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.

Item Type:	Refereed Article
Keywords:	data centers, cloud computing, task analysis, green products, job shop scheduling, informatics, carbon footprint, software defined networking, green energy, industrial workflow applications, big data, industrial clouds
Research Division:	Information and Computing Sciences
Research Group:	Distributed computing and systems software
Research Field:	Cloud computing
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Computer systems
UTAS Author:	Garg, S (Dr Saurabh Garg)
ID Code:	142769
Year Published:	2020
Web of Science® Times Cited:	9
Deposited By:	Information and Communication Technology
Deposited On:	2021-02-11
Last Modified:	2022-08-29
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