A complex network approach to cloud computing

TL;DR

This paper explores how complex network models like Erdos-Renyi and Barabasi-Albert can optimize server placement in cloud computing, analyzing performance based on communication cost and task distribution.

Contribution

It introduces a novel approach using complex network topologies to model and analyze cloud server performance, including community structures.

Findings

ER topology offers better task balance at lower degrees

BA topology results in lower communication costs

Assigning servers to different communities improves performance

Abstract

Cloud computing has become an important means to speed up computing. One problem influencing heavily the performance of such systems is the choice of nodes as servers responsible for executing the users' tasks. In this article we report how complex networks can be used to model such a problem. More specifically, we investigate the performance of the processing respectively to cloud systems underlain by Erdos-Renyi and Barabasi-Albert topology containing two servers. Cloud networks involving two communities not necessarily of the same size are also considered in our analysis. The performance of each configuration is quantified in terms of two indices: the cost of communication between the user and the nearest server, and the balance of the distribution of tasks between the two servers. Regarding the latter index, the ER topology provides better performance than the BA case for smaller average degrees and opposite behavior for larger average degrees. With respect to the cost, smaller values are found in the BA topology irrespective of the average degree. In addition, we also verified that it is easier to find good servers in the ER than in BA. Surprisingly, balance and cost are not too much affected by the presence of communities. However, for a well-defined community network, we found that it is important to assign each server to a different community so as to achieve better performance.