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Optimizing Task Scheduling and Resource Allocation in Computing Environments using Metaheuristic Methods

Optimizing system performance in dynamic and heterogeneous environments and the efficient management of computational tasks are crucial. This paper therefore looks at task scheduling and resource allocation algorithms in some depth. The work evaluates five algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FA) and Simulated Annealing (SA) across various workloads achieved by varying the task-to-node ratio. The paper identifies Finish Time and Deadline as two key performance metrics for gauging the efficacy of an algorithm, and a comprehensive investigation of the behaviors of these algorithms across different workloads was carried out. Results from the experiments reveal unique patterns in algorithmic behaviors by workload. In the 15-task and 5-node scenario, the GA and PSO algorithms outclass all others, completing 100 percent of tasks before deadlines, Task 5 was a bane to the ACO algorithm. The study proposes a more extensive system that promotes an adaptive algorithmic approach based on workload characteristics. Numerically, the GA and PSO algorithms triumphed completing 100 percent of tasks before their deadlines in the face of 10 tasks and 5 nodes, while the ACO algorithm stumbled on certain tasks. As it is stated in the study, The above-mentioned system offers an integrated approach to ill-structured problem of task scheduling and resource allocation. It offers an intelligent and aggressive scheduling scheme that runs asynchronously when a higher number of tasks is submitted for the completion in addition to those dynamically aborts whenever system load and utilization cascade excessively. The proposed design seems like full-fledged solution over project scheduling or resource allocation issues. It highlights a detailed method of the choice of algorithms based on semantic features, aiming at flexibility. Effects of producing quantifiable statistical results from the experiments on performance empirically demonstrate each algorithm performed under various settings.

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Publication Date
Sun Dec 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Using Time Series Methods To Modify The Seasonal Variations in the Consumer Price Index

     As is  known that the consumer price index (CPI) is one of the most important  price indices because of its direct effect on the welfare of the individual and his living.

       We have been address the problem of Strongly  seasonal  commodities in calculating  (CPI) and identifying some of the solution.

   We have  used an actual data  for a set of commodities (including strongly seasonal commodities) to calculate the index price by using (Annual Basket With Carry Forward Prices method) . Although this method can be successfully used in the context of seasonal&nbs

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Publication Date
Wed Dec 01 2021
Journal Name
Iraqi Journal Of Science
Molecular diagnosis of Chlamydia trachomatis in infertile Iraqi women using Real time-PCR and comparison with other methods

Chlamydia trachomatis is the most common of negative gram bacteria that cause sexually transmitted diseases. It affects the reproductive system in women, not the symptoms of the disease, but the most serious is the long-term effects of the reproductive system.. out of 100 women were attending different hospitals in Baghdad included the Gynaecology Departments of Women Health Center at Al-Elwyia Obstetrics Hospital . Ibn Al balady Maternity and Children's Hospital , Kamal al-Samarrai hospital Fertility Center infertility treatment and In Vitro Fertilization ( IVF ) (20 control and 80 women with infertility) DNA was extracted from the Endocervical Swabs of all infertili women, to investigate the bacteria by using Real time -PCR technique a

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Publication Date
Fri Apr 01 2011
Journal Name
Al-mustansiriyah Journal Of Science
Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
Publication Date
Wed Aug 31 2022
Journal Name
Iraqi Journal Of Science
Optimal CPU Jobs Scheduling Method Based on Simulated Annealing Algorithm

     Task scheduling in an important element in a distributed system. It is vital how the jobs are correctly assigned for each computer’s processor to improve performance. The presented approaches attempt to reduce the expense of optimizing the use of the CPU. These techniques mostly lack planning and in need to be comprehensive. To address this fault, a hybrid optimization scheduling technique is proposed for the hybridization of both First-Come First-Served (FCFS), and Shortest Job First (SJF). In addition, we propose to apply Simulated Annealing (SA) algorithm as an optimization technique to find optimal job’s execution sequence considering both job’s entrance time and job’s execution time to balance them to reduce the job

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Publication Date
Wed Apr 25 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Local Search Algorithms for Multi-criteria Single Machine Scheduling Problem

   Real life scheduling problems require the decision maker to consider a number of criteria before arriving at any decision. In this paper, we consider the multi-criteria scheduling problem of n jobs on single machine to minimize a function of five criteria denoted by total completion times (∑), total tardiness (∑), total earliness (∑), maximum tardiness () and maximum earliness (). The single machine total tardiness problem and total earliness problem are already NP-hard, so the considered problem is strongly NP-hard.

We apply two local search algorithms (LSAs) descent method (DM) and simulated annealing method (SM) for the 1// (∑∑∑

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Publication Date
Fri Apr 01 2022
Journal Name
Baghdad Science Journal
An Evolutionary Algorithm for Solving Academic Courses Timetable Scheduling Problem

Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating opti

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Publication Date
Tue Aug 15 2023
Journal Name
Al-academy
Reading the visual discourse according to the design structures of the internal environments

The current research dealt with contrastive structures and the culture of reception in the design of interior spaces as embodying a rhetorical aspect that reveals formal values related to the meanings of beauty through the mechanisms of symbolism and interpretation that drives mental behavior and is in harmony with intellectual data and its performance function.
Hence, the research in the first chapter dealt with the research problem, the need for it, and the extent of the necessity that calls for studying contrastive structures in interior design and architecture, and touching and searching for what is the paradox and its representations for the recipient, in which the interior designer plays an active role in presenting the best cre

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Publication Date
Fri Aug 01 2008
Journal Name
2008 First International Conference On The Applications Of Digital Information And Web Technologies (icadiwt)
Hybrid canonical genetic algorithm and steepest descent algorithm for optimizing likelihood estimators of ARMA (1, 1) model

This paper presents a hybrid genetic algorithm (hGA) for optimizing the maximum likelihood function ln(L(phi(1),theta(1)))of the mixed model ARMA(1,1). The presented hybrid genetic algorithm (hGA) couples two processes: the canonical genetic algorithm (cGA) composed of three main steps: selection, local recombination and mutation, with the local search algorithm represent by steepest descent algorithm (sDA) which is defined by three basic parameters: frequency, probability, and number of local search iterations. The experimental design is based on simulating the cGA, hGA, and sDA algorithms with different values of model parameters, and sample size(n). The study contains comparison among these algorithms depending on MSE value. One can conc

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Publication Date
Sat Dec 30 2023
Journal Name
Traitement Du Signal
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