Sustainability was first introduced by UNEP in Rio de Janeiro (1992) as one of the main goals of future humankind development. The United Nations declared sustainability as the guideline for 21st century in Rio de Janeiro (Kloepffer, 2008). Sustainability is a concept which considers environmental, social and economic aspects as three dimensions which has been denote as three pillars of sustainability. The objective of the PERCCOM program is to understand the existing sustainable challenges in the society and to address them with
46
ICT education to build greener and energy efficient systems (Porras et al., 2016) (Rondeau, Andersson and Porras, 2019). This research work is directly correlated with sustainability.
This work contributed towards sustainability by making efficient use of renewable energy and cost-saving approach.
Figure. 21. Three pillars of sustainability
Considering the three-pillar approach of sustainability, this research work directly contributes to two pillars from three of them.
I. Environmental: This reseach work focuses on reduction of carbon emission by integrating renewable energy in micro-datacenter. This is achieved by considering carbon-aware load-shifting paradigm. It increase the energy efficiency of datacenters by reducing significant carbon emissions which as a result increases datacenters lifetime.
II. Economical: Previously descibed carbon reduction goal of this research work translates to reduction of energy production and utility costs. Datacenters depending mostly on renewable energy can cut-off the cost of drawing energy from high-cost power grid.
47 4.4.1 Five Dimensions of Sustainability
Figure. 22. Five dimensions of sustainability
Five dimensions of sustainability is propsed by Becker et al. (2015). In the following section, impact and contribution of this research work to acheive sustainabilty is discussed.
• Individual: The result of this research work represents the carbon index calculation for serving each incoming workload by datacenter. Each record of carbon emission shows how a single request can contribute in carbon footprint. This study helps individual to increase awareness and practice sustainability.
• Social: This study can help to establish trust between people and service provider.
As the cost of service to end user is calculated based on the energy usage, the study
48
results aim to show energy source for each. People can have estimatation about their energy costs as well as carbon footprint.
• Economic: The main economic aspect of this reasearch work is that it helps to reduce energy expense. The energy source of datacenters are partially replaced by renewable source during the availability of solar energy in daytime. During this time, a workload served by solar energy has no impact on carbon emission.
• Technical: In this work, load is shifted where renewable energy is available. A better energy management can be done by load-shifting.
• Environmental: Integrating renewable energy in datacenter can significantly reduce carbon emission in environemnt. Also the carbon-aware load-shifting of this reserach work aims to serve workloads with renewable sources as much as possible.
These five dimensions of sustainability analysis also consider immediate, long-run and future impacts. Based on the model, a sustainability analysis is conducted in figure 21.
49
5 CONCLUSION AND FUTURE WORK
This thesis work has investigated the energy consumption behavior and performance of micro-distributed datacenters both in simulation environment and in real cloud infrastructure. First, the experiments are done in simulation environments which cannot mimic all the possible real-world scenario. Later, results obtained from cloud environment overcome the drawback of testing datacenter energy consumption and load-shifting scenario with simulated workloads. The main focus is on understanding algorithms for geographical load-shifting in interconnected small-scale datacenters. Integrating renewable sources in datacenters considering load-shifting overall reduces the energy usage and operational costs. From the observations made, it is clear that serving workloads solely from grid energy results high carbon-emission and not cost effective as well. So, it is important to include renewable energy generation and load-shifting strategy in datacenters.
Our experiments highlight that carbon-aware load-shifting can provide an effective tool for reducing carbon emission. All these cases ease the incorporation of renewables and reduce datacenters brown energy consumption.
Moreover, an energy-efficient system is effective when it meets the QoS requirements.
Therefore, selection of load-shifting algorithms for datacenters job scheduling plays an important role. Both RR and WRR algorithms can be a good combination in small-scale edge datacenter to analyze the load-shifting pattern by following renewable sources.
There can be several interesting directions for future work that are motivated by the studies in this work. With respect to the design of load-shifting algorithms, this work didn’t consider the switching cost (in terms of delay) associated with workload transferring from a datacenter to another. Our work also ignores server on/off scenario which is quite common in general.
