Mehdi Effatparvar

Work place: ECE Department, Ardabil branch, Islamic Azad University, Ardabil, Iran



Research Interests: Distributed Computing, Computer Networks, Operating Systems, Computer systems and computational processes


Mehdi Effatparvar (Ph.D) is member of computer engineering department in Islamic Azad University of Ardabil, His research interests include computer networks, distributed systems and operating systems.

Author Articles
Scheduling in Grid Systems using Ant Colony Algorithm

By Saeed Molaiy Mehdi Effatparvar

DOI:, Pub. Date: 8 Jan. 2014

Task scheduling is an important factor that directly influences the performance and efficiency of the system. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids. This paper presents a new algorithm based on ant colony optimization (ACO) metaheuristic for solving this problem. In this study, a proposed ACO algorithm for scheduling in Grid systems will be presented. Simulation results indicate our ACO algorithm optimizes total response time and also it increase utilization.

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High Performance Scheduling in Parallel Heterogeneous Multiprocessor Systems Using Evolutionary Algorithms

By Mohammad Sadeq Garshasbi Mehdi Effatparvar

DOI:, Pub. Date: 8 Oct. 2013

Scheduling is the process of improving the performance of a parallel and distributed system. Parallel systems are part of distributed systems. Parallel systems refers to the concept of run parallel jobs that can be run simultaneously on several processors. Load balancing and scheduling are very important and complex problems in multiprocessor systems. So that problems are an NP-Complete problems. In this paper, we introduce a method based on genetic algorithms for scheduling and laod balancing in parallel heterogeneous multi-processor systems. The results of the simulations indicate Genetic algorithm for scheduling at in systems is better than LPT, SPT and FIFO. Simualation results indicate Genetic Algorithm reduce total response time and also it increase utilization.

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Designing a Universal Data-Oriented Random Number Generator

By Rasoul Farjami Nezhad Mehdi Effatparvar Mohammad Rahimzadeh

DOI:, Pub. Date: 8 Feb. 2013

Data-oriented is new and applied theory which provides method that models the concepts with data structure. If the concept is modeled by using sufficient data in modeling, required inferences and calculations can be done fast with less complexity. Random variable was modeled with digital probability graph, by using Ahmad Fact and probability density function. Some data-oriented random generators have been presented based on data-oriented approach. In this paper a universal data-oriented random number generator is introduced which is able to generate random numbers with uniform, normal, exponential and chi-square distributions.

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