Performance Evaluation of Adaptive Scheduling Algorithm for Shared Heterogeneous Cluster Systems

Cluster computing systems have recently generated enormous interest for providing easily scalable and cost-effective parallel computing solution for processing large-scale applications. Various adaptive space-sharing scheduling algorithms have been proposed to improve the performance of dedicated and homogeneous clusters. But commodity clusters are naturally non-dedicated and tend to be heterogeneous over the time as cluster hardware is usually upgraded and new fast machines are also added to improve cluster performance. The existing adaptive policies for dedicated homogeneous and heterogeneous parallel systems are not suitable for such conditions. Most of the existing adaptive policies assume a priori knowledge of certain job characteristics to take scheduling decisions. However such information is not readily available without incurring great cost. This paper fills these gaps by designing robust and effective space-sharing scheduling algorithm for non-dedicated heterogeneous cluster systems, assuming no job characteristics to reduce mean job response time. Evaluation results show that the proposed algorithm provide substantial improvement over existing algorithms at moderate to high system utilizations

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