Paper Info
Title
Job Scheduling Optimization of High Performance Computing in Biological Gene Sequencing Based on Workload Analysis.
Abstract
Mining job scheduling features based on extraction and analysis of workload trace in high performance computing clusters can be used to optimize scheduling strategy and enhance system performance. Based on detailed analysis of workload trace from a gene sequencing high performance computing system, this paper proposes a multi-queue backfilling scheduling algorithm, which is based on traditional backfilling scheduling. While optimizing for memory resource demands, this algorithm provides queue level load balancing to deal with the innate load imbalance characteristics of high performance systems. Experimental results based on practical gene sequencing workload trace clearly demonstrate that compared with traditional scheduling algorithms, the algorithm proposed in this paper is a good strategy to reduce the job waiting time and improve resource utilization.
Year
Venue
Field
2017
ISPA/IUCC
Resource management,Load management,Supercomputer,Computer science,Scheduling (computing),Load balancing (computing),Workload,Queue,Human–computer interaction,Job scheduler,Distributed computing
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Xiaofei Wu102.70
Shoubin Dong255.51
Liyun Zuo3433.21
Yizhen Sun410.71