Task deployment recommendation with worker availability

Dong Wei, Senjuti Basu Roy, Sihem Amer-Yahia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study recommendation of deployment strategies to task requesters that are consistent with their deployment parameters: a lower-bound on the quality of the crowd contribution, an upper-bound on the latency of task completion, and an upper-bound on the cost incurred by paying workers. We propose BatchStrat, an optimization-driven middle layer that recommends deployment strategies to a batch of requests by accounting for worker availability. We develop computationally efficient algorithms to recommend deployments that maximize task throughput and pay-off, and empirically validate its quality and scalability.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PublisherIEEE Computer Society
Pages1806-1809
Number of pages4
ISBN (Electronic)9781728129037
DOIs
StatePublished - Apr 2020
Event36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
Duration: Apr 20 2020Apr 24 2020

Publication series

NameProceedings - International Conference on Data Engineering
Volume2020-April
ISSN (Print)1084-4627

Conference

Conference36th IEEE International Conference on Data Engineering, ICDE 2020
Country/TerritoryUnited States
CityDallas
Period4/20/204/24/20

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

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