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

Wen, Zhenyu; Garg, Saurabh; Aujla, Gagangeet Singh; Alwasel, Khaled; Puthal, Deepak; Dustdar, Schahram; Zomaya, Albert Y.; Rajan, Rajiv

Abstract

Industry 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.

Citation

Wen, Z., Garg, S., Aujla, G. S., Alwasel, K., Puthal, D., Dustdar, S., …Rajan, R. (2021). Running Industrial Workflow Applications in a Software-defined Multi-Cloud Environment using Green Energy Aware Scheduling Algorithm. IEEE Transactions on Industrial Informatics, 17(8), 5645-5656. https://doi.org/10.1109/tii.2020.3045690

• Journal Article Type: Article
• Online Publication Date: Dec 18, 2020
• Publication Date: 2021-08
• Deposit Date: Apr 27, 2021
• Publicly Available Date: Apr 27, 2021
• Journal: IEEE Transactions on Industrial Informatics
• Print ISSN: 1551-3203
• Electronic ISSN: 1941-0050
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 8
• Pages: 5645-5656
• DOI: https://doi.org/10.1109/tii.2020.3045690