TY - JOUR
T1 - Running Industrial Workflow Applications in a Software-Defined Multicloud Environment Using Green Energy Aware Scheduling Algorithm
AU - Wen, Zhenyu
AU - Garg, Saurabh
AU - Aujla, Gagangeet Singh
AU - Alwasel, Khaled
AU - Puthal, Deepak
AU - Dustdar, Schahram
AU - Zomaya, Albert Y.
AU - Ranjan, Rajiv
N1 - Funding Information:
Manuscript received September 2, 2020; revised December 15, 2020; accepted December 15, 2020. Date of publication December 18, 2020; date of current version May 3, 2021. This work was supported in part by the PACE Project EP/T021985/1, in part by the SUPER Project EP/R033293/1, in part by the National Natural Science Foundation of China under Grant 62072408, and in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LY20F020030. Paper no. TII-20-4197. (Corresponding author: Gagangeet Singh Aujla.) Zhenyu Wen and Deepak Puthal are with the School of Computing, Newcastle University, NE1 7RU Newcastle upon Tyne, U.K. (e-mail: [email protected]; [email protected]). Saurabh Garg is with the University of Tasmania, Hobart Australia (e-mail: [email protected]).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows 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 article 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 tradeoff of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of nonrenewable 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 times with a slight increase in the cost when compared to cost-based workflow scheduling methods.
AB - Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows 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 article 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 tradeoff of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of nonrenewable 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 times with a slight increase in the cost when compared to cost-based workflow scheduling methods.
KW - Big data
KW - green energy
KW - industrial clouds
KW - industrial workflow applications
KW - software defined networking
UR - http://www.scopus.com/inward/record.url?scp=85098799185&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.3045690
DO - 10.1109/TII.2020.3045690
M3 - Article
AN - SCOPUS:85098799185
SN - 1551-3203
VL - 17
SP - 5645
EP - 5656
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 8
M1 - 9298863
ER -