Algorithms 2012, 5(2), 289-303; doi:10.3390/a5020289
Article

Modeling and Performance Analysis to Predict the Behavior of a Divisible Load Application in a Cloud Computing Environment

Faculty of Information Technology, UAE University, 17551 Al-Maqam, Al-Ain, UAE
* Author to whom correspondence should be addressed.
Received: 10 April 2012; in revised form: 1 May 2012 / Accepted: 2 May 2012 / Published: 11 May 2012
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Abstract: Cloud computing is an emerging technology where IT resources are virtualized to users as a set of a unified computing resources on a pay per use basis. The resources are dynamically chosen to satisfy a user Service Level Agreement and a required level of performance. Divisible load applications occur in many scientific and engineering applications and can easily be mapped to a Cloud using a master-worker pattern. However, those applications pose challenges to obtain the required performance. We model divisible load applications tasks processing on a set of cloud resources. We derive a novel model and formulas for computing the blocking probability in the system. The formulas are useful to analyze and predict the behavior of a divisible load application on a chosen set of resources to satisfy a Service Level Agreement before the implementation phase, thus saving time and platform energy. They are also useful as a dynamic feedback to a cloud scheduler for optimal scheduling. We evaluate the model in a set of illustrative scenarios.
Keywords: distributed systems; divisible load application; scheduling; performance analysis; cloud computing

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MDPI and ACS Style

Ismail, L.; Zhang, L. Modeling and Performance Analysis to Predict the Behavior of a Divisible Load Application in a Cloud Computing Environment. Algorithms 2012, 5, 289-303.

AMA Style

Ismail L, Zhang L. Modeling and Performance Analysis to Predict the Behavior of a Divisible Load Application in a Cloud Computing Environment. Algorithms. 2012; 5(2):289-303.

Chicago/Turabian Style

Ismail, Leila; Zhang, Liren. 2012. "Modeling and Performance Analysis to Predict the Behavior of a Divisible Load Application in a Cloud Computing Environment." Algorithms 5, no. 2: 289-303.

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