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2022 | OriginalPaper | Chapter

QoS and Energy Efficiency Using Green Cloud Computing

Authors : Riman Mandal, Sourav Banerjee, Md Bagbul Islam, Pushpita Chatterjee, Utpal Biswas

Published in: Intelligent Internet of Things for Healthcare and Industry

Publisher: Springer International Publishing

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Abstract

Green cloud computing is an emerging technology. Cloud computing is already well accepted in the industry. Green cloud computing brings green computing into the cloud computing environment. The goal of this collaborative technology is to provide environment sustainability while providing cloud services in a cost-effective manner. Traditional data centers consume huge electricity generated from fossil fuels that increases the carbon footprint in the environment. Green cloud computing will ensure efficient service delivery with minimal energy consumption. Several security concerns also arise with new technology. Those security concerns and some solutions were discussed in this book chapter.

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S. Mathew, J. Varia, Overview of Amazon Web Services, Amaz. Whitepapers (2014)

C. Kotas, T. Naughton, N. Imam, A comparison of Amazon Web Services and Microsoft Azure cloud platforms for high performance computing, in 2018 IEEE International Conference on Consumer Electronics (ICCE), 2018, pp. 1–4

S. Challita, F. Zalila, C. Gourdin, P. Merle, A precise model for google cloud platform, in 2018 IEEE International Conference on Cloud Engineering (IC2E), 2018, pp. 177–183

X. Jia, Google cloud computing platform technology architecture and the impact of its cost, in 2010 Second World Congress on Software Engineering, 2010, vol. 2, pp. 17–20

S. Kale, Cloud Computing – Types of Cloud, ESDS, Jan. 18, 2021. https://www.esds.co.in/blog/cloud-computing-types-cloud/#sthash.yymu3L0e.dpbs (accessed Feb. 01, 2021)

R. Mandal, M.K. Mondal, S. Banerjee, C. Chakraborty, U. Biswas, A survey and critical analysis on energy generation from datacenter, in Data Deduplication Approaches, (Elsevier, 2021), pp. 203–230CrossRef

A. Marashi, Improving Data Center Power Consumption & Energy Efficiency, VXCHNGE, Feb. 12, 2020. https://www.vxchnge.com/blog/growing-energy-demands-of-data-centers (accessed Feb. 02, 2020)

F. Pearce, Energy Hogs: Can World’s Huge Data Centers Be Made More Efficient?, YaleEnvironment360, Apr. 03, 2018. https://e360.yale.edu/features/energy-hogs-can-huge-data-centers-be-made-more-efficient (accessed Apr. 02, 2020)

Facts and Figures, Air Transport Action Group, Sep. 2020. https://www.atag.org/facts-figures.html (accessed Feb. 02, 2020)

10.

A. Ohri, Green Cloud Computing: A Beginner’s Guide, Jigsaw Academy, Nov. 23, 2020. https://www.jigsawacademy.com/blogs/cloud-computing/green-cloud-computing/ (accessed Jan. 02, 2021)

11.

Y. Mathur, Green Cloud Computing, Lucid Outstanding Solutions, Sep. 22, 2018. https://lucidoutsourcing.com/blog/green-cloud-computing (accessed May 02, 2021)

12.

S.K. Garg, R. Buyya, Green cloud computing and environmental sustainability. Harnessing Green IT Princ. Pract. 2012, 315–340 (2012)

13.

J. Liu, F. Zhao, X. Liu, W. He, Challenges towards elastic power management in internet data centers, in 2009 29th ieee international conference on distributed computing systems workshops, 2009, pp. 65–72

14.

Y. S. Patel, N. Mehrotra, S. Soner, Green cloud computing: A review on Green IT areas for cloud computing environment, in 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 2015, pp. 327–332

15.

L. Liu et al., GreenCloud: a new architecture for green data center, in Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session, 2009, pp. 29–38

16.

L.-D. Radu, Green cloud computing: A literature survey. Symmetry 9(12), 295 (2017)CrossRef

17.

N. Xiong, W. Han, A. Vandenberg, Green cloud computing schemes based on networks: A survey. IET Commun. 6(18), 3294–3300 (2012)CrossRef

18.

B. Wadhwa, A. Verma, Energy and carbon efficient VM placement and migration technique for green cloud datacenters, in 2014 Seventh international conference on contemporary computing (IC3), 2014, pp. 189–193

19.

B. Wadhwa, A. Verma, Energy saving approaches for Green Cloud Computing: A review, in 2014 Recent Advances in Engineering and Computational Sciences (RAECS), 2014, pp. 1–6

20.

