Scheduling Algorithms for Efficient Execution of Stream Workflow Applications in Multicloud Environments

Barika M., Garg S., Chan A., Calheiros R. N.

IEEE Transactions on Services Computing, vol.15, no.2, pp.860-875, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1109/tsc.2019.2963382
  • Journal Name: IEEE Transactions on Services Computing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Page Numbers: pp.860-875
  • Keywords: Big Data, Real-time systems, Processor scheduling, Cloud computing, Quality of service, Resource management, Big data, stream workflow, scheduling, greedy algorithm, genetic algorithm
  • TED University Affiliated: No


© 2008-2012 IEEE.Big data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is stream workflow application, which integrates multiple streaming big data applications to support decision making. Each analytical component of these applications runs continuously and processes data streams whose velocity will depend on several factors such as network bandwidth and processing rate of parent analytical component. As a consequence, the execution of these applications on cloud environments requires advanced scheduling techniques that adhere to end user's requirements in terms of data processing and deadline for decision making. In this article, we propose two multicloud scheduling and resource allocation techniques for efficient execution of stream workflow applications on multicloud environments while adhering to workflow application and user performance requirements and reducing execution cost. Results showed that the proposed genetic algorithm is an adequate and effective for all experiments.