A Scheduling Algorithm for Many-Task Computing Optimized for IO Contention in Heterogeneous Grid Environment

Zhang, Yu; Chen, Shuwei; Hu, Ziqian

doi:10.1109/iccis.2013.405

Cited by 4 publications

(1 citation statement)

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“…The system can be extended further by making network and workload fluctuation as dynamic. Maria A. Rodriguez and Rajkumar Buyya (2014) proposed a Particle Swarm Optimization [16], a scheduling strategy for scientific workflows based on meta heuristics optimization technique. The algorithm reduces the overall workflow execution cost while meeting deadlines constraints.…”

Section: Review On Schedulingmentioning

confidence: 99%

Range smart cluster monitor based guesstimate approach for resource scheduling in small size clusters

Gokuldev¹,

Jathin²

2018

IJET

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Performing scheduling of tasks with low energy consumption with high performance is one of the major concerns in distributed computing. Most of the existing systems have achieved improved energy efficiency but compromised with QoS metrics such as makespan and resource utilization. A resource scheduling strategy for wireless clusters is proposed by making careful considerations on decisions that would im-prove the battery life of nodes. The proposed strategy also incorporates monitoring system with in the clusters for optimizing the system performance as well as energy consumption. The system ensures “Any case zero loss" performance wherein each cluster will be monitored by at least one cluster monitor. This is implemented by using predictive calculation at each cluster monitor to communicate only if absolutely essential, during assigning jobs to resources, selecting optimal resources by assigning the jobs to the most power efficient resource among the available idle resources within the cluster. The experimental result ensures improved system performance with low power consumption in homogeneous computing environment. The resource sharing strategy is experimentally analyzed, considering the important performance metrics such as starvation deadline, turnaround time, miss hit count through simulations. Significant results were observed with improved efficiency.

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Section: Review On Schedulingmentioning

confidence: 99%