section: Dynamic Provisioning In the Operating Systemmentioning

confidence: 99%

Trends and challenges in operating systems—from parallel computing to cloud computing

Polze

Tröger

2011

Concurrency and Computation

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SUMMARY Over many decades, advances in computer system design and processor manufacturing have resulted in an ever‐increasing per‐chip transistor count, which, in combination with increased clock frequencies, has led to a tremendous increase in single‐thread performance in desktop and server CPUs. The trend to higher integration in processor manufacturing will continue for the next couple of years. However, the increased transistor count leads to additional compute cores within a CPU, rather than to an increased single‐thread performance. Programming and utilizing these future CPUs impose a number of problems commonly referred to as the multicore challenge. These trends primarily affect server computers, whereas on client systems, a break‐even between users' willingness to pay for compute power and today's CPU implementation seems to be reached. In this paper, we argue that the full exploitation of many‐core architectures on the server demands better support by the operating system. This relates to the support of new application programming models, the seamless integration of internal and external services, security, as well as on monitoring and (self‐adaptive) management of such server environments. Within this paper, we discuss three major trends that will drive the adoption of server operating systems to modern many‐core hardware: dynamic parallelism, dynamic partitioning, and dynamic provisioning. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Dynamic Provisioning In the Operating Systemmentioning

confidence: 99%

Trends and challenges in operating systems—from parallel computing to cloud computing

Polze

Tröger

2011

Concurrency and Computation

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“…However, the results obtained for queue time predictions are still relatively inaccurate, making them inadequate for many metacomputing applications, notably those that perform co-allocation (i.e., that spread across multiple machine schedulers). This has prompted the metacomputing community to ask for the enhancement of supercomputer schedulers by the introduction of reservations 28] or guaranteed computing power 29,52]. Reservations consist of a guarantee that a certain amount of resources is going to be available continuously starting at a pre-determined future time.…”

Section: Scheduling In a Metacomputing Environmentmentioning

confidence: 99%

Benchmarks and Standards for the Evaluation of Parallel Job Schedulers

Chapin

Cirne

Feitelson

et al. 1999

Job Scheduling Strategies for Parallel Processing

121

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The evaluation of parallel job schedulers hinges on the workloads used. It is suggested that this be standardized, in terms of both format and content, so as to ease the evaluation and comparison of different systems. The question remains whether this can encompass both traditional parallel systems and metacomputing systems. This paper is based on a panel on this subject that was held at the workshop, and the ensuing discussion; its authors are both the panel members and participants from the audience. Naturally, not all of us agree with all the opinions expressed here...

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“…Deciding how to map computations to hosts in systems with such dynamically changing loads (what we will call the mapping problem) is a basic problem that arises in a number of important contexts, such as dynamically load-balancing the tasks in a parallel program [24,1,26], and scheduling tasks to meet deadlines in a distributed soft real-time system [15,22,23,17].…”

Section: Introductionmentioning

confidence: 99%

The Statistical Properties of Host Load

Dinda

1999

Scientific Programming

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Understanding how host load changes over time is instrumental in predicting the execution time of tasks or jobs, such as in dynamic load balancing and distributed soft real‐time systems. To improve this understanding, we collected week‐long, 1 Hz resolution traces of the Digital Unix 5 second exponential load average on over 35 different machines including production and research cluster machines, compute servers, and desktop workstations. Separate sets of traces were collected at two different times of the year. The traces capture all of the dynamic load information available to user‐level programs on these machines. We present a detailed statistical analysis of these traces here, including summary statistics, distributions, and time series analysis results. Two significant new results are that load is self‐similar and that it displays epochal behavior. All of the traces exhibit a high degree of self‐similarity with Hurst parameters ranging from 0.73 to 0.99, strongly biased toward the top of that range. The traces also display epochal behavior in that the local frequency content of the load signal remains quite stable for long periods of time (150–450 s mean) and changes abruptly at epoch boundaries. Despite these complex behaviors, we have found that relatively simple linear models are sufficient for short‐range host load prediction.

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Predictable network computing

Cited by 13 publications

References 29 publications

Trends and challenges in operating systems—from parallel computing to cloud computing

Trends and challenges in operating systems—from parallel computing to cloud computing

Benchmarks and Standards for the Evaluation of Parallel Job Schedulers

The Statistical Properties of Host Load

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