50
REFERENCES
1. Number of data centers worldwide 2015-2021 | Statistic (no date). Available at:
https://www.statista.com/statistics/500458/worldwide-datacenter-and-it-sites
2. R. Buyya, C. Vecchiola, and S. Selvi, Mastering Cloud Computing: Foundations and Applications Programming. Amsterdam, The Netherlands: Elsevier, 2013 3. Intel Intelligent Power Technology, Canalys, Shanghai, China, 2012. [Online].
Available: http://www.canalys.com/newsroom/data-centerinfrastructure-market-will-be-worth-152-billion-2016
4. Canalys Newsroom- Data center infrastructure market will be worth $152 billion by 2016 (2012). Available at: https://www.canalys.com/newsroom/data-center-infrastructure-market-will-be-worth-152-billion-2016
5. Dayarathna, M., Wen, Y. and Fan, R. (2016) ‘Data center energy consumption modeling: A survey’, IEEE Communications Surveys and Tutorials. IEEE, 18(1), pp. 732–794. doi: 10.1109/COMST.2015.2481183.
6. Whitehead, B. et al. (2014) ‘Assessing the environmental impact of data centres part 1: Background, energy use and metrics’, Building and Environment. Elsevier Ltd, 82(December), pp. 151–159. doi: 10.1016/j.buildenv.2014.08.021.
7. Mathew, V., Sitaraman, R. K. and Shenoy, P. (2012) ‘Energy-aware load balancing in content delivery networks’, Proceedings - IEEE INFOCOM. IEEE, pp. 954–962.
doi: 10.1109/INFCOM.2012.6195846.
8. Corcoran, P. and Andrae, A. (2017) ‘Emerging Trends in Electricity Consumption for Consumer ICT Authors’, pp. 1–56.
9. The E and Program (2014) ‘A joint initiative of Australian, State and Territory and New Zealand Governments. Energy Efficiency Policy Options for Australian and New Zealand Data Centres Final Report Consumer Research Associates’, (April).
Available at: http://creativecommons.org/licences/by/3.0/au.
10. Top 10 Energy-Saving Tips for a Greener Data Center (2007). Available at:
http://static.infotech.com/downloads/samples/070411_premium_oo_greendc_top_1 0.pdf
11. Aazam, M. and Huh, E. N. (2015) ‘Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT’, Proceedings - International
51
Conference on Advanced Information Networking and Applications, AINA. IEEE, 2015-April, pp. 687–694. doi: 10.1109/AINA.2015.254.
12. Kim, B. and Lee, B. (2016) ‘Integrated management system for distributed micro-datacenters’, International Conference on Advanced Communication Technology, ICACT. Global IT Research Institute (GiRI), 2016-March, pp. 466–469. doi:
10.1109/ICACT.2016.7423434.
13. Andrae, A. S. G. (2017) ‘Total Consumer Power Consumption Forecast’, Nordic Digital Business Summit, (October). Available at:
https://www.researchgate.net/publication/320225452_Total_Consumer_Power_Co nsumption_Forecast.
14. Fiorani, M. et al. (2016) ‘Flexible Architecture and Control Strategy for Metro-Scale Networking of Geographically Distributed Data Centers’, 2016 European Conference on Optical Communication (ECOC), (1), pp. 1–3.
15. Zhao, Y. et al. (2014) ‘Experimental performance evaluation of software defined networking (SDN) based data communication networks for large scale flexi-grid optical networks’, Optics Express, 22(8), p. 9538. doi: 10.1364/oe.22.009538.
16. Reddy, V. D. et al. (2017) ‘Metrics for Sustainable Data Centers’, IEEE Transactions on Sustainable Computing, 2(3), pp. 290–303. doi:
10.1109/tsusc.2017.2701883.
17. Data Centers ‘Going Green’ to Reduce a Carbon Footprint Larger than the Airline Industry - Data Economy (no date). Available at: https://data-economy.com/data-centers-going-green-to-reduce-a-carbon-footprint-larger-than-the-airline-industry/
18. Da Silva, R. A. C. and Da Fonseca, N. L. S. (2016) ‘Energy-aware migration of groups of virtual machines in distributed data centers’, 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings. IEEE, pp. 1–6.
doi: 10.1109/GLOCOM.2016.7841803.
