research-article

Workflow Scheduling Algorithms for Hard-deadline Constrained Cloud Environments

Authors:

Alexander A. Visheratin,

Mikhail Melnik,

Denis NasonovAuthors Info & Claims

Procedia Computer Science, Volume 80, Issue C

Pages 2098 - 2106

https://doi.org/10.1016/j.procs.2016.05.529

Published: 01 June 2016 Publication History

Abstract

Cloud computational platforms today are very promising for execution of scientific applications since they provide ready to go infrastructure for almost any task. However, complex tasks, which contain a large number of interconnected applications, which are usually called workflows, require efficient tasks scheduling in order to satisfy user defined QoS, like cost or execution time (makespan). When QoS has some restrictions limited cost or deadline scheduling becomes even more complicated. In this paper we propose heuristic algorithm for scheduling workflows in hard-deadline constrained clouds Levelwise Deadline Distributed Linewise Scheduling (LDD-LS) which, in combination with implementation of IC-PCP algorithm, is used for initialization of proposed metaheuristic algorithm Cloud Deadline Coevolutional Genetic Algorithm (CDCGA). Experiments show high efficiency of CDCGA, which makes it potentially applicable for scheduling in cloud environments.

References

[1]

Sinnen, O. (2007). Task scheduling for parallel systems. Wiley-Interscience, 108.

Digital Library

[2]

SCEC Project, Southern California Earthquake Center, http://www.scec.org/.

[3]

Juve, Gideon, Characterizing and profiling scientific workflows, Future Generation Computer Systems, 29.3 (2013) 682-692.

Digital Library

[4]

Bharathi, Shishir, et al. Characterization of scientific workflows. Workflows in Support of Large-Scale Science, 2008. WORKS 2008. Third Workshop on. IEEE, 2008.

[5]

Sakellariou, Rizos, et al. Scheduling workflows with budget constraints. Integrated research in GRID computing. Springer US, 2007. 189-202.

[6]

Frincu, Marc E., and Ciprian Craciun. Multi-objective meta-heuristics for scheduling applications with high availability requirements and cost constraints in multi-cloud environments. Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on. IEEE, 2011.

Digital Library

[7]

A. Visheratin, M. Melnik, N. Butakov, D. Nasonov, Hard-deadline Constrained Workflows Scheduling Using Metaheuristic Algorithms, Procedia Computer Science, 66 (2015) 506-514.

[8]

Fard, Hamid Mohammadi, et al. A multi-objective approach for workflow scheduling in heterogeneous environments. Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012). IEEE Computer Society, 2012.

Digital Library

[9]

R. Marler, Timothy, S. Jasbir, Arora, Survey of multi-objective optimization methods for engineering, Structural and multidisciplinary optimization, 26.6 (2004) 369-395.

[10]

Zhang Fan, Multi-objective scheduling of many tasks in cloud platforms, Future Generation Computer Systems, 37 (2014) 309-320.

[11]

H. Ibarra Oscar, E. Kim. Chul, Heuristic algorithms for scheduling independent tasks on nonidentical processors, Journal of the ACM (JACM), 24.2 (1977) 280-289.

Digital Library

[12]

H. Arabnejad, J.G. Barbosa, R. Prodan, Low-time complexity budgetdeadline constrained workflow scheduling on heterogeneous resources, Future Generation Computer Systems, 55 (2016) 29-40.

Digital Library

[13]

Butakov, N., & Nasonov, D. (2014, October). Co-evolutional genetic algorithm for workflow scheduling in heterogeneous distributed environment. InApplication of Information and Communication Technologies (AICT), 2014 IEEE 8th International Conference, 1-5.

[14]

Abrishami Saeid, Mahmoud Naghibzadeh, H.J. Epema Dick, Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds, Future Generation Computer Systems, 29.1 (2013) 158-169.

Digital Library

[15]

Arabnejad, H. (2013). List Based Task Scheduling Algorithms on Heterogeneous Systems-An overview.

[16]

Yu, J.R. (2008). Workflow scheduling algorithms for grid computing. Metaheuristics for scheduling in distributed computing environments, 173-214.

