5 research outputs found
A hyper-heuristic for adaptive scheduling in computational grids
In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in computational grids. An efficient scheduling of jobs to grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics. In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid resources according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware applications that need efficient planning of jobs to resources. The hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.Peer ReviewedPostprint (author's final draft
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