S. Sridevi*, Jeevaa Katiravan
Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 223-233, 2023, DOI:10.32604/iasc.2023.031928
Abstract Scientific workflows have gained the emerging attention in sophisticated large-scale scientific problem-solving environments. The pay-per-use model of cloud, its scalability and dynamic deployment enables it suited for executing scientific workflow applications. Since the cloud is not a utopian environment, failures are inevitable that may result in experiencing fluctuations in the delivered performance. Though a single task failure occurs in workflow based applications, due to its task dependency nature, the reliability of the overall system will be affected drastically. Hence rather than reactive fault-tolerant approaches, proactive measures are vital in scientific workflows. This work puts forth an attempt to concentrate on… More >
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