Abstract
In this paper we present the Grid Scheduling Simulator (GSSIM), a comprehensive and advanced simulation tool for distributed computing problems. Based on a classification of simulator features proposed in the paper, we define problems that can be simulated using GSSIM and compare it to other simulation tools. We focus on an extension of our previous works including advanced workload generation methods, simulation of a network with advance reservation features, handling specific application performance models and energy efficiency modeling. Some important features of GSSIM are demonstrated by three diverse experiments conducted with the use of the tool. We also present an advanced web tool for the remote management and execution of simulation experiments, which makes GSSIM the comprehensive distributed computing simulator available on the Web.
Highlights
Scheduling algorithms in distributed computing systems have been the subject of intensive research over the last decade
We introduced the Grid Scheduling Simulator (GSSIM) which provides an automated framework for the management of experiments related to resource management in distributed computing environments [17]
We review several approaches to simulation of distributed computing systems based on the proposed classification of simulator features
Summary
Scheduling algorithms in distributed computing systems have been the subject of intensive research over the last decade. In consequence, setting up an experiment requires a lot of work and is rarely applicative by other researchers To address these issues, we introduced the Grid Scheduling Simulator (GSSIM) which provides an automated framework for the management of experiments related to resource management in distributed computing environments [17]. Bak et al / GSSIM – A tool for distributed computing experiments teractive visualization of results In this way, GSSIM provides a comprehensive environment enabling researchers to test resource management algorithms and architectures, and to exchange workloads and results of experiments and implementations of algorithms. It discusses the simulated architecture, introduces the workload management concept, explains how to incorporate a specific application performance model into simulations, describes an extension for flow-based simulation of network with advance reservations, presents energy-efficiency modeling and demonstrates an advanced web interface for remote management of experiments.
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