Rationalizing Resource Utilization in Cloud Computing Using Coalition Formation Strategy

Abstract

Even though the contribution of cloud computing towards the Sustainable Development (SD) of communities is still under research investigation, cloud computing has become an integral part of many ICT solutions that shape our daily lives. Thus, some researchers recommend taking considerable actions to point cloud computing development towards supporting SD. In this research, an approach to designing energy efficient cloud architecture as a way of supporting SD is proposed. Resource allocation is a challenging process in cloud management, the goal is to allocate the exact amount of resources needed throughout the service duration; tight enough to avoid unnecessarily wasting resources and loose enough to prevent any degradation in Quality of Service (QoS) that may lead to the violation of the Service Level Agreement (SLA) between the service provider and the cloud user. This study aims to achieve the desired balance by benefiting from the history of the user’s behaviour and from sharing resources – more specifically Virtual Machines (VM) – among a coalition of users. Coalition formation strategy is used to build groups of cloud users based on their cloud behaviour history. Users are grouped in a way that their usage patterns complement each other, either to avoid the loss stemming from VM excess reserved space or from idle times. A type of architecture that fulfils this improvement process is proposed and implemented on Google Compute Engine (GCE). The contribution of this research is that it applies the Coalition formation strategy in cloud computing resource management in a novel way and experiments show that there are scenarios where the efficiency of resource management has improved. Evaluation of the performance of the proposed architecture is done by comparing resource utilization for both the cloud following this architecture and the cloud that runs the basic GCE strategy. In conclusion, it is observed that improvements depend on accuracy of the prediction of usage pattern of the user. Results show that in certain scenarios, improvements can be made to up to 24% of VM usage and, in other scenarios, it can minimize the number of required VMs, thus contributing to green computing.