A static benchmarking for grid scheduling problems

Analysis of algorithms for Grid computing systems before deployment in real Grid infrastructures is an important issue in Grid computing domain. Due to the complexity of real Grid systems, assessing performance analysis of optimization algorithms such as scheduling algorithms, is in general difficult, costly and time consuming. Benchmarking and simulation are two most used alternatives for analyzing optimization algorithms in Grid systems before deployment. In this paper we present a static benchmarking for scheduling problems in Grid systems. The benchmarking has been generated using the HyperSim-G Grid simulator and captures several types of Grid systems based on combinations of different machine and task types. Instances have six different sizes ranging from tiny (32 machines/512 tasks) to extra large size (1024 machines/16384 tasks) and are grouped according to machine and task types. The benchmark suite, consisting of about 720 instances, is offered through a web pagePeer ReviewedPostprint (published version

DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability

DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical\ud analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability

A static benchmarking for grid scheduling problems

Analysis of algorithms for Grid computing systems before deployment in real Grid infrastructures is an important issue in Grid computing domain. Due to the complexity of real Grid systems, assessing performance analysis of optimization algorithms such as scheduling algorithms, is in general difficult, costly and time consuming. Benchmarking and simulation are two most used alternatives for analyzing optimization algorithms in Grid systems before deployment. In this paper we present a static benchmarking for scheduling problems in Grid systems. The benchmarking has been generated using the HyperSim-G Grid simulator and captures several types of Grid systems based on combinations of different machine and task types. Instances have six different sizes ranging from tiny (32 machines/512 tasks) to extra large size (1024 machines/16384 tasks) and are grouped according to machine and task types. The benchmark suite, consisting of about 720 instances, is offered through a web pagePeer Reviewe