Multi-Objective Scheduling for Scientific Workflows on Cloud with Peer-To-Peer Clustering

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Wangsom P., Lavangnananda K., Bouvry P.

ผู้เผยแพร่: Hindawi

ปีที่เผยแพร่ (ค.ศ.): 2019

หน้าแรก: 175

หน้าสุดท้าย: 180

จำนวนหน้า: 6

ISBN: 9781538675120

นอก: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065090012&doi=10.1109%2fKST.2019.8687412&partnerID=40&md5=9e10845b81c24162f418f28d86dfb799

ภาษา: English-Great Britain (EN-GB)

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บทคัดย่อ

Running scientific workflows, often involves large-scale data-intensive applications, on cloud. The reduction of data movement is necessary because of the major impact to network utilization and energy consumption in network equipment in cloud data center. In order to reduce the Data Movement, this paper proposes Peer-To-peer clustering technique which can be applied to Directed Acyclic Graph (DAG) workflows. This research also includes Cost and Makespan which are probably the most common objectives for workflow scheduling. Due to dealing with the multi-objective optimization problem, Nondominated Sorting Genetic Algorithm-III (NSGA-III) is selected for finding solutions. Three well-known scientific workflows including Epigenomics, LIGO, and Montage are chosen as testbeds. Pareto front is adopted as the mean for visualization of the results while Hypervolume is selected as the performance metric. DAGs with Peer-To-peer clustering is evaluated by comparing its performance against the original DAG versions. The comparison reveals that DAGs with Peer-To-peer clustering enable NSGA-III to generate solutions having better Hypervolume and Pareto front in all three scientific workflows. ฉ 2019 IEEE.

คำสำคัญ

Cloud, Scientific Workflow