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Running Industrial Workflow Applications in a Software-defined Multi-Cloud Environment using Green Energy Aware Scheduling Algorithm

Lookup NU author(s): Dr Zhenyu Wen, Khaled Alwasel, Dr Deepak Puthal

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Abstract

IEEEIndustry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and Cloud computing. To handle the work-flows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers which is not only a financial burden but also increases their carbon footprint. The private Software Defined Wide Area network (SDWAN) connects a cloud provider's data centers across the planet. This gives the opportunity to develop new scheduling strategies to manage cloud providers workload in a more energy-efficient manner. In this context, this paper addresses the problem of scheduling data-driven industrial workflow applications over a set of private SDWAN connected data centers in an energy-efficient manner while managing trade-off of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of non-renewable energy by utilizing the real-world electricity prices with the availability of green energy on different cloud data centers, where the energy consumption consists of the usage of running application over multiple data centers and transferring the data among them through SDWAN. The evaluation shows that our proposed method can increase usage of green energy for the execution of industrial workflow up to 3× times with a slight increase in the cost when compared to cost-based workflow scheduling methods.


Publication metadata

Author(s): Wen Z, Garg S, Aujla GSS, Alwasel K, Puthal D, Dustdar S, Zomaya AY, Rajan R

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Industrial Informatics

Year: 2020

Pages: epub ahead of print

Online publication date: 18/12/2020

Acceptance date: 02/04/2016

ISSN (print): 1551-3203

ISSN (electronic): 1941-0050

Publisher: IEEE Computer Society

URL: https://doi.org/10.1109/TII.2020.3045690

DOI: 10.1109/TII.2020.3045690


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