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Issue No.03 - March (2013 vol.24)
pp: 464-478
Sheng Di , The University of Hong Kong, Hong Kong
Cho-Li Wang , The University of Hong Kong, Hong Kong
By leveraging virtual machine (VM) technology which provides performance and fault isolation, cloud resources can be provisioned on demand in a fine grained, multiplexed manner rather than in monolithic pieces. By integrating volunteer computing into cloud architectures, we envision a gigantic self-organizing cloud (SOC) being formed to reap the huge potential of untapped commodity computing power over the Internet. Toward this new architecture where each participant may autonomously act as both resource consumer and provider, we propose a fully distributed, VM-multiplexing resource allocation scheme to manage decentralized resources. Our approach not only achieves maximized resource utilization using the proportional share model (PSM), but also delivers provably and adaptively optimal execution efficiency. We also design a novel multiattribute range query protocol for locating qualified nodes. Contrary to existing solutions which often generate bulky messages per request, our protocol produces only one lightweight query message per task on the Content Addressable Network (CAN). It works effectively to find for each task its qualified resources under a randomized policy that mitigates the contention among requesters. We show the SOC with our optimized algorithms can make an improvement by 15-60 percent in system throughput than a P2P Grid model. Our solution also exhibits fairly high adaptability in a dynamic node-churning environment.
Vectors, Resource management, Equations, Heuristic algorithms, System-on-a-chip, Convex functions, Protocols, P2P multiattribute range query, Cloud computing, VM-multiplexing resource allocation, convex optimization
Sheng Di, Cho-Li Wang, "Dynamic Optimization of Multiattribute Resource Allocation in Self-Organizing Clouds", IEEE Transactions on Parallel & Distributed Systems, vol.24, no. 3, pp. 464-478, March 2013, doi:10.1109/TPDS.2012.144
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