N. Sabry, P. Krause, Optimal green virtual machine migration model. Int. J. Bus. Data Commun. Netw. IJBDCN 9(3), 35–52 (2013)

21.

Q. Chen, P. Grosso, K. van der Veldt, C. de Laat, R. Hofman, H. Bal, Profiling energy consumption of VMs for green cloud computing, in 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, 2011, pp. 768–775

22.

G. B. Fioccola, P. Donadio, R. Canonico, G. Ventre, Dynamic routing and virtual machine consolidation in green clouds, in 2016 IEEE international conference on cloud computing technology and science (CloudCom), 2016, pp. 590–595

23.

J. Li et al., CyberGuarder: A virtualization security assurance architecture for green cloud computing. Futur. Gener. Comput. Syst. 28(2), 379–390 (2012)CrossRef

24.

S.K. Mishra et al., Energy-efficient VM-placement in cloud data center. Sustain. Comput. Inform. Syst. 20, 48–55 (2018)

25.

A. Khosravi, S. K. Garg, R. Buyya, Energy and carbon-efficient placement of virtual machines in distributed cloud data centers, in European Conference on Parallel Processing, 2013, pp. 317–328

26.

W. Wei, H. Gu, W. Lu, T. Zhou, X. Liu, Energy efficient virtual machine placement with an improved ant colony optimization over data center networks. IEEE Access 7, 60617–60625 (2019)CrossRef

27.

B. Kar, E.H.-K. Wu, Y.-D. Lin, Energy cost optimization in dynamic placement of virtualized network function chains. IEEE Trans. Netw. Serv. Manag. 15(1), 372–386 (2017)CrossRef

28.

B. Dougherty, J. White, D.C. Schmidt, Model-driven auto-scaling of green cloud computing infrastructure. Futur. Gener. Comput. Syst. 28(2), 371–378 (2012)CrossRef

29.

R. Mandal, M.K. Mondal, S. Banerjee, U. Biswas, An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing. J. Supercomput. 76(9), 7374–7393 (Sep. 2020). https://doi.org/10.1007/s11227-020-03165-6 CrossRef

30.

F. Farahnakian et al., Energy-aware dynamic VM consolidation in cloud data centers using ant colony system, in 2014 IEEE 7th International Conference on Cloud Computing, 2014, pp. 104–111

31.

D.-K. Kang, F. Alhazemi, S.-H. Kim, C.-H. Youn, Dynamic virtual machine consolidation for energy efficient cloud data centers, in International Conference on Cloud Computing, 2015, pp. 70–80

32.

N.K. Biswas, S. Banerjee, U. Biswas, U. Ghosh, An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing. Sustain. Energy Technol. Assess. 45, 101087 (2021)

33.

M. H. Al Shayeji, M. Samrajesh, An energy-aware virtual machine migration algorithm, in 2012 International Conference on Advances in Computing and Communications, 2012, pp. 242–246

34.

V. R. Reguri, S. Kogatam, M. Moh, Energy efficient traffic-aware virtual machine migration in green cloud data centers, in 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), 2016, pp. 268–273

35.

S. Banerjee, R. Mandal, U. Biswas, An approach towards amelioration of an efficient VM allocation policy in cloud computing domain. Wirel. Pers. Commun. 98(2), 1799–1820 (Jan. 2018). https://doi.org/10.1007/s11277-017-4946-0 CrossRef

36.

L. Qi, Y. Chen, Y. Yuan, S. Fu, X. Zhang, X. Xu, A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web 23(2), 1275–1297 (2020)CrossRef

37.

S. Banerjee, A. Roy, A. Chowdhury, R. Mutsuddy, R. Mandal, U. Biswas, An approach toward amelioration of a new cloudlet allocation strategy using Cloudsim. Arab. J. Sci. Eng. 43(2), 879–902 (Feb. 2018). https://doi.org/10.1007/s13369-017-2781-y CrossRef

38.

Q. Tang, S.K.S. Gupta, G. Varsamopoulos, Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: A cyber-physical approach. IEEE Trans. Parallel Distrib. Syst. 19(11), 1458–1472 (2008). https://doi.org/10.1109/TPDS.2008.111 CrossRef

39.

T. V. T. Duy, Y. Sato, and Y. Inoguchi, Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing, in 2010 IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010, pp. 1–8, doi: https://doi.org/10.1109/IPDPSW.2010.5470908

40.

H. Chen, X. Zhu, H. Guo, J. Zhu, X. Qin, J. Wu, Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment. J. Syst. Softw. 99, 20–35 (2015)CrossRef

41.

