survey

Multiple Workflows Scheduling in Multi-tenant Distributed Systems: A Taxonomy and Future Directions

Authors:

Muhammad H. Hilman,

Maria A. Rodriguez,

Rajkumar BuyyaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 1

Article No.: 10, Pages 1 - 39

https://doi.org/10.1145/3368036

Published: 06 February 2020 Publication History

Abstract

Workflows are an application model that enables the automated execution of multiple interdependent and interconnected tasks. They are widely used by the scientific community to manage the distributed execution and dataflow of complex simulations and experiments. As the popularity of scientific workflows continue to rise, and their computational requirements continue to increase, the emergence and adoption of multi-tenant computing platforms that offer the execution of these workflows as a service becomes widespread. This article discusses the scheduling and resource provisioning problems particular to this type of platform. It presents a detailed taxonomy and a comprehensive survey of the current literature and identifies future directions to foster research in the field of multiple workflow scheduling in multi-tenant distributed computing systems.

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Tao XQian XHan LFan WShi YZhu XLi ZWei SXu R(2024)Key Flow First Prioritized Flow Scheduling Strategy in Multi-Tenant Data CentersIEEE Transactions on Network and Service Management10.1109/TNSM.2024.336414921:3(3264-3277)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3364149
Nguyen T(2024)Holistic cold-start management in serverless computing cloud with deep learning for time seriesFuture Generation Computer Systems10.1016/j.future.2023.12.011153:C(312-325)Online publication date: 16-May-2024
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Multiple Workflows Scheduling in Multi-tenant Distributed Systems: A Taxonomy and Future Directions

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 1

January 2021

781 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3382040

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 06 February 2020

Accepted: 01 October 2019

Revised: 01 September 2019

Received: 01 July 2019

Published in�CSUR�Volume 53, Issue 1

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https://dl.acm.org/doi/10.1109/TNSM.2024.3364149
Nguyen T(2024)Holistic cold-start management in serverless computing cloud with deep learning for time seriesFuture Generation Computer Systems10.1016/j.future.2023.12.011153:C(312-325)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.future.2023.12.011
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https://doi.org/10.1016/j.asoc.2023.111142
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Khaledian NVoelp MAzizi SShirvani M(2024)AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic reviewCluster Computing10.1007/s10586-024-04442-227:8(10265-10298)Online publication date: 1-Nov-2024
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Roy SGharote MRamamurthy APawar ALodha S(2024)Security-Aware Scheduling of Multiple Scientific Workflows in CloudCloud Computing and Services Science10.1007/978-3-031-68165-3_1(1-24)Online publication date: 15-Aug-2024
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Zhang JWang TCheng L(2023)Time-Sensitive and Resource-Aware Concurrent Workflow Scheduling for Edge Computing Platforms Based on Deep Reinforcement LearningApplied Sciences10.3390/app13191068913:19(10689)Online publication date: 26-Sep-2023
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Zhou JBao YJian DWu HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)PDAS: A Practical Distributed ADMM System for Large-Scale Linear Programming Problems at AlipayProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599883(5717-5727)Online publication date: 6-Aug-2023
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