It's hard to share: joint service placement and request scheduling in edge clouds with sharable and non-sharable resources
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 49846696
- Type
- E - Conference contribution
- DOI
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10.1109/ICDCS.2018.00044
- Title of conference / published proceedings
- 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)
- First page
- 365
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
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- 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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4
- Research group(s)
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-
- Citation count
- 29
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This has generated considerable academic impact over a short time. At INFOCOM 2019, four papers cite the work, with two building directly on it. The paper also kickstarted a major collaborative research effort on data analytics in edge clouds between the organisations involved in the paper (Southampton, PSU, IBM), leading first to a successful Dstl/ARL-funded joint proposal on edge cloud resource allocation (DAIS ITA BPP18 Task 4.1, $494k for Southampton), and then to Dr Stein’s Turing AI Fellowship, which considers how crowdsourcing can support data analytics (EPSRC Grant EP/V022067/1, £1.16M; with Dstl and IBM as partners and advisor from PSU).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
