A Study on Nature Inspired Task Scheduling Algorithms in Cloud Environment

Authors

  • N. Deepika Research Scholar, Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, Tamil Nadu, India
  • O. S. Abdul Qadir Assistant Professor, Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajcst-2019.8.S2.2019

Keywords:

Cloud Computing, Task Scheduling, Nature Inspired Algorithm

Abstract

Cloud computing is an encouraging paradigm which offers resources to customers on their demand with least cost. Task scheduling is the key difficult in cloud computing which decreases the performance of the system. To develop performance of the system, there is necessity of an effective task-scheduling algorithm. Nature inspired computing is a technique that is inspired by practices detected from nature. These computing techniques led to the growth of algorithms called Nature Inspired Algorithms (NIA). These algorithms are theme of computational intelligence. The persistence of raising such algorithms is to enhance engineering problems. Nature inspired algorithms have enlarged huge popularity in recent years to challenge hard real world (NP hard and NP complete) problems and resolve complex optimization functions whose actual solution doesn’t occur. The paper presents a complete review of 12 nature inspired algorithms. This study offers the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of essential input parameters, their key evolutionary strategies and application areas. This study would support the research community to recognize what all algorithms could be observed for big scale global optimization to overwhelm the problem of ‘curse of dimensionality’.

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Published

09-03-2019

How to Cite

Deepika, N., & Abdul Qadir, O. S. (2019). A Study on Nature Inspired Task Scheduling Algorithms in Cloud Environment. Asian Journal of Computer Science and Technology, 8(S2), 79–82. https://doi.org/10.51983/ajcst-2019.8.S2.2019