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, 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 (Cloud²Sim), leveraging the Java in-memory data grid platforms. Cloud²Sim 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. Cloud²Sim 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.