Scheduling parallel DAG jobs online to minimize average flow time

Kunal Agrawal, Jing Li, Kefu Lu, Benjamin Moseley

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

26 Scopus citations


In this work, we study the problem of scheduling parallelizable jobs online with an objective of minimizing average flow time. Each parallel job is modeled as a DAG where each node is a sequential task and each edge represents dependence between tasks. Previous work has focused on a model of parallelizability known as the arbitrary speed-up curves setting where a scalable algorithm is known. However, the DAG model is more widely used by practitioners, since many jobs generated from parallel programming languages and libraries can be represented in this model. However, little is known for this model in the online setting with multiple jobs. The DAG model and the speed-up curve models are incomparable and algorithmic results from one do not immediately imply results for the other. Previous work has left open the question of whether an online algorithm can be O(1)-competitive with O(1)-speed for average flow time in the DAG setting. In this work, we answer this question positively by giving a scalable algorithm which is (1 + ϵ)-speed O(1/3ϵ)-competitive for any ϵ > 0. We further introduce the first greedy algorithm for scheduling parallelizable jobs - our algorithm is a generalization of the shortest jobs first algorithm. Greedy algorithms are among the most useful in practice due to their simplicity. We show that this algorithm is (2 + ϵ)-speed O(1/ϵ4) - competitive for any ϵ > 0.

Original languageEnglish (US)
Title of host publication27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016
EditorsRobert Krauthgamer
PublisherAssociation for Computing Machinery
Number of pages14
ISBN (Electronic)9781510819672
StatePublished - 2016
Externally publishedYes
Event27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016 - Arlington, United States
Duration: Jan 10 2016Jan 12 2016

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms


Other27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • General Mathematics


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