19. Life On The Edge: Why Micro Data Centers Are The Next Frontier (no date).
Available at: https://www.crn.com/news/data-center/300096141/life-on-the-edge-why-micro-data-centers-are-the-next-frontier.htm
20. Micro Data Centers Evolve to Fit New Business Requirements of Edge Computing |
Network World (no date). Available at:
https://www.networkworld.com/article/3365178/micro-data-centers-evolve-to-fit-52
new-business-requirements-of-edge-computing.html
21. Singh, A., Goyal, P. and Batra, S. (2010) ‘An optimized round robin scheduling algorithm for CPU scheduling’, International Journal on Computer Science and Engineering, 02(07), pp. 2383–2385.
24. Fernández-Cerero, D., Fernández-Montes, A. and Velasco, F. (2018) ‘Productive Efficiency of Energy-Aware Data Centers’, Energies, 11(8), p. 2053. doi:
10.3390/en11082053.
25. Koomey, J. G. and Ph, D. (2011) ‘Growing in Data Center Electircity Use 2005 to 2010’.
26. Van Heddeghem, W. et al. (2014) ‘Trends in worldwide ICT electricity consumption from 2007 to 2012’, Computer Communications, 50(September), pp.
64–76. doi: 10.1016/j.comcom.2014.02.008.
27. Cook, G. et al. (2017) ‘Click Clean’, Clicking Clean: Who Is Winning the Race To Build a Green Internet?, p. 102.
28. Da Silva, R. A. C. and Da Fonseca, N. L. S. (2016) ‘Energy-aware migration of groups of virtual machines in distributed data centers’, 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings. IEEE, pp. 1–6.
doi: 10.1109/GLOCOM.2016.7841803. Natural Gas / Renewable Energy Workshops, Colorado, US, 2001.
53
31. Li, C. et al. (2011) ‘SolarCore: Solar energy driven multi-core architecture power management’, Proceedings - International Symposium on High-Performance Computer Architecture. IEEE, pp. 205–216. doi: 10.1109/HPCA.2011.5749729.
32. Goiri, Í. et al. (2012) ‘GreenHadoop: leveraging green energy in data-processing frameworks’, Proceedings of the 7th ACM european conference on Computer Systems - EuroSys ’12, p. 57. doi: 10.1145/2168836.2168843.
33. Li, C., Qouneh, A. and Li, T. (2012) ‘iSwitch: Coordinating and optimizing renewable energy powered server clusters’, Proceedings - International Symposium on Computer Architecture. IEEE, 00©, pp. 512–523. doi:
10.1109/ISCA.2012.6237044.
34. Goiri, Í. et al. (2013) ‘Parasol and GreenSwitch’, ACM SIGPLAN Notices, 48(4), p.
51. doi: 10.1145/2499368.2451123.
35. Li, C., Zhou, R. and Li, T. (2013) ‘Enabling distributed generation powered sustainable high-performance data center’, Proceedings - International Symposium on High-Performance Computer Architecture. IEEE, pp. 35–46. doi:
10.1109/HPCA.2013.6522305.
36. England, I. new (2009) ‘Overview of the Smart Grid — Policies , Initiatives , and Needs’, Initiatives, ©.
37. Rahman, A., Liu, X. and Kong, F. (2014) ‘A survey on geographic load balancing based data center power management in the smart grid environment’, IEEE Communications Surveys and Tutorials. IEEE, 16(1), pp. 214–233. doi:
10.1109/SURV.2013.070813.00183.
38. Logenthiran, T., Srinivasan, D. and Shun, T. Z. (2012) ‘Demand side management in smart grid using heuristic optimization’, IEEE Transactions on Smart Grid.
IEEE, 3(3), pp. 1244–1252. doi: 10.1109/TSG.2012.2195686.
39. Rao, L. et al. (2010) ‘MEC-IDC : Joint Load Balancing and Power Control for Distributed Internet Data Centers’, Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems - ICCPS ’10, pp. 188–197. doi:
10.1145/1795194.1795220.
40. Qureshi, A. et al. (2009) ‘Cutting the electric bill for internet-scale systems’, ACM SIGCOMM Computer Communication Review, 39(4), p. 123. doi:
10.1145/1594977.1592584.
54
41. Rao, L. et al. (2010) ‘Minimizing electricity cost: Optimization of distributed internet data centers in a multi-electricity-market environment’, Proceedings - IEEE INFOCOM. IEEE, pp. 1–9. doi: 10.1109/INFCOM.2010.5461933.
