Advertisement

A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption

  • Haruna ChiromaEmail author
  • Usman Ali Abdullahi
  • Ibrahim Abaker Targio Hashem
  • Younes Saadi
  • Rawaa Dawoud Al-Dabbagh
  • Muhammad Murtala Ahmad
  • Gbenga Emmanuel Dada
  • Sani Danjuma
  • Jaafar Zubairu Maitama
  • Adamu Abubakar
  • Shafi’i Muhammad Abdulhamid
Chapter
Part of the Green Energy and Technology book series (GREEN)

Abstract

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amount of energy, especially during the training phase. The transmission of big data between service providers, users and data centres emits carbon dioxide as a result of high power consumption. This chapter proposes a theoretical framework for big data analytics using computational intelligent algorithms that has the potential to reduce energy consumption and enhance performance. We suggest that researchers should focus more attention on the issue of energy within big data analytics in relation to computational intelligent algorithms, before this becomes a widespread and urgent problem.

Keywords

Big data analytics Energy Cluster systems Computational intelligent algorithms Artificial neural networks Cuckoo search algorithm 

References

  1. 1.
    IEA (2016, 5 May, 2017) World Energy Outlook. Available: http://www.iea.org/newsroom/news/2016/november/world-energy-outlook-2016.html
  2. 2.
    Horn M, Mirzatuny M (2013) Mining big data to transform electricity. In: Broadband networks, smart grids and climate change. Springer, Berlin, pp 47–58Google Scholar
  3. 3.
    Shojafar M, Cordeschi N, Amendola D, Baccarelli E (2015) Energy-saving adaptive computing and traffic engineering for real-time-service data centers. In: 2015 IEEE international conference on communication workshop (ICCW), pp 1800–1806Google Scholar
  4. 4.
    Yu S, Wang C, Liu K, Zomaya AY (2016) Editorial for IEEE access special section on theoretical foundations for big data applications: challenges and opportunities. IEEE Access 4:5730–5732CrossRefGoogle Scholar
  5. 5.
    Salinas S, Chen X, Ji J, Li P (2016) A tutorial on secure outsourcing of large-scale computations for big data. IEEE Access 4:1406–1416CrossRefGoogle Scholar
  6. 6.
    Wu K, Barker RJ, Kim MA, Ross KA (2013) Navigating big data with high-throughput, energy-efficient data partitioning. In: ACM SIGARCH computer architecture news, 2013, pp 249–260CrossRefGoogle Scholar
  7. 7.
    Li C, Zu Y, Hou B (2016) A feature selection method of power consumption data. In: International conference on computational science and its applications, 2016, pp 547–554Google Scholar
  8. 8.
    Baker T, Al-Dawsari B, Tawfik H, Reid D, Ngoko Y (2015) GreeDi: an energy efficient routing algorithm for big data on cloud. Ad Hoc Netw 35:83–96CrossRefGoogle Scholar
  9. 9.
    Chiroma H, Abdul-Kareem S, Khan A, Nawi NM, Gital AYU, Shuib L et al (2015) Global warming: predicting OPEC carbon dioxide emissions from petroleum consumption using neural network and hybrid cuckoo search algorithm. PloS One 10:e0136140CrossRefGoogle Scholar
  10. 10.
    Li R, Harai H, Asaeda H (2015) An aggregatable name-based routing for energy-efficient data sharing in big data era. IEEE Access 3:955–966CrossRefGoogle Scholar
  11. 11.
    Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: OSDI’04 Proceedings of the 6th conference on symposium on operating systems design and implementation (Int J Eng Sci Invent). URL: http://static.googleusercontent.com/media/research.google.com (diunduh pada 2015-05-10), pp 10–100
  12. 12.
    Kambatla K, Kollias G, Kumar V, Grama A (2014) Trends in big data analytics. J Parallel Distrib Comput 74:2561–2573CrossRefGoogle Scholar
  13. 13.
    Fernández MR, García AC, Alonso IG, Casanova EZ (2016) Using the Big Data generated by the Smart Home to improve energy efficiency management. Energ Effi 9:249–260CrossRefGoogle Scholar
