CWOM: A lightweight cloud-oriented workflow optimisation middleware

被引：0
作者：
Xiao P. [1 ]
机构：
[1] School of Computer and Communication, Hunan Institute of Engineering, Xiangtan City, Hunan Province
来源：
International Journal of Networking and Virtual Organisations | 2021年 / 24卷 / 01期
关键词：
Cloud computing; Performance monitor; Virtual machine; Workflow;
D O I：
10.1504/IJNVO.2021.111617
中图分类号：
学科分类号：
摘要：
In recent years, more and more workflow applications have been developed to execute on various kinds of cloud platforms. Although many cloud-based workflow management and enactment systems have been proposed, there are still many challenges when deploying and executing workflows on various kinds of real-world cloud platform. In this paper, we present a lightweight framework, namely cloud-oriented workflow optimisation middleware (CWOM), which is designed for easing the work of optimising the workflow execution efficiency as well as the work of realising fine-grained workflow management. The proposed CWOM is implemented as a plug-in middleware that can interact with the other workflow services and therefore provide more desirable services for workflow applications. Another advantage of our CWOM is that its services are loosely coupled with other cloud services, which makes it more flexible and configurable in most of the real-world cloud platforms. To investigate the effectiveness of the proposed system, we deploy the prototype implementation of CWOM in a campus-based cloud platform, and the experimental results indicate that it can significantly improve the delivered QoS and enhance the service level offered by conventional workflow solutions. Copyright © 2021 Inderscience Enterprises Ltd.
引用
收藏
页码：67 / 83
页数：16
相关论文
共 32 条
  • [1] Abdi S., PourKarimi L., Et al., Cost minimization for bag-of-tasks workflows in a federation of clouds, Journal of Supercomputing, 74, 6, pp. 2801-2822, (2018)
  • [2] Aslam S., Islam S.u., Et al., Information collection centric techniques for cloud resource management: taxonomy, analysis and challenges, Journal of Network and Computer Applications, 100, 1, pp. 80-94, (2017)
  • [3] Bryk P., Malawski M., Et al., Storage-aware algorithms for scheduling of workflow ensembles in clouds, Journal of Grid Computing, 14, 2, pp. 359-378, (2016)
  • [4] Byun E-K., Kee Y-S., Et al., BTS: resource capacity estimate for time-targeted science workflows, Journal of Parallel and Distributed Computing, 71, 6, pp. 848-862, (2011)
  • [5] Cai Z., Li X., Et al., A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds, Future Generation Computer Systems, 71, 1, pp. 57-72, (2017)
  • [6] Cao H., Jin H., Et al., DAGMap: efficient and dependable scheduling of DAG workflow job in grid, Journal of Supercomputing, 51, 2, pp. 201-223, (2010)
  • [7] Casas I., Taheri J., Et al., A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems, Future Generation Computer Systems, 74, 2, pp. 168-178, (2017)
  • [8] Deelman E., Vahi K., Et al., Pegasus, a workflow management system for science automation, Future Generation Computer Systems, 46, 1, pp. 17-35, (2015)
  • [9] Delavar A.G., Aryan Y., HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems, Cluster Computing, 17, 1, pp. 129-137, (2014)
  • [10] Diaz-Montes J., Abdelbaky M., Et al., CometCloud: enabling software-defined federations for end-to-end application workflows, IEEE Internet Computing, 19, 1, pp. 69-73, (2015)
1234