Abstract
Network-aware resource allocation aims to ensure the performance of user’s applications as well as the efficiency of overall resource usage inside cloud data centers. Traditional resource allocating algorithms do not take into account the interior properties of the cloud network and user’s personalized requirements. In this paper, we present an optimized network-aware resource allocation technique, in which the hierarchical network topology characteristics of the data center are taken into consideration and utilized to further minimize the maximum latency in communication between VMs as well as the overall network costs. We incorporate the resource heterogeneities by including the computational and communication requirements in the proposed technique. Simulation results indicate that our approach can improve the effectiveness of the resource allocation procedure significantly with respect to the other traditional resource allocation mechanisms.
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Acknowledgements
This work is based on research sponsored by the Office of the Assistant Secretary of Defense for Research and Engineering (OASD(R&E)) under agreement number FAB750-15-2-0120. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Office of the Assistant Secretary of Defense for Research and Engineering (OASD(R&E)) or the US Government. This work is also supported in part by an ARO grant W911NF-12-1-0055, National Science Foundation (NSF) Grant HRD-1137466, Department of Homeland Security (DHS) SLA grant 2010-ST-062-0000041 and 2014-ST-062-000059.
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Shetty, S., Yuchi, X., Song, M. (2016). Optimizing Network-Aware Resource Allocation in Cloud Data Centers. In: Moving Target Defense for Distributed Systems. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-31032-9_4
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DOI: https://doi.org/10.1007/978-3-319-31032-9_4
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