Fuzzy Based Amalgamated Technique for Optimal Service Time in Distributed Computing System
Dr. Anju Khandelwal, Associate Prof., Department of Mathematics
SRMS College of Engineering & Technology, Bareilly Affiliated to Dr. A. P. J Abdul Kalam Technical University, Lucknow, India
Manuscript received on 07 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 6763-6768 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4783098319/19©BEIESP | DOI: 10.35940/ijrte.C4783.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to the continuous progress of microprocessor technology and computer network, the distributed computing system (DCS) is currently one of the key areas of interest. The distributed computing system [DCS] provides the ability to share better performance and resources. There are a few registering nodes that communicate with one another through the message transient system. The advancement of new technologies in communication and information leads to the development of distributed systems. Task assignment is a critical step in the distributed computing system. For the proper utilization of available enumeration strength, it is necessary to allocate tasks to the processors, whose features are vastly suitable for execution. In this research paper, we have examined a task allocation problem with fuzzy performance time and fuzzy communication time, which is more realistic and general in nature. The problem of fuzzy task allocation is impure and it has been converted into a single number (i.e crisp one) using the fuzzy magnitude ranking method. Here, a serviceable model has been evolved to establish the system’s optimum impedance time by optimal assignment of tasks based on triangular fuzzy execution time and triangular fuzzy communication time processor speed.
Keywords: Fuzzy Assignment, Crisp Value, Magnitude Ranking Method, Task Allocation, Fuzzy Execution Time, Fuzzy Communication Time, Task Response Time.
Scope of the Article: Fuzzy Logics