Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/227204
Title: Autonomic fault tolerant scheduling for multiple workflows in cloud environment
Researcher: Bala, Anju
Guide(s): Chana, Inderveer
Keywords: Autonomic Fault Tolerance
Cloud Computing
Computer Science
Workflow Scheduling
University: Thapar Institute of Engineering and Technology
Completed Date: 2015
Abstract: Cloud Computing is becoming an increasingly admired paradigm that owns the characteristics of existing paradigms through strong support for virtualization along with various additional features such as on demand resource provisioning, reduced cost, computing flexibility etc. Most of the scientific communities employ workflow technologies to cope up with the complexity and heterogeneity of large scale scientific applications. As, the scientific workflows need a suitable paradigm for deployment and execution in conjunction with high availability of Cloud services. Thus, Cloud is a current benchmark for effective facilitation of the execution of scientific workflows through flexibility of accessible services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) without allusion to the infrastructure on which these applications are hosted. For the successful execution of the scientific workflows on Clouds, Cloud platform should be able to manage the faults through autonomic fault tolerant approaches during the scheduling of workflow tasks on Cloud resources. Cloud providers also entail efficient scheduling algorithms to schedule these workflows along with autonomic fault tolerant approaches. Although, Cloud Computing technology has evolved but still some of the key challenges like autonomic fault tolerance and workflow scheduling need to be achieved. To achieve the set of challenges for the fault tolerant workflow scheduling, a comprehensive study of workflow scheduling algorithms along with the required set of Quality of Service(QoS) parameters is carried out. In addition, Cloud platforms and workflow engines are extensively explored and metrics relevant to the Cloud services are ascertained. Furthermore, a thorough study of failure prediction approaches, fault tolerant techniques and fault tolerant scheduling has been performed.
Pagination: xiii, 149p.
URI: http://hdl.handle.net/10603/227204
Appears in Departments:Department of Computer Science and Engineering

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