The MultiCluster Model to the Integrated Use of Multiple Workstation Clusters

IEEE Trans. Parallel Distrib. Syst.

et al. 2006

Abstract-This paper addresses workload allocation techniques for two types of sequential jobs that might be found in multicluster systems, namely, non-real-time jobs and soft real-time jobs. Two workload allocation strategies, the Optimized mean Response Time (ORT) and the Optimized mean Miss Rate (OMR), are developed by establishing and numerically solving two optimization equation sets. The ORT strategy achieves an optimized mean response time for non-real-time jobs, while the OMR strategy obtains an optimized mean miss rate for soft real-time jobs over multiple clusters. Both strategies take into account average system behaviors (such as the mean arrival rate of jobs) in calculating the workload proportions for individual clusters and the workload allocation is updated dynamically when the change in the mean arrival rate reaches a certain threshold. The effectiveness of both strategies is demonstrated through theoretical analysis. These strategies are also evaluated through extensive experimental studies and the results show that when compared with traditional strategies, the proposed workload allocation schemes significantly improve the performance of job scheduling in multiclusters, both in terms of the mean response time (for non-real-time jobs) and the mean miss rate (for soft real-time jobs).

Section: Introductionmentioning

confidence: 99%

Allocating non-real-time and soft real-time jobs in multiclusters

IEEE Trans. Parallel Distrib. Syst.

et al. 2006

“…These constituent clusters may be located within a single organisation or across different administrative organisations [1][2][3]. Workload management and scheduling are key issues in multicluster and grid computing and parallel jobs constitute a typical workload type.…”

Section: Introductionmentioning

confidence: 99%

Hybrid performance-based workload management for multiclusters and grids

et al. 2004

IEE Proc., Softw.

The paper addresses the dynamic scheduling of parallel jobs with quality-of-service demands (soft-deadlines) in multiclusters and grids. Three performance metrics (over-deadline, makespan and idle-time) are combined with variable weights to evaluate the scheduling performance. These three metrics are used to measure the extent to which jobs comply with their QoS demands, the resource throughput and the resource utilisation. Therefore, clusters situated in different administrative organisations can utilise different weight combinations to represent their different performance requirements. Two levels of performance optimisation are applied in the multicluster. At the multicluster level, a scheduler (which we call MUSCLE) allocates parallel jobs with high packing potential to the same cluster; MUSCLE also takes the jobs' QoS requirements into account and employs a heuristic to allocate suitable workloads to each cluster to balance the overall system performance. At the local cluster level, an existing workload manager, called TITAN, utilises a genetic algorithm to further improve the scheduling performance of the jobs sent by MUSCLE. Extensive experimental studies are conducted to verify the effectiveness of the scheduling mechanism in MUSCLE. The results show that, compared with traditional distributed workload allocation policies, the comprehensive scheduling performance (in terms of over-deadline, makespan and idle-time) of parallel jobs is significantly improved across the multicluster.

“…Separate clusters are increasingly being interconnected to create multicluster or grid computing architectures [1][2] [6] [7] and as a result, workload management for these architectures is becoming a key research issue. Parallel jobs that run in these domains can be classified into two categories: rigid and moldable [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Performance-Oriented Scheduling of Moldable Jobs with QoS Demands in Multiclusters and Grids

Lecture Notes in Computer Science

et al. 2004

Abstract. This paper addresses the dynamic scheduling of moldable jobs with QoS demands (soft-deadlines) in multiclusters. A moldable job can be run on a variable number of resources. Three metrics (over-deadline, makespan and idletime) are combined with weights to evaluate the scheduling performance. Two levels of performance optimisation are applied in the multicluster. At the multicluster level, a scheduler (which we call MUSCLE) allocates parallel jobs with high packing potential to the same cluster; MUSCLE also takes the jobs' QoS requirements into account and employs a heuristic to achieve performance balancing across the multicluster. At the single cluster level, an existing workload manager, called TITAN, utilizes a genetic algorithm to further improve the scheduling performance of the jobs allocated by MUSCLE. Extensive experimental studies are conducted to verify the effectiveness of the scheduling mechanism in MUSCLE. The results show that the comprehensive scheduling performance of parallel jobs is significantly improved across the multicluster.