  14. 14.
    Abawajy J (2015) Comprehensive analysis of big data variety landscape. Int J Parallel Emergent Distrib Syst 30:5–14MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wang D, Yu W, Chai T (2015) Guest editorial: special issue on computational intelligence for industrial data processing and analysis. Neurocomputing 358–360CrossRefGoogle Scholar
  16. 16.
    Cuadra L, Salcedo-Sanz S, Nieto-Borge J, Alexandre E, Rodríguez G (2016) Computational intelligence in wave energy: comprehensive review and case study. Renew Sustain Energy Rev 58:1223–1246CrossRefGoogle Scholar
  17. 17.
    Hu J, Vasilakos AV (2016) Energy big data analytics and security: challenges and opportunities. IEEE Trans Smart Grid 7:2423–2436CrossRefGoogle Scholar
  18. 18.
    Engelbrecht AP (2007) Introduction to computational intelligence. In: Computational intelligence: an introduction, 2nd edn, pp 1–13Google Scholar
  19. 19.
    Păun G (2005) Bio-inspired computing paradigms (natural computing). In: Unconventional programming paradigms, pp 97–97Google Scholar
  20. 20.
    Fister Jr I, Yang X-S, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186
  21. 21.
    Yang X-S, He X (2016) Nature-inspired optimization algorithms in engineering: overview and applications. In: Nature-inspired computation in engineering. Springer, Berlin, pp 1–20CrossRefGoogle Scholar
  22. 22.
    Fister Jr I, Mlakar U, Brest J, Fister I (2016) A new population-based nature-inspired algorithm every month: is the current era coming to the end. In: StuCoSReC: proceedings of the 2016 3rd student computer science research conference. University of Primorska, Koper, pp 33–37Google Scholar
  23. 23.
    Yang X-S (2014) Cuckoo search and firefly algorithm: overview and analysis. In: Cuckoo search and firefly algorithm. Springer, Berlin, pp 1–26Google Scholar
  24. 24.
    Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature & biologically inspired computing, NaBIC 2009, pp 210–214Google Scholar
  25. 25.
    Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation, 2012, pp 240–249CrossRefGoogle Scholar
  26. 26.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06. Erciyes University, Engineering Faculty, Computer Engineering DepartmentGoogle Scholar
  27. 27.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, MHS’95, pp 39–43Google Scholar
  28. 28.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI. Reprinted in 1998Google Scholar
  29. 29.
    Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19:171–209CrossRefGoogle Scholar
  30. 30.
    Chen CP, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347CrossRefGoogle Scholar
  31. 31.
    Gupta A, Gupta S, Ge R, Zong Z (2015) CRUSH: data collection and analysis framework for power capped data intensive computing. In: 2015 sixth international green computing conference and sustainable computing conference (IGSC), pp 1–6Google Scholar
  32. 32.
    Yang H-C, Parker DS (2009) Traverse: simplified indexing on large map-reduce-merge clusters. In: International conference on database systems for advanced applications, 2009, pp 308–322Google Scholar
  33. 33.
    Jlassi A, Martineau P (2016) Benchmarking Hadoop performance in the cloud-an in depth study of resource management and energy consumption. In: The 6th international conference on cloud computing and services scienceGoogle Scholar
  34. 34.
    Rabl T, Gómez-Villamor S, Sadoghi M, Muntés-Mulero V, Jacobsen H-A, Mankovskii S (2012) Solving big data challenges for enterprise application performance management. Proc VLDB Endowment 5:1724–1735CrossRefGoogle Scholar
  35. 35.
    Rong H, Zhang H, Xiao S, Li C, Hu C (2016) Optimizing energy consumption for data centers. Renew Sustain Energy Rev 58:674–691CrossRefGoogle Scholar
