A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment
- Submitting institution
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The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1134
- Type
- D - Journal article
- DOI
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10.1109/tpds.2015.2402655
- Title of journal
- IEEE Transactions on Parallel and Distributed Systems
- Article number
- -
- First page
- 305
- Volume
- 27
- Issue
- 2
- ISSN
- 1045-9219
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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5
- Research group(s)
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C - Communications and Networking (Comms)
- Citation count
- 63
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper was the first to conceptualise achieving overall load balancing in a long-term process rather than the short-term scheduling-cycle-based approach in the current literature. It also achieves balanced task deployment with a global (rather than local) search capability; achieving higher success rate of task deployment, improved network performance by 60% on average. Significantly, it opens a new door for networked cloud environments in terms of service provisioning and consequently a new way of pricing services with more precision and scalably. The work enables the creation of new business models and underpinned subsequent developments in the EU FP7 project MONICA.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -