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- W2808555215 abstract "With rapidly increasing volumes of data to be processed in modern data analytics, it is commonplace to run multiple data processing jobs with inter-job dependencies in a datacenter cluster, typically as recurring data processing workloads. Such a group of inter-dependent data analytic jobs is referred to as a workflow, and may have a deadline due to its mission-critical nature. In contrast, non-recurring ad-hoc jobs are typically best-effort in nature, and rather than meeting deadlines, it is desirable to minimize their average job turnaround time. The state-of-the-art scheduling mechanisms focused on meeting deadlines for individual jobs only, and are oblivious to workflow deadlines. In this paper, we present FlowTime, a new system framework designed to make scheduling decisions for workflows so that their deadlines are met, while simultaneously optimizing the performance of ad-hoc jobs. To achieve this objective, we first adopt a divide-and-conquer strategy to transform the problem of workflow scheduling to a deadline-aware job scheduling problem, and then design an efficient algorithm that tackles the scheduling problem with both deadline-aware jobs and ad-hoc jobs by solving its corresponding optimization problem directly using a linear program solver. Our experimental results have clearly demonstrated that FlowTime achieves the lowest deadline-miss rates for deadline-aware workflows and 2-10 times shorter average job turnaround time, as compared to the state-of-the-art scheduling algorithms." @default.
- W2808555215 created "2018-06-21" @default.
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- W2808555215 date "2018-07-01" @default.
- W2808555215 modified "2023-09-27" @default.
- W2808555215 title "FlowTime: Dynamic Scheduling of Deadline-Aware Workflows and Ad-Hoc Jobs" @default.
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- W2808555215 doi "https://doi.org/10.1109/icdcs.2018.00094" @default.
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