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A Simulation Model for Large Scale Distributed Systems

A Simulation Model for Large Scale Distributed Systems

Ciprian Dobre
Copyright: © 2012 |Pages: 35
ISBN13: 9781466601918|ISBN10: 1466601914|EISBN13: 9781466601925
DOI: 10.4018/978-1-4666-0191-8.ch019
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MLA

Dobre, Ciprian. "A Simulation Model for Large Scale Distributed Systems." Simulation in Computer Network Design and Modeling: Use and Analysis, edited by Hussein Al-Bahadili, IGI Global, 2012, pp. 392-426. https://doi.org/10.4018/978-1-4666-0191-8.ch019

APA

Dobre, C. (2012). A Simulation Model for Large Scale Distributed Systems. In H. Al-Bahadili (Ed.), Simulation in Computer Network Design and Modeling: Use and Analysis (pp. 392-426). IGI Global. https://doi.org/10.4018/978-1-4666-0191-8.ch019

Chicago

Dobre, Ciprian. "A Simulation Model for Large Scale Distributed Systems." In Simulation in Computer Network Design and Modeling: Use and Analysis, edited by Hussein Al-Bahadili, 392-426. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0191-8.ch019

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Abstract

The use of discrete-event simulators in the design and development of Large Scale Distributed Systems (LSDSs) is appealing due to their efficiency and scalability. Their core abstractions of process and event map neatly to the components and interactions of modern-day distributed systems and allow designing realistic simulation scenarios. MONARC 2, a multithreaded, process oriented simulation framework designed for modeling LSDSs, allows the realistic simulation of a wide-range of distributed system technologies, with respect to their specific components and characteristics. This chapter presents the design characteristics of the simulation model proposed in MONARC 2. It starts by first analyzing existing work, outlining the key decision points taken in the design of the MONARC’s simulation model. The model includes the necessary components to describe various actual distributed system technologies and provides the mechanisms to describe concurrent network traffic, evaluate different strategies in data replication, and analyze job scheduling procedures.

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