Ouyang, X, Garraghan, P, McKee, D et al. (2 more authors) (2016) Straggler Detection in Parallel Computing Systems through Dynamic Threshold Calculation. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA). The 30th IEEE International Conference on Advanced Information Networking and Applications (AINA-2016), 23-25 Mar 2016, Crans-Montana, Switzerland. IEEE , pp. 414-421. ISBN 978-1-5090-1857-4
Abstract
Cloud computing systems face the substantial challenge of the Long Tail problem: a small subset of straggling tasks significantly impede parallel jobs completion. This behavior results in longer service response times and degraded system utilization. Speculative execution, which create task replicas at runtime, is a typical method deployed in large-scale distributed systems to tolerate stragglers. This approach defines stragglers by specifying a static threshold value, which calculates the temporal difference between an individual task and the average task progression for a job. However, specifying static threshold debilitates speculation effectiveness as it fails to consider the intrinsic diversity of job timing constraints within modern day Cloud computing systems. Capturing such heterogeneity enables the ability to impose different levels of strictness for replica creation while achieving specified levels of QoS for different application types. Furthermore, a static threshold also fails to consider system environmental constraints in terms of replication overheads and optimal system resource usage. In this paper we present an algorithm for dynamically calculating a threshold value to identify task stragglers, considering key parameters including job QoS timing constraints, task execution characteristics, and optimal system resource utilization. We study and demonstrate the effectiveness of our algorithm through simulating a number of different operational scenarios based on real production cluster data against state-of-the-art solutions. Results demonstrate that our approach is capable of creating 58.62% less replicas under high resource utilization while reducing response time up to 17.86% for idle periods compared to a static threshold.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Keywords: | Long Tail Problem; Stragglers; Speculative Execution; Service QoS; Resource Utilization |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jun 2016 11:22 |
Last Modified: | 16 Nov 2016 01:59 |
Published Version: | http://dx.doi.org/10.1109/AINA.2016.84 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/AINA.2016.84 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100522 |