42. Yao, Y. et al. (2012) ‘Data centers power reduction: A two time scale approach for delay tolerant workloads’, Proceedings - IEEE INFOCOM. IEEE, pp. 1431–1439.
doi: 10.1109/INFCOM.2012.6195508.
43. Zhang, Y., Wang, Y. and Wang, X. (2011) ‘Capping the electricity cost of cloud-scale data centers with impacts on power markets’, p. 271. doi:
10.1145/1996130.1996170.
44. Narayan Sankaranarayanan, Ananth & Sharangi, Somsubhra & Fedorova, Alexandra. (2011). Global cost diversity aware dispatch algorithm for heterogeneous data centers.. ACM SIGMETRICS Performance Evaluation Review.
39. 289-294. 10.1145/2160803.2160834.
45. Li, J. et al. (2012) ‘Towards optimal electric demand management for internet data centers’, IEEE Transactions on Smart Grid. IEEE, 3(1), pp. 183–192. doi:
10.1109/TSG.2011.2165567.
46. Parolini, L. et al. (2012) ‘A cyber-physical systems approach to data center modeling and control for energy efficiency’, Proceedings of the IEEE. IEEE, 100(1), pp. 254–268. doi: 10.1109/JPROC.2011.2161244.
47. Le, K. et al. (2010) ‘Capping the brown energy consumption of internet services at low cost’, 2010 International Conference on Green Computing, Green Comp 2010.
IEEE, pp. 3–14. doi: 10.1109/GREENCOMP.2010.5598305.
48. Doyle, J., O’Mahony, D. and Shorten, R. (2011) ‘Server selection for carbon emission control’, p. 1. doi: 10.1145/2018536.2018538.
49. Krioukov, A. et al. (2011) ‘Integrating Renewable Energy Using Data Analytics Systems : Challenges and Opportunities’, Bulletin of the IEEE Computer Society Technical Committee, pp. 1–9.
50. Carroll, R. et al. (2011) ‘Dynamic optimization solution for green service migration in data centres’, IEEE International Conference on Communications. IEEE, pp. 1–
6. doi: 10.1109/icc.2011.5963030.
51. Zhang, Y., Wang, Y. and Wang, X. (2011) ‘GreenWare: Greening cloud-scale data centers to maximize the use of renewable energy’, Lecture Notes in Computer
55
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7049 LNCS, pp. 143–164. doi: 10.1007/978-3-642-25821-3_8.
52. Sami, M., Haggag, M. and Salem, D. (2015) ‘Resource Allocation and Server Consolidation Algorithms for Green Computing’, International Journal of Scientific & Engineering Research, 6(12), pp. 313–316. Available at:
http://www.ijser.org.
53. Dash, A. R., Sahu, S. kumar and Samantra, S. K. (2015) ‘An Optimized Round Robin CPU Scheduling Algorithm with Dynamic Time Quantum’, International Journal of Computer Science, Engineering and Information Technology, 5(1), pp.
07–26. doi: 10.5121/ijcseit.2015.5102.
54. Alsheikhy, A., Ammar, R. and Elfouly, R. (2016) ‘An improved dynamic Round Robin scheduling algorithm based on a variant quantum time’, 2015 11th International Computer Engineering Conference: Today Information Society What’s Next?, ICENCO 2015, pp. 98–104. doi: 10.1109/ICENCO.2015.7416332.
55. Wierman, A. et al. (2014) ‘Greening Geographical Load Balancing’, IEEE/ACM Transactions on Networking, 23(2), pp. 657–671. doi: 10.1109/tnet.2014.2308295.
56. Lin, M. et al. (2011) ‘Dynamic right-sizing for power-proportional data centers’, 2011 Proceedings IEEE INFOCOM. IEEE, pp. 1098–1106. doi:
10.1109/INFCOM.2011.5934885.
57. Pakbaznia, E. and Pedram, M. (2009) ‘Minimizing data center cooling and server power costs’, p. 145. doi: 10.1145/1594233.1594268.
58. Nadjaran Toosi, A. et al. (2017) ‘Renewable-aware geographical load balancing of web applications for sustainable data centers’, Journal of Network and Computer Applications. Elsevier Ltd, 83(January), pp. 155–168. doi:
10.1016/j.jnca.2017.01.036.
