Hazim Abdulameer Fadhil Al-afare
@mtu.edu.iq
Computer Systems dept. / Institute Administration Alrusaffa
Middle Technical University
EDUCATION
Master of Computer Science Lecturer at Middle Technical University
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Human-Computer Interaction, Information Systems, Signal Processing
2
Scopus Publications
Scopus Publications
- Design Model to Improve Task Scheduling in Cloud Computing Based on Particle Swarm Optimization
Almothana Khodar, Liudmila V. Chernenkaya, Iyad Alkhayat, Hazim Abdulameer Fadhil Al-Afare, and Elena N. Desyatirikova
IEEE
Cloud computing is one of the most modern innovations that are growing rapidly in all technical fields because of its advantages in securing multiple resources and high speed in performance. It provides users connected via the Internet with a variety of storage and computing supplies. Cloud computing is both a catalyst and an enabler for major technological developments such as mobile computing, big data, and machine learning. With all these features, task scheduling has become one of the most difficult challenges facing the cloud, It plays a key role in the effectiveness of all cloud computing tools. Scheduling tasks means allocating tasks to the suitable virtual machine so that the course of computation can be executed to satisfy QoS constraints defined by users such as time to finish and expense. Most current optimization algorithms focus on only one aspect. In this paper, Depending on the characteristics of the swarm algorithm we will develop a model to solve the dilemma of task scheduling as well in order to get the fastest time to transfer tasks and the fastest time for execution with the lowest possible expense. In addition to that, we will develop a multi-purpose algorithm based on the PSO method to provide an optimal solution for the proposed model. The experimental consequences of this paper demonstrate that the proposed manner is more efficacious in expediting tasks and reducing expenses. - New Scheduling Approach for Virtual Machine Resources in Cloud Computing based on Genetic Algorithm
Almothana Khodar, Hazim Abdulameer Fadhil Al-Afare, and Iyad Alkhayat
IEEE
In the actual cloud computing environment, the approaches to resource scheduling of virtual machines (VM) are focused only on the current state of a whole system. This paper presents a strategy for scheduling based on a genetic algorithm. In accordance with the historical data and the current state of the system, this strategy calculates in advance its impact on the system after the distribution of the required VM resources. The experimental results demonstrate that the strategy provides a better load distribution and reduces dynamic migration.