QoS-aware Scheduling for Data Intensive Workflow

Authors

Wan Cong, College of Information Science and Engineering, Northeastern University, Shenyang 110004, China;
Cuirong Wang, College of Information Science and Engineering, Northeastern University, Shenyang 110004, China;
Wang Cong, College of Information Science and Engineering, Northeastern University, Shenyang 110004, China;

Abstract

Abstract: The development of technology enables people to access resources from different data centers. Resource management and scheduling of applications, such as workflow, that are deployed on the cloud computing environment have already become a hot spot. A QoS-aware scheduling algorithm for data intensive workflow on multiple data center environment was proposed. Scheduling data intensive workflow on multiple data center environment has two characteristics: A large amount of data is distributed in different geographical locations, the process of data migration will consume a large amount of time and bandwidth; secondly, the data centers have different price and resources. Data migration between data centers was mapped to a task of workflow, models workflow with DAG, simplifying the DAG, and scheduling all the tasks of workflow using Simulated Annealing. The experiment using CloudSim platform and Hadoop platform shows that this scheduling algorithm is effective.

Recommended Citation

Cong, Wan; Wang, Cuirong; and Cong, Wang (2020) "QoS-aware Scheduling for Data Intensive Workflow," Journal of System Simulation: Vol. 28: Iss. 3, Article 7.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss3/7

First Page

549

Revised Date

2014-10-23

DOI Link

https://doi.org/

Last Page

558

CLC

TP391

Recommended Citation

Wan Cong, Wang Cuirong, Wang Cong. QoS-aware Scheduling for Data Intensive Workflow[J]. Journal of System Simulation, 2016, 28(3): 549-558.