Task Deployment Recommendation With Worker Availability
2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020)(2020)
摘要
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.
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关键词
task requesters,deployment parameters,crowd contribution,task completion latency,optimization-driven middle layer,deployment strategies,worker availability,task throughput,task deployment recommendation,BatchStrat,upper-bound
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