59. Zhou, Z. et al. (2013) ‘Carbon-aware load balancing for geo-distributed cloud services’, Proceedings - IEEE Computer Society’s Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS. IEEE, pp. 232–241. doi:
10.1109/MASCOTS.2013.31.
60. Lintner, W., Tschudi, B. and VanGeet, O. (2011) ‘Best Practices Guide for
Energy-56
Efficient Data Center Design’, U.S Department of Energy, (March), pp. i–24.
Available at:
http://www1.eere.energy.gov/femp/pdfs/eedatacenterbestpractices.pdf.
61. Zhou, Z. et al. (2013) ‘Carbon-aware load balancing for geo-distributed cloud services’, Proceedings - IEEE Computer Society’s Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS. IEEE, pp. 232–241. doi:
10.1109/MASCOTS.2013.31.
62. Neglia, G., Sereno, M. and Bianchi, G. (2016) ‘Geographical Load Balancing across Green Datacenters’, ACM SIGMETRICS Performance Evaluation Review, 44(2), pp. 64–69. doi: 10.1145/3003977.3003998.
63. Kothari, C. R. (2004) Research Methodology: Methods and Techniques. New Age
International (P) Ltd. Available at:
65. Dooley, K. (2015) ‘Simulation research methods’, (August).
66. Harrison, J. R. et al. (2007) ‘Simulation modeling in organizational and management research. Academy of Management Review, to appear’, Academy of Management Review, 32(4), pp. 1229–1245. doi: 10.1.1.125.2188.
67. Davis, J. P., Eisenhardt, K. M. and Bingham, C. B. (2007) ‘Developing Theory through Simulation Methods’, 32(2), pp. 1–21. doi: 10.5465/AMR.2007.24351453.
68. Colin, K. (2017) Power consumption calculator for Ubuntu Linux. Available at:
Farrington, H. B. Nembhard, D. T. Sturrock, and G. W. Evans, eds.’, pp. 7–13.
57
71. Fishman, G. S. (2001) Discrete-Event Simulation : Modeling, Programming, and Analysis. Springer New York.
72. Arlitt, M. F. and Williamson, C. L. (1996) ‘Web Server Workload Characterization: The Search for Invariant’, in. Available at:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.41.6586&rep=rep1&type
74. Porras, J. et al. (2016) ‘PERCCOM: A master program in pervasive computing and COMmunications for sustainable development’, Proceedings - 2016 IEEE 29th Conference on Software Engineering Education and Training, CSEEandT 2016.
IEEE, pp. 204–212. doi: 10.1109/CSEET.2016.39.
75. Kloepffer, W. (2008) ‘State-of-the-Art Life Cycle Sustainability Assessment of Products State-of-the-Art in Life Cycle Sustainability Assessment (LCSA)’, Int J LCA, 13(2), pp. 89–95. doi: 10.1065/lca2008.02.376.
76. Becker, C. et al. (2015) ‘Requirements: The Key to Sustainability’, IEEE Software, 33(1), pp. 56–65. doi: 10.1109/ms.2015.158.
77. Reiss, C., Wilkes, J. and Hellerstein, J. (2011) ‘Google cluster-usage traces:
format+ schema’, Google Inc., …, pp. 1–14. doi: 10.1007/978-3-540-69057-3_88.
78. Ganesan, M. (no date) ‘ENERGY-EFFICIENT CLOUD INFRASTRUCTURE FOR IoT BIG DATA PROCESSING’.
79. Duboc, L, Betz, S, Penzenstadler, B, Akinli Kocak, S, Chitchyan, R, Leifler, O, Porras, J, Seyff, N & Venters, CC 2019, Do we really know what we are building?
Raising awareness of potential Sustainability Effects of Software Systems in Requirements Engineering. in 27th IEEE International Requirements Engineering Conference. 27th IEEE International Requirements Engineering Conference, Jeju Island, Korea, Republic of, 23/09/19
80. Rondeau, E., Andersson, K. and Porras, J. (2019) ‘Education in Green ICT and
58
Control of Smart Systems : A First Hand Experience from the International PERCCOM Masters Programme’, (July).
59
APPENDIX
Screenshot of experiments of phase-3 in Amazon cloud platform.
60 REST API to get data – GET method
http://13.238.16.58:80/mdc-base/available Sample output: