Cost-efficient Workflow as a Service using Containers

Workflows are special applications used to solve complex scientific problems. The emerging Workflow as a Service (WaaS) model provides scientists with an effective way of deploying their workflow applications in Cloud environments. The WaaS model can execute multiple workflows in a multi-tenant Clou...

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Bibliographic Details
Published in:Journal of grid computing 2024-03, Vol.22 (1), p.40, Article 40
Main Authors: Karmakar, Kamalesh, Tarafdar, Anurina, Das, Rajib K., Khatua, Sunirmal
Format: Article
Language:English
Subjects:
Cloud computing
Computer Science
Containers
Distributed processing
Management of Computing and Information Systems
Processor Architectures
Quality of service
Resource utilization
Scheduling
Software
Task scheduling
User Interfaces and Human Computer Interaction
User satisfaction
Workflow software
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Summary:Workflows are special applications used to solve complex scientific problems. The emerging Workflow as a Service (WaaS) model provides scientists with an effective way of deploying their workflow applications in Cloud environments. The WaaS model can execute multiple workflows in a multi-tenant Cloud environment. Scheduling the tasks of the workflows in the WaaS model has several challenges. The scheduling approach must properly utilize the underlying Cloud resources and satisfy the users’ Quality of Service (QoS) requirements for all the workflows. In this work, we have proposed a heurisine-sensitive workflows in a containerized Cloud environment for the WaaS model. We formulated the problem of minimizing the MIPS (million instructions per second) requirement of tasks while satisfying the deadline of the workflows as a non-linear optimization problem and applied the Lagranges multiplier method to solve it. It allows us to configure/scale the containers’ resources and reduce costs. We also ensure maximum utilization of VM’s resources while allocating containers to VMs. Furthermore, we have proposed an approach to effectively scale containers and VMs to improve the schedulability of the workflows at runtime to deal with the dynamic arrival of the workflows. Extensive experiments and comparisons with other state-of-the-art works show that the proposed approach can significantly improve resource utilization, prevent deadline violation, and reduce the cost of renting Cloud resources for the WaaS model.
ISSN:1570-7873
1572-9184
DOI:10.1007/s10723-024-09745-7