S. Srikantaiah, A. Kansal, and F. Zhao, Energy Aware Consolidation for Cloud Computing, Dec. 2008, USENIX HotPower’08: Workshop on Power Aware Computing and Systems at OSDI., [Online]. Available: https://www.microsoft.com/en-us/research/publication/energy-aware-consolidation-for-cloud-computing/

42.

G. Prekas, M. Primorac, A. Belay, C. Kozyrakis, E. Bugnion, Energy proportionality and workload consolidation for latency-critical applications, in Proceedings of the Sixth ACM Symposium on Cloud Computing, Kohala Coast Hawaii, Aug. 2015, pp. 342–355, doi: https://doi.org/10.1145/2806777.2806848

43.

R. Reddy, A. Kathpal, J. Basak, R. Katz, Data layout for power efficient archival storage systems, in Proceedings of the Workshop on Power-Aware Computing and Systems, 2015, pp. 16–20

44.

D. K. Lee, K. Koh, PDC-NH: Popular data concentration on NAND flash and hard disk drive, in 2009 10th IEEE/ACM International Conference on Grid Computing, 2009, pp. 196–200, doi: https://doi.org/10.1109/GRID.2009.5353061

45.

S. Iwata, K. Shiozawa, A simulation result of replicating data with another layout for reducing media exchange of cold storage, (2016)

46.

N. Zhao, J. Wan, J. Wang, C. Xie, GreenCHT: A power-proportional replication scheme for consistent hashing based key value storage systems, in 2015 31st Symposium on Mass Storage Systems and Technologies (MSST), 2015, pp. 1–6

47.

A. Shafiee, et al., ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars, in 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), 2016, pp. 14–26, https://doi.org/10.1109/ISCA.2016.12

48.

M. M. Ozdal, et al., Energy Efficient Architecture for Graph Analytics Accelerators, in 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), 2016, pp. 166–177, https://doi.org/10.1109/ISCA.2016.24

49.

W. Itani, C. Ghali, A. Kayssi, A. Chehab, I. Elhajj, G-route: An energy-aware service routing protocol for green cloud computing. Clust. Comput. 18(2), 889–908 (Jun. 2015). https://doi.org/10.1007/s10586-015-0443-y CrossRef

50.

S.K. Garg, C.S. Yeo, A. Anandasivam, R. Buyya, Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers. J. Parallel Distrib. Comput. 71(6), 732–749 (Jun. 2011). https://doi.org/10.1016/j.jpdc.2010.04.004 CrossRefMATH

51.

S.-Y. Jing, S. Ali, K. She, Y. Zhong, State-of-the-art research study for green cloud computing. J. Supercomput. 65(1), 445–468 (Jul. 2013). https://doi.org/10.1007/s11227-011-0722-1 CrossRef

52.

S. P. Reddy, H. K. S. Chandan, Energy aware scheduling of real-time and non real-time tasks on cloud processors (Green Cloud Computing), in International Conference on Information Communication and Embedded Systems (ICICES2014), 2014, pp. 1–5, https://doi.org/10.1109/ICICES.2014.7033827

53.

J. Conejero, O. Rana, P. Burnap, J. Morgan, B. Caminero, C. Carrión, Analyzing Hadoop power consumption and impact on application QoS. Futur. Gener. Comput. Syst. 55, 213–223 (Feb. 2016). https://doi.org/10.1016/j.future.2015.03.009 CrossRef

54.

C. Cappiello, N.T.T. Ho, B. Pernici, P. Plebani, M. Vitali, CO ₂ -aware adaptation strategies for cloud applications. IEEE Trans. Cloud Comput. 4(2), 152–165 (Apr. 2016). https://doi.org/10.1109/TCC.2015.2464796 CrossRef

55.

S. R. Hussein, Y. Alkabani, H. K. Mohamed, Green cloud computing: Datacenters power management policies and algorithms, in 2014 9th International Conference on Computer Engineering Systems (ICCES), 2014, pp. 421–426, https://doi.org/10.1109/ICCES.2014.7030998

56.

L. A. Rocha, E. Cardozo, A Hybrid Optimization Model for Green Cloud Computing, in 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, 2014, pp. 11–20, https://doi.org/10.1109/UCC.2014.9

57.

D. Kliazovich, P. Bouvry, S.U. Khan, GreenCloud: A packet-level simulator of energy-aware cloud computing data centers. J. Supercomput. 62(3), 1263–1283 (Dec. 2012). https://doi.org/10.1007/s11227-010-0504-1 CrossRef

58.

J. Mao, T. Cao, X. Peng, T. Bhattacharya, W.-S. Ku, and X. Qin, “Security-Aware Energy Management in Clouds,” in 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), 2020, pp. 284–293, https://doi.org/10.1109/TPS-ISA50397.2020.00044

59.

