Multi-Objective Scientific-Workflow Scheduling with Data Movement Awareness in Cloud

บทความในวารสาร

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

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

ผู้เผยแพร่: Institute of Electrical and Electronics Engineers

ปีที่เผยแพร่ (ค.ศ.): 2019

วารสาร: IEEE Access (2169-3536)

Volume number: 7

หน้าแรก: 177063

หน้าสุดท้าย: 177081

จำนวนหน้า: 19

นอก: 2169-3536

eISSN: 2169-3536

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076819764&doi=10.1109%2fACCESS.2019.2957998&partnerID=40&md5=5838db09db6a3ddc4091af822c2b22b6

ภาษา: English-Great Britain (EN-GB)

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บทคัดย่อ

Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multi-objective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGA-III is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NSGA-III alone, E-NSGA-III with Peer-to-Peer clustering and E-NSGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed. ฉ 2013 IEEE.

คำสำคัญ

data movement, directed acyclic graph (DAG), extreme nondominated sorting genetic algorithm-III (E-NSGA-III), makespan, multilevel dependent node clustering (MDNC), nondominated sorting genetic algorithm-III (NSGA-III), peer-to-peer clustering, scientific workflows