2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
In the cloud computing model, cloud providers invoice clients for resource consumption. Hence, tools helping the client to budget the cost of running their application are of pre-eminent importance. However, the opaque and multi-tenant nature of clouds, make job runtimes both variable and hard to predict. In this paper, we propose an improved simulation framework that takes into account this variability using the Monte-Carlo method. We consider the execution of batch jobs on an actual platform, scheduled using typical heuristics based on the user estimates of tasks' runtimes. We model the observed variability through simple distributions to use as inputs to the Monte-Carlo simulation. We show that, our method can capture over 90% of the empirical observations of total execution times.
client-server systems, cloud computing, digital simulation, Monte Carlo methods, processor scheduling
L. Bertot, S. Genaud and J. Gossa, "An Overview of Cloud Simulation Enhancement Using the Monte-Carlo Method," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 386-387.