H.A. Hassan, S.A. Salem, E.M. Saad, A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment. Futur. Gener. Comput. Syst. 112, 431–448 (Nov. 2020). https://doi.org/10.1016/j.future.2020.05.040 CrossRef

60.

L.M. Amulu, R. Ramraj, Combinatorial meta-heuristics approaches for DVFS-enabled green clouds. J. Supercomput. 76(8), 5825–5834 (Aug. 2020). https://doi.org/10.1007/s11227-019-02997-1 CrossRef

61.

J. He, A. Chaintreau, C. Diot, A performance evaluation of scalable live video streaming with nano data centers. Comput. Netw. 53(2), 153–167 (Feb. 2009). https://doi.org/10.1016/j.comnet.2008.10.014 CrossRefMATH

62.

N. Laoutaris, P. Rodriguez, L. Massoulie, ECHOS: Edge capacity hosting overlays of nano data centers. ACM SIGCOMM Comput. Commun. Rev. 38(1), 51–54 (Jan. 2008). https://doi.org/10.1145/1341431.1341442 CrossRef

63.

V. Valancius, N. Laoutaris, L. Massoulié, C. Diot, P. Rodriguez, Greening the internet with nano data centers, in Proceedings of the 5th international conference on Emerging networking experiments and technologies - CoNEXT ‘09, Rome, Italy, 2009, p. 37, https://doi.org/10.1145/1658939.1658944

64.

A.D. Borah, D. Muchahary, S.K. Singh, J. Borah, Power saving strategies in green cloud computing systems. Int. J. Grid Distrib. Comput 8(1), 299–306 (Feb. 2015). https://doi.org/10.14257/ijgdc.2015.8.1.28 CrossRef

65.

K. Kaur, T. Dhand, N. Kumar, S. Zeadally, Container-as-a-service at the edge: Trade-off between energy efficiency and service availability at Fog Nano Data Centers. IEEE Wirel. Commun. 24(3), 48–56 (2017). https://doi.org/10.1109/MWC.2017.1600427 CrossRef

66.

D. G. Andersen, J. Franklin, M. Kaminsky, A. Phanishayee, L. Tan, V. Vasudevan, FAWN: A fast array of wimpy nodes, in Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, 2009, pp. 1–14

67.

L.A. Barroso, FAWN: A fast array of wimpy nodes: Technical perspective. Commun. ACM 54(7), 100–100 (Jul. 2011). https://doi.org/10.1145/1965724.1965746 CrossRef

68.

A. Beloglazov, R. Buyya, Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exp. 24(13), 1397–1420 (2012)CrossRef

69.

Z. Li, X. Yu, L. Yu, S. Guo, V. Chang, Energy-efficient and quality-aware VM consolidation method. Futur. Gener. Comput. Syst. 102, 789–809 (Jan. 2020). https://doi.org/10.1016/j.future.2019.08.004 CrossRef

70.

K. Haghshenas, S. Mohammadi, Prediction-based underutilized and destination host selection approaches for energy-efficient dynamic VM consolidation in data centers. J. Supercomput. 76(12), 10240–10257 (Dec. 2020). https://doi.org/10.1007/s11227-020-03248-4 CrossRef

71.

R. Sinha, Cybersecurity in The Cloud: Here’s What It Means, ESDS, Jul. 21, 2020. https://www.esds.co.in/blog/cybersecurity-in-the-cloud-heres-what-it-means/#sthash.lom6AXHa.dpbs (accessed Oct. 02, 2021)

72.

K. S. Gill, A. Sharma, IDPS based framework for security in green cloud computing and comprehensive review on existing frameworks and security issues, in 2015 International Conference on Computing, Communication and Security (ICCCS), 2015, pp. 1–6, https://doi.org/10.1109/CCCS.2015.7374153

73.

K.A. Torkura, M.I.H. Sukmana, F. Cheng, C. Meinel, CloudStrike: Chaos engineering for security and resiliency in cloud infrastructure. IEEE Access 8, 123044–123060 (2020). https://doi.org/10.1109/ACCESS.2020.3007338 CrossRef

Title: QoS and Energy Efficiency Using Green Cloud Computing
Authors: Riman Mandal
Sourav Banerjee
Md Bagbul Islam
Pushpita Chatterjee
Utpal Biswas
Publisher: Springer International Publishing
Book: Intelligent Internet of Things for Healthcare and Industry
Print ISBN: 978-3-030-81472-4

Electronic ISBN: 978-3-030-81473-1

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-81473-1_14

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