Self-Adaptive Load Balancing System for Grid Computing

Abstract

Load balancing is necessary for computer network infrastructure services, with the aim of balancing computing resources to achieve optimal load processing time. There are many methods that can be used for load balancing, namely by adjusting the load on the computing resources that are provided. The strategy manages the coordination of each computing resources by looking at their ability. The problem that can arise here is that there is a diverse resource involved, yang. This is related to the problems of heterogeneity, scalability, and adaptability that is the fundamental issue in the process of determining the workload on the infrastructure of computing resources. When this has been many methods that can be used for load balancing techniques, namely by adjusting the load on the computing resources will be provided. The strategy does manage the coordination of each of computing resources by looking at their ability. The problem that can arise here is that there is diversity resource involved, as well as changes in workload that can happen quickly and in real time. This is related to the problems heterogeneity, scalability, and adaptability that is the fundamental issue in the process of determining the workload on the infrastructure of computing resources. This paper introduces the mechanism of load balancing approach, taking into account its ability to adapt to changing dynamic workload, as well as the diversity of computing resources involved. The strategy used is to integrate autonomic computing approach into the component-based software so that it can give birth to the ability of self-adaptive load balancing. Based on the experimental results, the proposed model can handle the workload factor of dynamism and diversity of resource through the description of system scalability. The strategy used is to integrate autonomic computing approach into the component-based software so that it can give birth to the ability of self-adaptive load balancing. Based on the experimental results, the proposed model can handle the workload factor of dynamism and diversity of resource through the description of system scalability. The strategy used is to integrate autonomic computing approach into the component-based software so that it can give birth to the ability of self-adaptive load balancing. Based on the experimental results, the proposed model can handle the workload factor of dynamism and diversity of resource through the description of system scalability.