Elastic grid resource provisioning with WoBinGO: A parallel framework for genetic algorithm based optimization

2015 ◽  
Vol 42 ◽  
pp. 44-54 ◽  
Author(s):  
Milos Ivanovic ◽  
Visnja Simic ◽  
Boban Stojanovic ◽  
Ana Kaplarevic-Malisic ◽  
Branko Marovic
Keyword(s):  
Genetic Algorithm ◽  
Resource Provisioning ◽  
Grid Resource ◽  
Elastic Grid

Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

2017 ◽  
Vol 26 (1) ◽  
pp. 169-184 ◽  
Author(s):  
Absalom E. Ezugwu ◽  
Nneoma A. Okoroafor ◽  
Seyed M. Buhari ◽  
Marc E. Frincu ◽  
Sahalu B. Junaidu
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Grid Computing ◽  
Resource Selection ◽  
Computing System ◽  
Population Based ◽  
Fine Tuning ◽  
Grid Resource ◽  
Performance Results ◽  
Grid Resources

AbstractThe operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

Formal QoS Policy Based Grid Resource Provisioning Framework

2012 ◽  
Vol 10 (2) ◽  
pp. 249-264 ◽  
Author(s):  
Rajni Aron ◽  
Inderveer Chana
Keyword(s):  
Resource Provisioning ◽  
Grid Resource

scholarly journals Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Monika Kumari ◽  
G. Sahoo
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Resource Provisioning ◽  
Vertical Scaling ◽  
Predator Prey ◽  
Average Completion Time ◽  
Predator Prey Model ◽  
De Algorithm ◽  
Optimal Resource ◽  
And Performance

Cloud is a widely used platform for intensive computing, bulk storage, and networking. In the world of cloud computing, scaling is a preferred tool for resource management and performance determination. Scaling is generally of two types: horizontal and vertical. The horizontal scale connects users’ agreement with the hardware and software entities and is implemented physically as per the requirement and demand of the datacenter for its further expansion. Vertical scaling can essentially resize server without any change in code and can increase the capacity of existing hardware or software by adding resources. The present study aims at describing two approaches for scaling, one is a predator-prey method and second is genetic algorithm (GA) along with differential evolution (DE). The predator-prey method is a mathematical model used to implement vertical scaling of task for optimal resource provisioning and genetic algorithm (GA) along with differential evolution(DE) based metaheuristic approach that is used for resource scaling. In this respect, the predator-prey model introduces two algorithms, namely, sustainable and seasonal scaling algorithm (SSSA) and maximum profit scaling algorithm (MPSA). The SSSA tries to find the approximation of resource scaling and the mechanism for maximizing sustainable as well as seasonal scaling. On the other hand, the MPSA calculates the optimal cost per reservation and maximum sustainable profit. The experimental results reflect that the proposed logistic scaling-based predator-prey method (SSSA-MPSA) provides a comparable result with GA-DE algorithm in terms of execution time, average completion time, and cost of expenses incurred by the datacenter.

Blower Resource Scheduling Based on Elite Genetic Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 591-595 ◽  
Author(s):  
Guang Nian Yang ◽  
Wei Qi ◽  
Jun Zhou
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Scheduling Algorithm ◽  
Sewage Treatment ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Task Completion ◽  
Toxic Substances ◽  
Improved Genetic Algorithm ◽  
Grid Resource

Now, our sewage treatment industry mainly depends on the blower of aeration act as metabolic, absorbed in the toxic substances. Blower resources management is the key issue of sewage treatment. Traditional resource scheduling algorithm exist some defects, for example it can not well meet the quality requirements and can not get the optimal solution. This article gives a new resource scheduling method based on improved genetic algorithm. It achieves grid resource scheduling by using real number encoding and activities point crossover. Experiments show that genetic algorithm can reduce executing time and task completion time, and further improve the scalability of resource scheduling model. This algorithm has stability and high efficiency in grid environment.

Grid Resource Scheduling Strategy Based on Elite DNA Genetic Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 1594-1598
Author(s):  
Sheng Jun Xue ◽  
Wei Qi
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Grid Resource ◽  
Grid Environment ◽  
Grid Resource Scheduling

Traditional resource scheduling algorithm, in grid environment, exist some defects, for example it can not well meet the quality requirements and can not get the optimal solution. This article gives a new resource scheduling method based on improved genetic algorithm. It achieves grid resource scheduling by using real number encoding and activities point crossover. Experiments show that genetic algorithm can reduce executing time and task completion time, and further improve the scalability of resource scheduling model. This algorithm has stability and high efficiency in grid environment.

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