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Load Balancing in Computational Grids Using Ant Colony Optimization Algorithm
- International Journal of Computer & Communication Technology (IJCCT
, 2012
"... Abstract -Grid computing is the combination of computer resources from multiple administrative domains for a common goal. Load balancing is one of the critical issues that must be considered in managing a grid computing environment. It is complicated due to the distributed and heterogeneous nature ..."
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Abstract -Grid computing is the combination of computer resources from multiple administrative domains for a common goal. Load balancing is one of the critical issues that must be considered in managing a grid computing environment. It is complicated due to the distributed and heterogeneous nature of the resources. An Ant Colony Optimization algorithm for load balancing in grid computing is proposed which will determine the best resource to be allocated to the jobs, based on resource capacity and at the same time balance the load of entire resources on grid. The main objective is to achieve high throughput and thus increase the performance in grid environment.
M.: A modular middleware for high-level dynamic network management
- In: Proceedings of the 1st workshop on Middlewareapplication interaction: in conjunction with Euro-Sys 2007
, 2007
"... In addition to the control and supervision of components and connections, network management middlewares are required to enhance reliability and efficiency of distributed systems. In this paper, we describe a modular and distributed middleware architecture aimed at high-level dynamic network manag ..."
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In addition to the control and supervision of components and connections, network management middlewares are required to enhance reliability and efficiency of distributed systems. In this paper, we describe a modular and distributed middleware architecture aimed at high-level dynamic network management. The proposed approach uses swarm-intelligence at the monitoring level to provide self-organization, adaptability, scalability and reactivity features. Resource management is performed by a multi-agent based layer. Knowledge exchange between the monitoring and management layers is mediated by a datawarehouse. The model is evaluated through a case study for grid computing.
Scheduling in Grid Systems using Ant Colony Algorithm
"... Abstract — Task scheduling is an important factor that directly influences the performance and efficiency of the system. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data ..."
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Abstract — Task scheduling is an important factor that directly influences the performance and efficiency of the system. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids. This paper presents a new algorithm based on ant colony optimization (ACO) metaheuristic for solving this problem. In this study, a proposed ACO algorithm for scheduling in Grid systems will be presented. Simulation results indicate our ACO algorithm optimizes total response time and also it increase utilization. Index Terms — Grid systems, scheduling, response
Sandip Kumar Goyal et al. / International Journal of Engineering and Technology (IJET) Adaptive and Dynamic Load Balancing in Grid Using Ant Colony Optimization
"... Abstract — Grid Computing involves coupled and coordinated use of geographically distributed resources for purposes such as large-scale computation and distributed data analysis. With the Grid becoming a viable high-performance alternative to the traditional supercomputing environment, a suitable an ..."
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Abstract — Grid Computing involves coupled and coordinated use of geographically distributed resources for purposes such as large-scale computation and distributed data analysis. With the Grid becoming a viable high-performance alternative to the traditional supercomputing environment, a suitable and efficient load balancing algorithm is needed to equally spread the load on each computing node in the grid. This research work presents Ant based algorithm to solve the load balancing problem in computational grid. In proposed algorithm, the pheromone is associated with resources, rather than path. The increase or decrease of pheromone represent load and depends on task status at resources. The main objective of proposed algorithm is to map tasks to resources in a way that balance out the load resulting in improved utilization of resources.
Performance Analysis of Load Balancing Algorithms in Cloud Computing
"... Cloud computing is a business oriented IT-technology, which is composed of multiple computing technologies accessed via internet. With the rapid increase in cloud usage, it becomes a challenge to deliver the cloud services effectively and efficiently to the cloud consumers on the pay-per usage basis ..."
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Cloud computing is a business oriented IT-technology, which is composed of multiple computing technologies accessed via internet. With the rapid increase in cloud usage, it becomes a challenge to deliver the cloud services effectively and efficiently to the cloud consumers on the pay-per usage basis. In this concern Balancing of load has become one of the essential components for the cloud computing environment to perform the effective operations. Scheduling of virtual machines or data centers has to be done properly by using an appropriate load balancing technique. Hence, several algorithms have been developed to process the client's request towards the cloud nodes.. In this present work, a hybridized swarm intelligence technique is proposed to evenly distribute the incoming task requests among the virtual machines or server. Additionally, the performance analysis has been performed using the CloudAnalyst simulator. This paper gives a comprehensive performance analysis of the proposed approach and compares its results with existing Round Robin (RR), Equally Spread Current Execution (ESCE) and ant colony optimization (ACO) techniques. Simulation results have demonstrated that the proposed technique shows a significant outcome in terms of response time, data center processing time and total cost in cloud computing.