  36. 36.
    Kumar R, Mieritz L (2007) Conceptualizing green IT and data center power and cooling issues. Gartner research paper, 2007Google Scholar
  37. 37.
    Johnson P, Marker T (2009) Data centre energy efficiency product profile. Pitt & Sherry, report to equipment energy efficiency committee (E3) of The Australian Government Department of the Environment, Water, Heritage and the Arts (DEWHA)Google Scholar
  38. 38.
    Karpowicz M, Niewiadomska-Szynkiewicz E, Arabas P, Sikora A (2016) Energy and power efficiency in cloud. In: Resource management for big data platforms. Springer, Berlin, pp 97–127Google Scholar
  39. 39.
    Barroso LA, Clidaras J, Hölzle U (2013) The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synth Lect Comput Archit 8:1–154CrossRefGoogle Scholar
  40. 40.
    Lefurgy C, Rajamani K, Rawson F, Felter W, Kistler M, Keller TW (2003) Energy management for commercial servers. Computer 36:39–48CrossRefGoogle Scholar
  41. 41.
    Mastelic T, Oleksiak A, Claussen H, Brandic I, Pierson J-M, Vasilakos AV (2015) Cloud computing: survey on energy efficiency. ACM Comput Surv (CSUR) 47:33CrossRefGoogle Scholar
  42. 42.
    Wang L, Khan SU (2013) Review of performance metrics for green data centers: a taxonomy study. J Supercomput 63:639–656CrossRefGoogle Scholar
  43. 43.
    Dongarra J, Beckman P, Moore T, Aerts P, Aloisio G, Andre J-C et al (2011) The international exascale software project roadmap. Int J High Perform Comput Appl 25:3–60CrossRefGoogle Scholar
  44. 44.
    Khalifa S, Elshater Y, Sundaravarathan K, Bhat A, Martin P, Imam F et al (2016) The six pillars for building big data analytics ecosystems. ACM Comput Surv (CSUR) 49:33CrossRefGoogle Scholar
  45. 45.
    Cheng S, Liu B, Shi Y, Jin Y, Li B (2016) Evolutionary computation and big data: key challenges and future directions. In: International conference on data mining and big data, pp 3–14CrossRefGoogle Scholar
  46. 46.
    Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35:137–144CrossRefGoogle Scholar
  47. 47.
    Hashem IAT, Chang V, Anuar NB, Adewole K, Yaqoob I, Gani A et al (2016) The role of big data in smart city. Int J Inf Manage 36:748–758CrossRefGoogle Scholar
  48. 48.
    Chiroma H, Abdul-Kareem S, Abubakar A (2014) A framework for selecting the optimal technique suitable for application in a data mining task. In: Future information technology. Springer, Berlin, pp 163–169Google Scholar
  49. 49.
    Jiang H, Wang K, Wang Y, Gao M, Zhang Y (2016) Energy big data: a survey. IEEE Access 4:3844–3861CrossRefGoogle Scholar
  50. 50.
    Hu H, Wen Y, Chua T-S, Li X (2014) Toward scalable systems for big data analytics: a technology tutorial. IEEE Access 2:652–687CrossRefGoogle Scholar
  51. 51.
    Kang D, Kim S, Lee T, Hwang J, Lee S, Jang S et al (2016) Energy information analysis using data algorithms based on big data platform. In: High performance computing and communications; IEEE 14th international conference on smart city; IEEE 2nd international conference on data science and systems (HPCC/SmartCity/DSS), 2016 IEEE 18th international conference on, pp 1530–1531Google Scholar
  52. 52.
    Alsheikh MA, Niyato D, Lin S, Tan H-P, Han Z (2016) Mobile big data analytics using deep learning and apache spark. IEEE Network 30:22–29CrossRefGoogle Scholar
  53. 53.
    Lee H, Grosse R, Ranganath R, Ng AY (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th annual international conference on machine learning, pp 609–616Google Scholar
  54. 54.
    Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554MathSciNetCrossRefGoogle Scholar
  55. 55.
    Wang Y, Li B, Luo R, Chen Y, Xu N, Yang H (2014) Energy efficient neural networks for big data analytics. In: Design, automation and test in Europe conference and exhibition (DATE), 2014, pp 1–2Google Scholar
  56. 56.
    Hu M, Li H, Wu Q, Rose GS (2012) Hardware realization of BSB recall function using memristor crossbar arrays. In: Proceedings of the 49th annual design automation conference, pp 498–503Google Scholar
  57. 57.
    Yoo H, Park S, Bong K, Shin D, Lee J, Choi S (2015) A 1.93 TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big data applications. In: IEEE international solid-state circuits conference, pp 80–81Google Scholar
  58. 58.
    Mehdipour F, Noori H, Javadi B (2016) Chapter two-energy-efficient big data analytics in datacenters. Adv Comput 100:59–101CrossRefGoogle Scholar
  59. 59.
    Park S-W, Park J, Bong K, Shin D, Lee J, Choi S et al (2015) An energy-efficient and scalable deep learning/inference processor with tetra-parallel MIMD architecture for big data applications. IEEE Trans Biomed Circuits Syst 9:838–848Google Scholar
  60. 60.
    Liang B, Jin S, Tang W, Sheng W, Liu K (2016) A parallel algorithm of optimal power flow on Hadoop platform. In: Power and energy engineering conference (APPEEC), 2016 IEEE PES Asia-Pacific, pp 566–570Google Scholar
  61. 61.
    Polato I, Barbosa D, Hindle, Kon F (2016) Hadoop energy consumption reduction with hybrid HDFS. In: Proceedings of the 31st annual ACM symposium on applied computing, pp 406–411Google Scholar
  62. 62.
    Nan Z, Hanyong H, Haiyan Z (2016) Efficient stereo index technology for fast combination query of electric power big data. In: 2016 IEEE international conference on computer communication and the internet (ICCCI), pp 329–333Google Scholar
  63. 63.
    Baccarelli E, Cordeschi N, Mei A, Panella M, Shojafar M, Stefa J (2016) Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study. IEEE Network 30:54–61CrossRefGoogle Scholar
  64. 64.
    Zhu N, Rao L, Liu X, Liu J, Guan H (2011) Taming power peaks in mapreduce clusters. In: ACM SIGCOMM computer communication review, pp 416–417CrossRefGoogle Scholar
  65. 65.
    Lee S, Jo J-Y, Kim Y (2016) Performance improvement of mapreduce process by promoting deep data locality. In: 2016 IEEE international conference on data science and advanced analytics (DSAA), pp 292–301Google Scholar
  66. 66.
    Iqbal R, Doctor F, More B, Mahmud S, Yousuf U (2016) Big data analytics: computational intelligence techniques and application areas. Int J Inf ManageGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haruna Chiroma
    • 1
    Email author
  • Usman Ali Abdullahi
    • 1
    • 2
  • Ibrahim Abaker Targio Hashem
    • 3
  • Younes Saadi
    • 4
  • Rawaa Dawoud Al-Dabbagh
    • 5
  • Muhammad Murtala Ahmad
    • 6
  • Gbenga Emmanuel Dada
    • 7
  • Sani Danjuma
    • 8
  • Jaafar Zubairu Maitama
    • 9
  • Adamu Abubakar
    • 10
  • Shafi’i Muhammad Abdulhamid
    • 11
  1. 1.Department of Computer ScienceFederal College of Education (Technical)GombeNigeria
  2. 2.Department of Computer and Information SciencesUniversiti Teknologi PETRONASPerakMalaysia
  3. 3.Centre for Data Science and Analytics, School of Computing & Information Technology, Taylor’s University, Subang, Jaya, MalaysiaKuala LumpurMalaysia
  4. 4.Department of Computer ScienceUniversity of BatnaFésdisAlgérie
  5. 5.Department of Computer ScienceUniversity of BaghdadBaghdadIraq
  6. 6.Department of Information TechnologyNational Open University of NigeriaLagosNigeria
  7. 7.Department of Computer EngineeringUniversity of MaiduguriMaiduguriNigeria
  8. 8.Department of Mathematical ScienceNorth-West University KanoKanoNigeria
  9. 9.Department of Information TechnologyBayero University KanoKanoNigeria
  10. 10.Department of Information SystemsInternational Islamic University MalaysiaGombakMalaysia
  11. 11.Department of Cyber Security ScienceFederal University of Technology MinnaMinnaNigeria

Personalised recommendations