A survey study on task scheduling schemes for workflow executions in cloud computing environment: classification and challenges

Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their p...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 80; no. 7; pp. 9384 - 9437
Main Author: Hosseini Shirvani, Mirsaeid
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2024
Springer Nature B.V
Subjects:
Algorithms
Classification
Cloud computing
Compilers
Computer centers
Computer Science
Energy consumption
Heuristic
Interpreters
Keywords
Literature reviews
Objectives
Processor Architectures
Programming Languages
Resource utilization
Scheduling
Task scheduling
Taxonomy
Utility computing
Workflow software
United States > US
Cloud computing
List schedulers
Task scheduling algorithm
Meta-heuristic algorithms
Directed acyclic graph (DAG)
Workflow application
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary:Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user’s quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders’ requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05806-y