Optimized Contract-based Model for Resource Allocation in Federated Geo-distributed Clouds


In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go modelcontinues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scaleand large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises newchallenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resourcestowards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achievingglobal efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies inlocal resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profitearned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPsto establish resource sharing contracts with individual datacentersapriorifor defined time intervals during a 24 hour time period. Basedon the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithmthat enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency andlocal fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloadsgenerated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharingfairness of the proposed model.

IEEE Transactions on Services Computing.