An analytic performance model of parallel systems that perform N tasks using P processors that can fail

Weerasinghe, G.; Antonios, Imad; Lipsky, Lester

doi:10.1109/nca.2001.962547

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Task level performance, putting N tasks on P processors that can fail is presented in [12], and gives a function analytically derived for performance on machines whose task execution times (for different sized tasks) follow Markov modeling and are derived via simulations.…”

Section: A Prior Workmentioning

confidence: 99%

Concurrent adaptive computing in heterogeneous environments (CACHE)

Duselis

Scherson

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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We introduce a computational framework for concurrent adaptive computing in heterogeneous environments for computationally intensive applications. This framework considers the presence of inter-connected computational resources which are discoverable and a workload which needs to be executed either by concurrent means or on a singular resource. The selection of resources, using a novel measurement of performance, leads to the adaptive inclusion/exclusion of resources to be used in the efficient execution of workload computations. The adaptive approach is that it makes a determination to include a proper subset of resources for system inclusion to execute a workload, which is contrary (except when the subset is allinclusive) to a greedy approach where all the resources are seized for the workload application. The selection of a subset of resources may be more efficient due to the high level of heterogeneity of the resources, where, for some resources, certain resource selections may be detrimental or have no value to send work there. Furthermore, this framework aims to lessen the unpredictability and uncontrollability of heterogeneous systems by using this analysis for resource selection.

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Section: A Prior Workmentioning

confidence: 99%

Concurrent adaptive computing in heterogeneous environments (CACHE)

Duselis

Scherson

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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“…Even though the Base Model (case 0) is presented in [20], a summary of it is included in the next subsection since the new performance models (case 1 to case 3) inherit its methodologies used to derive expressions.…”

Section: Generalized Modelmentioning

confidence: 99%

“…In this paper, we extend our work (the base model) in [20] to develop a generalized analytical performance model to evaluate systems that execute a job consisting of N tasks using P fault-prone processors for the following three additional cases: 1) we allow all the processors to fail, including the ones that are idle; 2) we allow one processor to be fail-safe and do not allow idle processors to fail; 3) we allow one processor to be fail-safe and allow idle processors to fail. This generalized model is also based on a Markov Chain with states representing service, and failure rates with k 0 k P active processors.…”

Section: Introductionmentioning

confidence: 96%

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A generalized analytic performance model of distributed systems that perform N tasks using p fault-p

Weerasinghe

Antonios

Lipsky

2002

Proceedings 16th International Parallel and Distributed Processing Symposium

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A family of Markov models for analyzing the performance of parallel processors that execute a job consisting of N independent tasks using P fault-prone processors is presented in this paper. This study extends our previous study by allowing idle processors to fail, and also by developing performance models to analyze the case where one processor is fail-safe. The models are based on Markov Chains with states representing service, and failure rates with k 0 k P active processors. The task-times and processor failures are exponentially distributed. For each performance model we derive a number of expressions to determine the mean execution time, probability of success, standard deviation, work, and the average number of processor failures, all conditioned on the job finishing successfully. We present results of the models for a range of values of processor failure rates. In particular we analyze the results for N = P and N P .

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Genetic Algorithm Based Scheduling of Meta-Tasks with Stochastic Execution Times in Heterogeneous Computing Systemst1

Doğan

Özgüner

2004

Cluster Computing

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An analytic performance model of parallel systems that perform N tasks using P processors that can fail

Cited by 5 publications

References 10 publications

Concurrent adaptive computing in heterogeneous environments (CACHE)

Concurrent adaptive computing in heterogeneous environments (CACHE)

A generalized analytic performance model of distributed systems that perform N tasks using p fault-p

Genetic Algorithm Based Scheduling of Meta-Tasks with Stochastic Execution Times in Heterogeneous Computing Systemst1

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