Optimized Contract-Based Model for Resource Allocation in Federated Geo-Distributed Clouds

• Published in: IEEE transactions on services computing Vol. 14; no. 2; pp. 530 - 543
• Main Authors: Xu, Jinlai, Palanisamy, Balaji
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Computational modeling; Contracts; Data centers; Data models; Federated cloud; geo-distributed cloud; Provisioning; Resource allocation; Resource management; resource sharing; resource sharing contracts; Response time (computers); Scheduling; Servers
• ISSN: 1939-1374; 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2018.2797910

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