Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2017

Abstract

Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over-and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.

Keywords

Cloud computing, scenario tree reduction, sensitivity analysis, software defined networking, stochastic optimization

Discipline

Databases and Information Systems | Management Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Services Computing

Volume

10

Issue

3

First Page

396

Last Page

409

ISSN

1939-1374

Identifier

10.1109/TSC.2015.2476812

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TSC.2015.2476812

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