Elastic Sketch: Adaptive and Fast Network-wide
Measurements
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 506
- Type
- E - Conference contribution
- DOI
-
10.1145/3230543.3230544
- Title of conference / published proceedings
- SIGCOMM 2018 - Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication
- First page
- 561
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- August
- Year of publication
- 2018
- 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
-
8
- Research group(s)
-
-
- Citation count
- 46
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Outcome of a long-standing collaboration with the Chinese Academy of Sciences (http://english.cas.cn/), Peking University, and the Alibaba company, China. This work was published at the most prestigious conference in data communications, with an acceptance rate of 18%, and has just been accepted in IEEE/ACM Transactions on Networking (DOI 10.1109/TNET.2019.2943939). This work enabled multiple extensions published at leading conferences with sub-20% acceptance rates, such as ACM SIGMOD, USENIX ATC, IEEE ICNP, as well as a 200K Alan Turing funded project "Learning-based reactive Internet Engineering" (2019 to 2021).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -