Observing impact of performance variation in cloud games using a chess engine

Cloud computing is growing extensively, and in correlation, so is the number of users. Businesses look at the opportunity of increasing effectiveness and decrease cost, and are moving their infrastructure to the cloud. With such high increase in users, the cloud providers has turned to multitenancy. In which multiple tenants may end up running services or applications on the same physical server. This leads to shared resources, and may contribute to contention for resource allocation between the various services. This contention may result in varying degrees of performance and yield a very unpredictable service. Furthermore one is witnessing parts of the industry taking advantage of the cloud as a platform for hosting games. The mentioned resource contention may impose severe performance deficiency on hosted games and servers running in the cloud. This thesis propose the use of a chess engine as a way of simulating games hosted in a cloud environment where one is looking at observing the possible impact of shared resources and contention between virtual machines. The goal of the the- sis is to map performance variation in the cloud and look at how it impacts the quality of the games, through observing chess matches being played under various conditions. In order to utilize a chess engine in the cloud, a set of frameworks was developed. The frameworks was responsible for hosting and running chess matches, and furthermore analyze the outcomes in order to observe any significant impact related to performance variation and resource contention