A Strategy for Resource Allocation and Pricing in Grid Environment Based on Economic Model

2009 ◽  
Author(s):  
Bin Lu ◽  
Jianjing Ma
Keyword(s):  
Resource Allocation ◽  
Economic Model ◽  
Grid Environment

Resource Allocation Mechanism with New Models for Grid Environment

2013 ◽  
Vol 5 (2) ◽  
pp. 1-26
Author(s):  
Kuppani Sathish ◽  
A. Rama Mohan Reddy
Keyword(s):  
Resource Allocation ◽  
Waiting Time ◽  
Vital Role ◽  
Allocation Mechanism ◽  
Grid Resource ◽  
Grid Environment ◽  
Grid Resources ◽  
Utilization Cost ◽  
Resource Allocation Mechanism ◽  
Grid Resource Allocation

Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

Benchmarking Grid Applications for Performance and Scalability Predictions

2010 ◽  
pp. 89-121 ◽  
Author(s):  
Radu Prodan ◽  
Farrukh Nadeeem ◽  
Thomas Fahringer
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Real World ◽  
Experimental Results ◽  
Problem Size ◽  
Grid Environment ◽  
Computational Costs ◽  
Grid Applications ◽  
Grid Resources ◽  
Grid Site

Application benchmarks can play a key role in analyzing and predicting the performance and scalability of Grid applications, serve as an evaluation of the fitness of a collection of Grid resources for running a specific application or class of applications (Tsouloupas & Dikaiakos, 2007), and help in implementing performance-aware resource allocation policies of real time job schedulers. However, application benchmarks have been largely ignored due to diversified types of applications, multi-constrained executions, dynamic Grid behavior, and heavy computational costs. To remedy these, the authors present an approach taken by the ASKALON Grid environment that computes application benchmarks considering variations in the problem size of the application and machine size of the Grid site. Their system dynamically controls the number of benchmarking experiments for individual applications and manages the execution of these experiments on different Grid sites. They present experimental results of our method for three real-world applications in the Austrian Grid environment.

scholarly journals A Multiconstrained Grid Scheduling Algorithm with Load Balancing and Fault Tolerance

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
P. Keerthika ◽  
P. Suresh
Keyword(s):  
Resource Allocation ◽  
Fault Tolerance ◽  
Load Balancing ◽  
User Satisfaction ◽  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Grid Environment ◽  
Resource Allocation Algorithm ◽  
Processing Cost ◽  
Comparison Results

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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