Analysis, modeling and simulation of workload patterns in a large-scale utility cloud
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 154337422
- Type
- D - Journal article
- DOI
-
10.1109/TCC.2014.2314661
- Title of journal
- IEEE Transactions on Cloud Computing
- Article number
- -
- First page
- 208
- Volume
- 2
- Issue
- 2
- ISSN
- 2168-7161
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2014
- 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
-
3
- Research group(s)
-
D - Distributed Systems
- Citation count
- 75
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This highly cited paper provides detailed insights into real-world operational user and software behaviour from production Cloud datacentres operated by Google. The work has had major impact within academia and industry from its dissemination into many sub-disciplines of Cloud research and invited key notes at Universities and companies (Alibaba, Huawei). The work’s methodology resulted in an international patent, £150,000 funding from an EPSRC Impact Acceleration Award, and is the core technology of a Cloud analytics University spin-out Edgetic Ltd. securing £1.6m VC funding. The paper is published within IEEE Transactions on Cloud Computing, a top venue for Cloud computing research.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
