Hybrid Algorithm for Optimal Load Sharing in Grid Computing

Ramesh,

doi:10.3844/jcssp.2012.175.180

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…So maintain a load factor value as balanced one. [12] Load factor (LF balancing same as optimal grid balancing in the grid network. Otherwise, buyer satisfaction is less for unbalanced load factor.…”

Section: ) Insert X Into Agent Hash Tablementioning

confidence: 99%

Intelligent based large scale multi agent's resource management on shopping service with security

Surendran

Varthini

2012

2012 Nirma University International Conference on Engineering (NUiCONE)

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Grid computing is an Internet based large scale computing network. It works with resources and services for the purpose sharing in the network from their own or lease based. Power of Grid computing is everywhere present over the world for computing the solution for the problem through Resource management technique (RMT). RMT consist of Resource requester, resource matchmaker for a task, Resource provider and Agent-based resource manager. Agent-based resource manager produce enhanced Resource discovery service. Agent is software, decision maker for grid service. Grid service is requiring when, where, what type, alternate service arrangement and how to discover the services for public / private grid network. Multiple Agents are available in between the grid supplier and client. Multiple Agents communicated together through message passing via grid network. Large scale agent in the sense number of agents is large with big data handling capability. In each grid site active optimized agents are presented for execute the job. Each agent has a unique ID and different role in grid computing network. In some situation agents are used to migrate the one supplier to another supplier easily that is work replacement carried out by multi agent. Overloaded agent's host is relocating the service to other agent's host named as agent migration. Little work and idle agents are waiting for upcoming job. For deploying the application via host, first predict the best agent from a lot of distributed active agents. Buyer or consumer asks the service request from the supplier or seller. In this pairing service, the multi agents are acting as bridge between Buyer and supplier. List of Intelligent based large scale multi agents are deployment agent, task agent, mobility agent, optimization agent, matchmaker agent, mobile agent, supplier agent, resource agent, application agent. Agent software is used to replace the difficulty of shopping & shipment application (service) through Service Oriented Computing (SOC). Shopping process fully will take care by grid agents. Agents are assigning for grid Trust purpose and Security problems also overcome by the agents based on pseudo random number RSA algorithm through Grid Security Infrastructure (GSI).Index Terms -Agent's Resource Management Technique; Grid service deployment Life Cycle; Service Oriented Computing; Agent based grid service for shopping service; Integrated Agent Migration based Hash Search.

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Section: ) Insert X Into Agent Hash Tablementioning

confidence: 99%

Intelligent based large scale multi agent's resource management on shopping service with security

Surendran

Varthini

2012

2012 Nirma University International Conference on Engineering (NUiCONE)

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“…Then, the grid broker assigns gridlets based on deadline request and load. In [22], a hybrid algorithm is proposed for optimal load sharing with two components such as hash table and distributed hash table. It finds the nearest node and shares the load of a highly loaded node to lightly loaded node.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Multiconstrained Grid Scheduling Algorithm with Load Balancing and Fault Tolerance

Keerthika¹,

Suresh²

2015

The Scientific World Journal

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Grid environment consists of millions of dynamic and heterogeneous resources. A grid environment which deals with computing resources is computational grid and is meant for applications that involve larger computations. A scheduling algorithm is said to be efficient if and only if it performs better resource allocation even in case of resource failure. Allocation of resources is a tedious issue since it has to consider several requirements such as system load, processing cost and time, user's deadline, and resource failure. This work attempts to design a resource allocation algorithm which is budget constrained and also targets load balancing, fault tolerance, and user satisfaction by considering the above requirements. The proposed Multiconstrained Load Balancing Fault Tolerant algorithm (MLFT) reduces the schedule makespan, schedule cost, and task failure rate and improves resource utilization. The proposed MLFT algorithm is evaluated using Gridsim toolkit and the results are compared with the recent algorithms which separately concentrate on all these factors. The comparison results ensure that the proposed algorithm works better than its counterparts.

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“…Desirable performance goals of grid scheduling includes: Maximizing resource utilization, minimizing the execution time (Abramson et al, 1995) and fulfilling economic constraints (Buyya et al, 2000). A task sheduling using ant colony optimization is proposed (Ramesh and Krishnan, 2012). In this study we have proposed a newthreshold based job scheduling to minimize the execution time and turnaround time.…”

Section: Grid Scheduling Algorithmsmentioning

confidence: 99%

A New-Threshold Based Job Scheduling for Grid System

Parvathy¹

2014

Journal of Computer Science

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In heterogeneous distributed systems, utility grids have emerged as a new model of service. In this service, workflow scheduling is one of the challenging problems for satisfying user's quality requirements. One of the main issues in work flow scheduling is to minimize the workflow execution cost in terms of time and makespan. In this study, we propose a new workflow scheduling algorithm based on a novel concept called New-Threshold Based Scheduling (NTBS) that attempts to minimize the cost of workflow execution time and provides service fairness. It works under two phases. The scheduler computes threshold in first phase and in second phase it schedules the grid jobs to reduce the execution time without affecting fairness. From the simulation results it is observed that NTBS gives better performance in terms of reduced makespan and consistent turnaround time as compared to FCFS, EDF and other scheduling algorithms.

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Hybrid Algorithm for Optimal Load Sharing in Grid Computing

Cited by 6 publications

References 10 publications

Intelligent based large scale multi agent's resource management on shopping service with security

Intelligent based large scale multi agent's resource management on shopping service with security

A Multiconstrained Grid Scheduling Algorithm with Load Balancing and Fault Tolerance

A New-Threshold Based Job Scheduling for Grid System

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