Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

Efficient Cloud Resource Scheduling for Stochastic Demand with Heterogeneous Cost Models

Authors
Wei Wei, Yang Liu
Corresponding Author
Wei Wei
Available Online November 2016.
DOI
https://doi.org/10.2991/iwama-16.2016.22How to use a DOI?
Keywords
heterogeneous cost models; demand stochasticity; cloud computing; resource scheduling
Abstract
Distributed cloud platforms facilitate service providers to deliver geographically dispersed online services to a large number of users all over the world, while the aggregated user requests introduce stochastic demands for various resources in different cloud data centers. Resource scheduling in cloud platform is of high complexity due to various nonlinear cost models and multidimensional demand stochasticity. Existed scheduling algorithms generally utilize linear cost model, thus is hard to utilize budget efficiently. We proposed an efficient Heterogeneous Cost models oriented cloud Resource Scheduling (HCRS) to address the problem. Experiments results show that HCRS increase revenue by up to 40% than that in previous mean demand based algorithm and is of low enough complexity. Therefore, HCRS can be used as a candidate for scheduling in cloud platforms with heterogeneous cost models.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
6th International Workshop of Advanced Manufacturing and Automation
Part of series
Advances in Economics, Business and Management Research
Publication Date
November 2016
ISBN
978-94-6252-243-5
ISSN
2352-5428
DOI
https://doi.org/10.2991/iwama-16.2016.22How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Wei Wei
AU  - Yang Liu
PY  - 2016/11
DA  - 2016/11
TI  - Efficient Cloud Resource Scheduling for Stochastic Demand with Heterogeneous Cost Models
BT  - 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.22
DO  - https://doi.org/10.2991/iwama-16.2016.22
ID  - Wei2016/11
ER  -