Syed Nasir Mehmood Shah

Development and performance analysis of grid resource allocation methods

A ‘grid’ is an infrastructure for resource sharing. A computational grid has the potential for solving large scale problems using heterogeneous and distributed resources. Resource allocation is a fundamental issue in achieving high performance on computational grids. Many methods have been developed for grid resource allocation. In this paper, we propose a new grid resource allocation method (modified least cost method) that assigns tasks to computing nodes in a way that is close to the optimum. This paper also proposes a linear programming model for grid scheduling and includes a comparative performance analysis of our proposed resource allocation method with other well known methods. To facilitate the research, a software tool has been developed which produces a comprehensive simulation of a number of grid resource allocation methods. The experimental results show that our proposed method yields improvements in terms of performance and results in a lower computational cost.

Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid

Short term forecasting is significant operation to forecast the future jobs for computational grids as it can provide a solution for inconsistent resource availability and feasible job scheduling. A job forecasting model is presented to forecast one hour ahead of jobs submitted for computations using regression random forests. The training data constitutes the information about the type of job and jobs submitted on average each hour. The forecast model is built on the basis of training process. A real job data set from LCG (Large Hadron Collider Computing Grid) is used for evaluating the proposed forecast model, while considering the fact that jobs submitted are inconsistent. Findings provide a proof that by using proposed method the forecast error can be reduced and the effectiveness of job forecast can be improved for long test periods.

Performance Management Using Autonomous Control-Based Distributed Coordination Approach in a Volunteer Grid Computing Environment

In volunteer grid environment, it is difficult to fulfill the requirements of all jobs due to increasing demands of resources. A resource requester submits a job, require resources for the job to be completed within deadline and budget if specified any. Whereas resource provider makes use of available resources and wants to utilize resources to maximum. Therefore, satisfying the requirements of both i.e., jobs and resources makes it difficult to manage the performance of a volunteer grid. In performance management, the main objectives include maintaining service level agreements, maximization of resource utilization, meeting job deadline/budget and minimizing the job transfer. In this paper, only the maximization of resource utilization and meeting job deadlines will be addressed for managing the performance of a volunteer grid computing environment. An autonomous approach is introduced that provides dynamic resource allocation for submitted jobs in a volunteer grid environment depending on the availability and demand of resources. Grid resource brokers are considered third party organizations that work as intermediaries between volunteer resource provider and requester. Proposed autonomous approach is developed by utilizing distributed coordination approach for interactive assignment of volunteer resources. The proposed approach is applying distributed coordination approach and giving priority to maximization of volunteer resource usage while completing jobs within deadline.

Representing Job Scheduling for Volunteer Grid Environment using Online Container Stowage

Volunteer grid computing comprises of volunteer resources which are unpredictable in nature and as such the scheduling of jobs among these resources could be very uncer-tain. It is also difficult to ensure the successful completion of submitted jobs on volunteer resources as these resources may opt to withdraw from the grid system anytime or there might be a resource failure, which requires job reassignments. However, a careful consideration of future jobs can make scheduling of jobs more reliable on volunteer resources. There are two possibilities; either to forecast the future jobs or to forecast the resource availability by studying the history events. In this paper an attempt has been made to utilize the future job forecasting in improving the job scheduling experience with volunteer grid resources. A scheduling approach is proposed that uses container stowage to allocate the volunteer grid resources based on the jobs submitted. The proposed scheduling approach optimizes the number of resources actively used. The approach presents online container stowage adaptability for scheduling jobs using volun-teer grid resources. The performance has been evaluated by making comparison to other scheduling algorithms adopted in volunteer grid. The simulation results have shown that the pro-posed approach performs better in terms of average turnaround and waiting time in comparison with existing scheduling algo-rithms. The job load forecast also reduced the number of job reassignments.