Nasim Soltani Soulegan

Work place: Department of Software Engineering, Allame Naeini Higher Education Institute, Naein, Isfahan, Iran



Research Interests: Data Mining,


Nasim Soltani Soulegan received her B.Sc. Degreein Computer engineering from Payam Noor University, and her M.Sc. degree from Allame Naeini Higher Education Institute, in 2013 and 2016 respectively. She has published several papers in Cloud Computing field in many conferences. Her main research interests include Cloud Computing, Task Scheduling, Distributed Systems, and Data Mining.

Author Articles
MTC: Minimizing Time and Cost of Cloud Task Scheduling based on Customers and Providers Needs using Genetic Algorithm

By Nasim Soltani Soulegan Behrang Barekatain Behzad Soleimani Neysiani

DOI:, Pub. Date: 8 Apr. 2021

Cloud computing is considered a pattern for distributed and heterogeneous computing derived from many resources, and requests aim to share resources. Recently, cloud computing is graded among the top best technologies globally, which must be scheduled favorably to maximize providers’ profit and improve service quality for their customers. Scheduling specifies how users’ requests are assigned to virtual machines, and it plays a vital role in the efficiency and capability of the system. Its objective is to have a throughput or complete jobs in minimum time and the highest standard. Scheduling jobs in heterogeneous distributed systems is an NP-hard polynomial indecisive problem that is not solvable in polynomial time for real-time scheduling. The time complexity of jobs is growing exponentially, and this problem has a considerable effect on the quality of cloud services and providers’ efficiencies. The optimization of scheduling-related parameters using heuristic and meta-heuristic algorithms can reduce the search space complexity and execution time. This study intends to represent a fitness function to minimize time and cost parameters. The proposed method uses a multi-purposed weighted genetic algorithm that provides six basic parameters: utility, task execution cost, response time, wait time, Makespan, and throughput to provide comprehensive optimization. The proposed approach improved response and wait times, throughput, Makespan, and utility 16, 9, 7, 8 percentages, respectively, by only a one cost unit reduction, which is dispensable. As a result, both providers and users will experience better services. The statistical tests show that the achieved improvement is valid for 94% of experiments.

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