Workflow Scheduling Techniques And Algorithms In IaaS Cloud: A Survey - Info and Reading Options
By International Journal of Electrical and Computer Engineering (IJECE)
“Workflow Scheduling Techniques And Algorithms In IaaS Cloud: A Survey” Metadata:
- Title: ➤ Workflow Scheduling Techniques And Algorithms In IaaS Cloud: A Survey
- Author: ➤ International Journal of Electrical and Computer Engineering (IJECE)
“Workflow Scheduling Techniques And Algorithms In IaaS Cloud: A Survey” Subjects and Themes:
- Subjects: IaaS cloud - Meta-heuristics - Resource provisioning - Scientific workflows - Workflow scheduling
Edition Identifiers:
- Internet Archive ID: 10.11591ijece.v8i2.pp853-866
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"Workflow Scheduling Techniques And Algorithms In IaaS Cloud: A Survey" Description:
The Internet Archive:
In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers.
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"Workflow Scheduling Techniques And Algorithms In IaaS Cloud: A Survey" is available for download from The Internet Archive in "texts" format, the size of the file-s is: 13.24 Mbs, and the file-s went public at Fri Jul 08 2022.
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