Home > Research > Publications & Outputs > START:

Electronic data

  • Tuli et al, START Straggler Prediction, IEEE TSC

    Rights statement: ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 1.81 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • Shreshth Tuli
  • Sukhpal Singh Gill
  • Peter Garraghan
  • Rajkumar Buyya
  • Giuliano Casale
  • Nicholas R. Jennings
Close
<mark>Journal publication date</mark>23/11/2021
<mark>Journal</mark>IEEE Transactions on Services Computing
Pages (from-to)1-1
Publication StatusE-pub ahead of print
Early online date23/11/21
<mark>Original language</mark>English

Abstract

Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are slow running instances that increase the overall response time. Such tasks can significantly impact the system’s Quality of Service (QoS) and the Service Level Agreements (SLA). To combat this issue, there is a need for automatic straggler detection and
mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore heterogeneous host or volatile task characteristics. In this paper, we propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. Our technique analyzes all tasks and hosts based on compute and network resource consumption using an Encoder Long-Short-Term-Memory (LSTM) network. The output of this network is then used to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START in a cloud environment and compare it with state-of-the-art techniques (IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler) in terms of QoS parameters such as energy consumption, execution time, resource contention, CPU utilization and SLA violation rate. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16% and 19%, respectively, compared to the state-of-the-art approaches.

Bibliographic note

©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.