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Task scheduling in cloud computing based on metaheuristic techniques: A review paper
- Source :
- EAI Endorsed Transactions on Cloud Systems, Vol 6, Iss 17 (2020)
- Publication Year :
- 2020
- Publisher :
- European Alliance for Innovation (EAI), 2020.
-
Abstract
- Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria.
- Subjects :
- Resource scheduling
task scheduling
lcsh:Computer engineering. Computer hardware
Computer science
business.industry
optimization criteria
Distributed computing
cloud computing
Provisioning
Cloud computing
lcsh:TK7885-7895
lcsh:QA75.5-76.95
Scheduling (computing)
Software
resource scheduling
meta-heuristic techniques
scheduling
lcsh:Electronic computers. Computer science
business
Time complexity
Metaheuristic
Subjects
Details
- Language :
- English
- ISSN :
- 24106895
- Volume :
- 6
- Issue :
- 17
- Database :
- OpenAIRE
- Journal :
- EAI Endorsed Transactions on Cloud Systems
- Accession number :
- edsair.doi.dedup.....577654f5307b58d549004e2cb149d82f