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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kebing Wang Zhaojuan Bian Qian Chen |
| Copyright Year | 2015 |
| Description | Author affiliation: Software & Service Group, Intel Corp., Shanghai, China (Kebing Wang; Zhaojuan Bian; Qian Chen) |
| Abstract | Hadoop YARN is evolving to become the de-facto standard that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform. And, there are lots of researches about efficiently managing cluster resources and scheduling parallel jobs over YARN clusters. However, the scheduling of sub-tasks derived from one application doesn't receive enough attention, which still managed by independent data processing engines on YARN. In this paper, we analyze the limitation of sub-task scheduling algorithms of popular data processing engines running on YARN, including MapReduce 2.0, Spark Then, we propose Millipedes: a distributed & set-based sub-task scheduling framework. In Millipedes, YARN's application master dispatches a set of sub tasks to data nodes, and then the local task scheduler of each data node schedules the execution of these sub tasks according to real-time dynamic resource usage. |
| Starting Page | 1597 |
| Ending Page | 1602 |
| File Size | 382250 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479989379 |
| DOI | 10.1109/HPCC-CSS-ICESS.2015.242 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | big data cluster Containers Software Scheduling sub-task scheduler Sparks Resource management distributed & set-based scheduling algorithm Yarn YARN Engines |
| Content Type | Text |
| Resource Type | Article |
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