An Adaptive Design Pattern for Genetic Algorithm Based Autonomic Computing System

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.

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Minimizing Response Time in an Autonomic Computing System Using Proportional Control

Communications in Computer and Information Science - Recent Trends in Networks and Communications ◽

10.1007/978-3-642-14493-6_7 ◽

2010 ◽

pp. 60-67

Author(s):

Harish S. Venkatarama ◽

Kandasamy Chandra Sekaran

Keyword(s):

Response Time ◽

Autonomic Computing ◽

Computing System ◽

Proportional Control

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Energy Aware Grid Scheduling for Dependent Task Using Genetic Algorithm

International Journal of Distributed Systems and Technologies ◽

10.4018/ijdst.2016040102 ◽

2016 ◽

Vol 7 (2) ◽

pp. 18-36

Author(s):

Shiv Prakash ◽

Deo Prakash Vidyarthi

Keyword(s):

Genetic Algorithm ◽

Energy Consumption ◽

Job Scheduling ◽

Computing System ◽

Computational Grid ◽

Task Execution ◽

Energy Aware ◽

Optimal Energy ◽

Proposed Model ◽

Dynamic Voltage

Consumption of energy in the large computing system is an important issue not only because energy sources are depleting fast but also due to the deteriorating environmental conditions. A computational grid is a large heterogeneous distributed computing platform which consumes enormous energy in the task execution. Energy-aware job scheduling, in the computational grid, is an important issue that has been addressed in this work. If the tasks are properly scheduled, keeping the optimal energy concern, it is possible to save the energy consumed by the system in the task execution. The prime objective, in this work, is to schedule the dependent tasks of a job, on the grid nodes with optimal energy consumption. Energy consumption is estimated with the help of Dynamic Voltage Frequency Scaling (DVFS). Makespan, while optimizing the energy consumption, is also taken care of in the proposed model. GA is applied for the purpose and therefore the model is named as Energy Aware Genetic Algorithm (EAGA). Performance evaluation of the proposed model is done using GridSim simulator. A comparative study with other existing models viz. min-min and max-min proves the efficacy of the proposed model.

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