An Adaptive Distributed Simulator for Cloud and MapReduce Algorithms and Architectures

Scalability and performance are crucial for simulations as much as accuracy is. Due to the limited availability and access to the variety of resources, cloud and MapReduce solutions are often evaluated on simulator platforms. As the complexity of the architectures and algorithms keep increasing, sim...

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Bibliographic Details
Published in:Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing pp. 79 - 88
Main Authors: Kathiravelu, Pradeeban, Veiga, Luis
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2014
Subjects:
Adaptation models
Adaptive Systems
Cloud computing
Cloud Simulations
Computational modeling
Computers
Distributed databases
In-memory Data Grids
Load modeling
MapReduce Simulations
Monitoring
Simulations
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Summary:Scalability and performance are crucial for simulations as much as accuracy is. Due to the limited availability and access to the variety of resources, cloud and MapReduce solutions are often evaluated on simulator platforms. As the complexity of the architectures and algorithms keep increasing, simulations themselves become large and resource-hungry. Simulators can be designed to be adaptive, exploiting the clusters and data-grid platforms. This paper describes the research for the design, development, and evaluation of a complete fully parallel and distributed cloud and MapReduce simulator (Cloud 2 Sim), leveraging the Java in-memory data grid platforms. Cloud 2 Sim provides a concurrent and distributed cloud simulator, by extending Cloud Sim cloud simulator, using Hazel cast in-memory key-value store. It also provides an assessment of the MapReduce implementations of Hazel cast and Infinispan, with means of simulating MapReduce executions. Cloud 2 Sim scales out the cloud and MapReduce simulations to multiple nodes running Hazel cast and Infinispan, based on load. The distributed execution model and adaptive scaling solution could further be leveraged as a general purpose auto-scaler middleware for a multi-tenanted deployment.
DOI:10.1109/UCC.2014.16