Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services
- Submitting institution
-
The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 24112185
- Type
- D - Journal article
- DOI
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10.1109/TSC.2015.2499770
- Title of journal
- IEEE Transactions on Services Computing
- Article number
- -
- First page
- 618
- Volume
- 10
- Issue
- 4
- ISSN
- 1939-1374
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- 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
-
1
- Research group(s)
-
-
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work addresses a significant engineering challenge facing cloud services providers: developing intelligent and efficient auto-scaling architectures for cloud-based services, which elevates human intervention while meeting users’ Quality of Services (QoS)/cost demands on industrial scale. The first to develop models for intelligent cloud autoscaling, considering QoS inference, dynamism, conflicts, and trade-offs.
Our results reveal superiority to existing approaches of simplistic formulations. Related contributions received successive invitations to major events in cloud (e.g. IEEE/ACM UCC), featured in IEEE Computer (High impact system magazine) with increased citations, and attracted Huawei’s interest for mobile-cloud autoscaling. TSC is the top Services Computing Journal, CORE-A*.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -