Scheduling Placement-Sensitive BSP Jobs with Inaccurate Execution Time Estimation Article Swipe
Zhenhua Han
,
Haisheng Tan
,
Shaofeng H.-C. Jiang
,
Xiaoming Fu
,
Wanli Cao
,
Francis C. M. Lau
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/infocom41043.2020.9155445
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/infocom41043.2020.9155445
The Bulk Synchronous Parallel (BSP) paradigm is gaining tremendous importance recently because of the pop-ularity of computations such as distributed machine learning and graph computation. In a typical BSP job, multiple workers concurrently conduct iterative computations, where frequent synchronization is required. Therefore, the workers should be scheduled simultaneously and their placement on different computing devices could significantly affect the performance. Simply retrofitting a traditional scheduling discipline will likely not yield the desired performance due to the unique characteristics of BSP jobs. In this work, we derive SPIN, a novel scheduling designed for BSP jobs with placement-sensitive execution to minimize the makespan of all jobs. We first prove the problem approximation hardness and then present how SPIN solves it with a rounding-based randomized approximation approach. Our analysis indicates SPIN achieves a good performance guarantee efficiently. Moreover, SPIN is robust against misestimation of job execution time by theoretically bounding its negative impact. We implement SPIN on a production-trace driven testbed with 40 GPUs. Our extensive experiments show that SPIN can reduce the job makespan and the average job completion time by up to 3× and 4.68×, respectively. Our approach also demonstrates better robustness to execution time misestimation compared with heuristic baselines.
Related Topics
Concepts
Computer science
Testbed
Job shop scheduling
Scheduling (production processes)
Bulk synchronous parallel
Distributed computing
Bounding overwatch
Computation
Rounding
Randomized rounding
Parallel computing
Approximation algorithm
Real-time computing
Mathematical optimization
Algorithm
Schedule
Computer network
Artificial intelligence
Mathematics
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/infocom41043.2020.9155445
- OA Status
- gold
- Cited By
- 12
- References
- 81
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3047610867
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3047610867Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/infocom41043.2020.9155445Digital Object Identifier
- Title
-
Scheduling Placement-Sensitive BSP Jobs with Inaccurate Execution Time EstimationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-01Full publication date if available
- Authors
-
Zhenhua Han, Haisheng Tan, Shaofeng H.-C. Jiang, Xiaoming Fu, Wanli Cao, Francis C. M. LauList of authors in order
- Landing page
-
https://doi.org/10.1109/infocom41043.2020.9155445Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://resolver.sub.uni-goettingen.de/purl?gro-2/135579Direct OA link when available
- Concepts
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Computer science, Testbed, Job shop scheduling, Scheduling (production processes), Bulk synchronous parallel, Distributed computing, Bounding overwatch, Computation, Rounding, Randomized rounding, Parallel computing, Approximation algorithm, Real-time computing, Mathematical optimization, Algorithm, Schedule, Computer network, Artificial intelligence, Mathematics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2023: 4, 2022: 2, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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81Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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