sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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Reciprocal Resource Fairness: Towards Cooperative Multiple-Resource Fair Sharing in IaaS Clouds

SESSION: Cloud Computing II


TIME: 4:00PM - 4:30PM

SESSION CHAIR: Jennifer M. Schopf

AUTHOR(S):Haikun Liu, Bingsheng He



Resource sharing in virtualized environments have been demonstrated significant benefits to improve application performance and resource/energy efficiency. However, resource sharing, especially for multiple types, poses several severe and challenging problems in pay-as-you-use cloud environments, such as sharing incentive, economic fairness and free-riding. To address these problems, we propose Reciprocal Resource Fairness (RRF), a novel resource allocation mechanism to enable fair sharing multiple types of resources among multiple tenants in new-generation clouds. RRF implements two complementary and hierarchical mechanisms for resource sharing: inter-tenant resource trading and intra-tenant weight adjustment. We show that RRF satisfies several highly desirable properties to ensure fairness. Experimental results show RRF is promising for both cloud providers and tenants. Compared to the current cloud models, RRF improves VM density and cloud providers’ revenue by 2.2X. For tenants, RRF improves application performance by 45% and guarantees 95% economic fairness among multiple tenants.

Chair/Author Details:

Jennifer M. Schopf (Chair) - Indiana University

Haikun Liu - Nanyang Technological University

Bingsheng He - Nanyang Technological University

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society