Cited By

Singh GChaturvedi A(2023)Hybrid modified particle swarm optimization with genetic algorithm (GA) based workflow scheduling in cloud-fog environment for multi-objective optimizationCluster Computing10.1007/s10586-023-04071-127:2(1947-1964)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04071-1
Jamil BIjaz HShojafar MMunir KBuyya R(2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3513002
Jain RSharma N(2022)A quantum inspired hybrid SSA–GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computingCluster Computing10.1007/s10586-022-03740-x26:6(3587-3610)Online publication date: 29-Sep-2022
https://dl.acm.org/doi/10.1007/s10586-022-03740-x
Show More Cited By

Index Terms

Workflow Scheduling Algorithms for Hard-deadline Constrained Cloud Environments

Recommendations

Deadline-constrained algorithms for scheduling of bag-of-tasks and workflows in cloud computing environments
HP3C: Proceedings of the 2nd International Conference on High Performance Compilation, Computing and Communications

Cloud computing is an emerging distributed computing paradigm that solves immense scientific applications through distributing computing resources over the Internet. These applications may have a huge number of tasks that may increase their execution ...
A Deadline Satisfaction Enhanced Workflow Scheduling Algorithm
PDP '11: Proceedings of the 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing

Meeting users' deadline constraint is usually the most important goal of workflow scheduling in Grid environment. In order to consider the dynamism of Grid resource, we adopted a stochastic model to describe dynamic workloads of Grid resources. A ...
Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds

The advent of Cloud computing as a new model of service provisioning in distributed systems encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in ...

Comments

Information & Contributors

Information

Published In

cover image Procedia Computer Science

Procedia Computer Science Volume 80, Issue C

June 2016

2452 pages

ISSN:1877-0509

EISSN:1877-0509

Issue’s Table of Contents

Copyright © The Authors.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 June 2016

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Singh GChaturvedi A(2023)Hybrid modified particle swarm optimization with genetic algorithm (GA) based workflow scheduling in cloud-fog environment for multi-objective optimizationCluster Computing10.1007/s10586-023-04071-127:2(1947-1964)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04071-1
Jamil BIjaz HShojafar MMunir KBuyya R(2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3513002
Jain RSharma N(2022)A quantum inspired hybrid SSA–GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computingCluster Computing10.1007/s10586-022-03740-x26:6(3587-3610)Online publication date: 29-Sep-2022
https://dl.acm.org/doi/10.1007/s10586-022-03740-x
Kaur MKadam SHannoon N(2022)Multi-level parallel scheduling of dependent-tasks using graph-partitioning and hybrid approaches over edge-cloudSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07048-126:11(5347-5362)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00500-022-07048-1
Khan MGhosh SBusari SHuq KDagiuklas TMumtaz SIqbal MRodriguez J(2021)Robust, Resilient and Reliable Architecture for V2X CommunicationsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.308451922:7(4414-4430)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1109/TITS.2021.3084519
Koulouzis SBianchi Rder Linde RWang YZhao Z(2021)SPIRIT: A Microservice-Based Framework for Interactive Cloud Infrastructure PlanningEuro-Par 2021: Parallel Processing Workshops10.1007/978-3-031-06156-1_32(405-416)Online publication date: 30-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-031-06156-1_32
Aziza HKrichen S(2020)A hybrid genetic algorithm for scientific workflow scheduling in cloud environmentNeural Computing and Applications10.1007/s00521-020-04878-832:18(15263-15278)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s00521-020-04878-8
Adhikari MAmgoth TSrirama S(2019)A Survey on Scheduling Strategies for Workflows in Cloud Environment and Emerging TrendsACM Computing Surveys10.1145/332509752:4(1-36)Online publication date: 30-Aug-2019
https://dl.acm.org/doi/10.1145/3325097
Mukhin VVyshnivskyi VKornaga YHerasymenko OBazaka YBazaliy M(2019)Study of the Functioning of the Distributed Computer System with a Resource Control Mechanism Based on a Network-Centric Approach2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS.2019.8924331(100-105)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1109/IDAACS.2019.8924331
Wu WZhu JHuang HXu XZhang Y(2018)Dynamic Idle Time Interval Scheduling for Hybrid Cloud Workflow Management System2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00608(3596-3602)Online publication date: 7-Oct-2018
https://dl.acm.org/doi/10.1109/SMC.2018.00608
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents