Abstract
Cloud computing utilizes thousands of Cloud Data Centres (CDC) and fulfils the demand of end-users dynamically using new technologies and paradigms such as Industry 4.0 and Internet of Things (IoT). With the emergence of Industry 4.0, the quality of cloud service has increased; however, CDC consumes a large amount of energy and produces a huge quantity of carbon footprint, which is one of the major drivers of climate change. This chapter discusses the impacts of cloud developments on climate and quantifies the carbon footprint of cloud computing in a warming world. Further, the dynamic transition from cloud computing to Industry 4.0 is discussed from an eco-friendly/climate change threat perspective. Finally, open research challenges and opportunities for prospective researchers are explored.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- CDC:
-
Cloud Data Centres
- AI:
-
Artificial Intelligence
- IoT:
-
Internet of Things
- 6G:
-
6th Generation
- VMs:
-
Virtual Machines
- SLA:
-
Service Level Agreements
- QoS:
-
Quality of Service
- NAS:
-
Network Attached Storage
- IPCC:
-
Intergovernmental Panel on Climate Change
- Terms:
-
Description
- Cloud Computing:
-
It is on-demand cloud service (using resources such as processor, storage, memory and network) to the users via Internet
- Industry 4.0:
-
Current trend of automation and data transfer in manufacturing technologies
- Climate Change:
-
Change in the mean or basic state of the climate
- Eco-friendly:
-
Anything that does not harm the environment
- Quality of Service (QoS):
-
It is a measurement in terms of performance parameters to evaluate the service quality
- Service Level Agreements (SLA):
-
SLA is an official document, which is signed between cloud user and cloud provider based on QoS requirements
- Agriculture 4.0:
-
Atomization of Agriculture related aspects such as precision agriculture and big data analytics
- Healthcare 4.0:
-
Management of vast amount of healthcare data efficiently
- Carbon Footprints:
-
It is the amount of carbon released in the environment by the computing system
- Digitization of Economies:
-
The transition of financial systems towards digital platforms
- COVID-19 Pandemic:
-
It is a coronavirus disease 2019, which is caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)
References
Al Nuaimi K, Mohamed N, Al Nuaimi M, Al-Jaroodi J (2012) A survey of load balancing in cloud computing: challenges and algorithms. In: Proceedings of the second symposium on network cloud computing and applications. pp 137–142
Banipal TS, Kaur R, Banipal PK (2017) Interactions of diazepam with sodium dodecylsulfate and hexadecyl trimethyl ammonium bromide: conductometric, UV–visible spectroscopy, fluorescence and NMR studies. J Mol Liq 236:331–337
Banipal TS, Kaur R, Banipal PK (2018) Effect of sodium chloride on the interactions of ciprofloxacin hydrochloride with sodium dodecyl sulfate and hexadecyl trimethylammonium bromide: conductometric and spectroscopic approach. J Mol Liq 255:113–121
Bittencourt L, Immich R, Sakellariou R, Fonseca N, Madeira E, Curado M, Villas L, DaSilva L, Lee C, Rana O (2018) The internet of things, fog and cloud continuum: integration and challenges. Internet Things 3:134–155
Buzzi S, Chih-Lin I, Klein TE, Poor HV, Yang C, Zappone A (2016) A survey of energy-efficient techniques for 5G networks and challenges ahead. IEEE J Sel Areas Commun 34(4):697–709
Catarinucci L, Cappelli M, Colella R, Tarricone L (2008) A novel low-cost multisensor-tag for RFID applications in healthcare. Microwave Opt Technol Lett 50(11):2877–2880
Crosby M, Pattanayak P, Verma S, Kalyanaraman V (2016) Blockchain technology: beyond bitcoin. Appl Innovation 2(6–10):71
Dasgupta P, Metya A, Naidu CV, Singh M, Roxy MK (2020) Exploring the long-term changes in the Madden Julian Oscillation using machine learning. Sci Rep 10(1):1–13
Dayarathna M, Wen Y, Fan R (2015) Data center energy consumption modeling: a survey. IEEE Commun Surv Tutorials 18(1):732–794
Did a 1912 Newspaper Article Predict Global Warming? [Accessed 2 Feb 2020] Available at: https://www.snopes.com/fact-check/1912-article-global-warming/
Europe heatwave: Paris latest to break record with 42.6C, [Accessed 2 Feb 2020] Available at: https://www.bbc.co.uk/news/world-europe-49108847
Familiar B (2015) IoT and microservices. In: Microservices, IoT, and Azure (pp 133–163). Apress, Berkeley, CA
Gill SS, Buyya R (2018) SECURE: Self-protection approach in cloud resource management. IEEE Cloud Comput 5(1):60–72
Gill SS, Buyya R (2019) A taxonomy and future directions for sustainable cloud computing: 360 degree view. ACM Comput Surv (CSUR) 51(5):1–33
Gill SS, Chana I, Buyya R (2017) IoT based agriculture as a cloud and big data service: the beginning of digital India. J Organ End User Comput (JOEUC) 29(4):1–23
Gill SS, Garraghan P, Stankovski V, Casale G, Thulasiram RK, Ghosh SK, Ramamohanarao K, Buyya R (2019) Holistic resource management for sustainable and reliable cloud computing: an innovative solution to global challenge. J Syst Softw 155:104–129
Gill SS, Tuli S, Minxian Xu, Singh I, Singh KV, Lindsay D, Tuli S et al (2019) Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: evolution, vision, trends and open challenges. Internet Things 8:1–26
Gill SS, Buyya R (2019) Sustainable cloud computing realization for different applications: a manifesto. In: digital business, Springer, Cham, pp 95–117
Gill SS, Kumar A, Singh H, Singh M, Kaur K, Usman M, Buyya R (2020) Quantum computing: a taxonomy, systematic review and future directions. arXiv preprint arXiv:2010.15559
Jamshidi S, Baniasad M, Niyogi D (2020) Global to USA county scale analysis of weather, urban density, mobility, homestay, and mask use on COVID-19. Int J Environ Res Public Health 17(21):7847
Krug L, Shackleton M, Saffre F (2014) Understanding the environmental costs of fixed line networking. In: Proceedings of the 5th international conference on Future energy systems. 87–95
Kulp SA, Strauss BH (2019) New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10(1):1–12
Kumar A, Sharma K, Singh H, Naugriya SG, Gill SS, Buyya R (2020) A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic. Futur Gener Comput Syst 115:1–19
Kuppusamy P (2014) A Cloud-oriented green computing architecture for E-Learning applications. Int J Recent Innovation Trends Comput Commun 2:3775–3783
Lane ND, Bhattacharya S, Georgiev P, Forlivesi C, Jiao L, Qendro L, Kawsar F (2016) Deepx: a software accelerator for low-power deep learning inference on mobile devices. In: Proceedings of the 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 1–12
Lee J, Azamfar M, Singh J (2019) A blockchain enabled cyber-physical system architecture for industry 4.0 manufacturing systems. Manufact Lett 20:34–39
Lee CC, Chung PS, Hwang MS (2013) A survey on attribute-based encryption schemes of access control in cloud environments. IJ Netw Secur 15(4):231–240
Liu Q, Li P, Zhao W, Cai W, Yu S, Leung VC (2018) A survey on security threats and defensive techniques of machine learning: a data driven view. IEEE Access 6:12103–12117
Liu J, Wang S, Zhou A, Xu J, Yang F (2020) SLA-driven container consolidation with usage prediction for green cloud computing. Frontiers Comput Sci 14(1):42–52
Hulkury MN, Doomun MR (2012) “Integrated green cloud computing architecture,” In: 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), Kuala Lumpur, pp 269–274
Mahallat I (2015) Fault-tolerance techniques in cloud storage: a survey. Int J Database Theory Appl 8(4):183–190
Masdari M, Zangakani M (2019) Green cloud computing using proactive virtual machine placement: challenges and issues. J Grid Computing 1–33
Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S (2018) Global warming of 1.5 C. An IPCC Special Report on the impacts of global warming of, 1
Millar RJ, Fuglestvedt JS, Friedlingstein P, Rogelj J, Grubb MJ, Matthews HD, Skeie RB, Forster PM, Frame DJ, Allen MR (2017) Emission budgets and pathways consistent with limiting warming to 1.5 C. Nature Geoscience 10(10):741–747
O’Leary DE (2013) Artificial intelligence and big data. IEEE Intell Syst 28(2):96–99
Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Church JA, Clarke L, Dahe Q, Dasgupta P, Dubash NK (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. p 151
Rebecca Wright, “There's an unlikely beneficiary of coronavirus: the planet”, Available at: https://edition.cnn.com/2020/03/16/asia/china-pollution-coronavirus-hnk-intl/index.html Accessed 20 Mar 2020
Rolnick D, Donti PL, Kaack LH, Kochanski K, Lacoste A, Sankaran et al (2019) Tackling climate change with machine learning. arXiv preprint arXiv:1906.05433
Roopaei M, Rad P, Jamshidi M (2017) Deep learning control for complex and large scale cloud systems. Intel Autom Soft Comput 23(3):389–391
Rosenthal B (2006) “Method and system for providing low cost, readily accessible healthcare.” U.S. Patent Application 11/105,220, filed October 19, 2006
Shaikh A, Uddin M, Elmagzoub MA, Alghamdi A (2020) PEMC: Power efficiency measurement calculator to compute power efficiency and CO2 emissions in Cloud Data Centers. IEEE Access
Singh S, Chana I, Buyya R (2020) Agri-info: cloud based autonomic system for delivering agriculture as a service. Internet Things 9:100131
Singh M, Krishnan R, Goswami B, Choudhury AD, Swapna P, Vellore R et al (2020) Fingerprint of volcanic forcing on the ENSO–Indian monsoon coupling. Sci Adv 6(38):eaba8164
Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (2013) Climate change 2013: the physical science basis. In: Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, 1535
Tang Z, Zhou X, Zhang F, Jia W, Zhao W (2018) Migration modeling and learning algorithms for containers in fog computing. IEEE Trans Serv Comput 12(5):712–725
Thi MT, Pierson JM, Da Costa G, Stolf P, Nicod JM, Rostirolla G, Haddad M (2019) Negotiation game for joint IT and energy management in green datacenters. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2019.11.018
Tuli S, Basumatary N, Gill SS, Kahani M, Arya RC, Wander GS, Buyya R (2020) HealthFog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Futur Gener Comput Syst 104:187–200
Tuli S, Mahmud R, Tuli S, Buyya R (2019) Fogbus: A blockchain-based lightweight framework for edge and fog computing. J Syst Softw 154:22–36
Tuli S, Tuli S, Wander G, Wander P, Gill SS, Dustdar S, Sakellariou R, Rana O (2020) Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions. Internet Technol Lett 3(2)e145:1–6
Tuli S, Tuli S, Tuli R, Gill SS (2020) “Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing.” Internet Things 100222
Tuli S, Gill SS, Casale G, Jennings NR (2020) iThermoFog: IoT Fog based automatic thermal profile creation for cloud data centers using artificial intelligence techniques. Internet Technol Lett 3(5):e198
Yang R, Yu FR, Si P, Yang Z, Zhang Y (2019) Integrated blockchain and edge computing systems: A survey, some research issues and challenges. IEEE Commun Surv Tutorials 21(2):1508–1532
Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32
Acknowledgements
We would like to thank the Editors (Prof. Paul Krause and Prof. Fatos Xhafa) and anonymous reviewers for their valuable comments and suggestions to help and improve our research paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Glossary of Terms
- Terms
-
Description
- Cloud Computing
-
It is on-demand cloud service (using resources such as processor, storage, memory and network) to the users via Internet
- Industry 4.0
-
Current trend of automation and data transfer in manufacturing technologies
- Climate Change
-
Change in the mean or basic state of the climate
- Eco-friendly
-
Anything that does not harm the environment
- Quality of Service (QoS)
-
It is a measurement in terms of performance parameters to evaluate the service quality
- Service Level Agreements (SLA)
-
SLA is an official document, which is signed between cloud user and cloud provider based on QoS requirements
- Agriculture 4.0
-
Atomization of Agriculture related aspects such as precision agriculture and big data analytics
- Healthcare 4.0
-
Management of vast amount of healthcare data efficiently
- Carbon Footprints
-
It is the amount of carbon released in the environment by the computing system
- Digitization of Economies
-
The transition of financial systems towards digital platforms
- COVID-19 Pandemic
-
It is a coronavirus disease 2019, which is caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Singh, M., Tuli, S., Butcher, R.J., Kaur, R., Gill, S.S. (2021). Dynamic Shift from Cloud Computing to Industry 4.0: Eco-Friendly Choice or Climate Change Threat. In: Krause, P., Xhafa, F. (eds) IoT-based Intelligent Modelling for Environmental and Ecological Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-030-71172-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-71172-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71171-9
Online ISBN: 978-3-030-71172-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)