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Computational Intelligence in Cloud Computing

  • Ruggero Donida Labati
  • Angelo GenoveseEmail author
  • Vincenzo Piuri
  • Fabio Scotti
  • Sarvesh Vishwakarma
Chapter
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 14)

Abstract

Cloud Computing (CC) is a model that enables ubiquitous, convenient, and on-demand network access to a shared pool of configurable computing resources. In CC applications, it is possible to access both software and hardware architectures remotely and with little or no knowledge about their physical or logical locations. Due to its low deployment and management costs, the CC paradigm is being increasingly used in a wide variety of online services and applications, including remote computation, software-as-a-service, off-site storage, entertainment, and communication platforms. However, several aspects of CC applications, such as system design, optimization, and security issues, have become too complex to be efficiently treated using traditional algorithmic approaches under the increasingly high complexity and performance demands of current applications. Recently, advances in Computational Intelligence (CI) techniques have fostered the development of intelligent solutions for CC applications. CI methods such as artificial neural networks, deep learning, fuzzy logic, and evolutionary algorithms have enabled improving CC paradigms through their capabilities of extracting knowledge from high quantities of real-world data, thus further optimizing their design, performance, and security with respect to traditional techniques. This chapter introduces recent CI techniques, reviews the main applications of CI in CC, and presents challenges and research trends.

References

  1. 1.
    M. Abdelsalam, R. Krishnan, Y. Huang, R. Sandhu, Malware detection in cloud infrastructures using convolutional neural networks, in Proceedings of the 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (2016), pp. 162–169Google Scholar
  2. 2.
    A. Abeshu, N. Chilamkurti, Deep learning: the frontier for distributed attack detection in fog-to-things computing. IEEE Commun. Mag. 56(2), 169–175 (2018)Google Scholar
  3. 3.
    C. Alippi, A. Ferrero, V. Piuri, Artificial intelligence for instruments and measurement applications. IEEE Instrum. Meas. Mag. 1(2), 9–17 (1998)Google Scholar
  4. 4.
    Amazon: amazon web services (AWS) (2018). https://aws.amazon.com/whitepapers
  5. 5.
    Amazon: AWS cost optimization (2018). https://aws.amazon.com/pricing/cost-optimization/
  6. 6.
  7. 7.
    Analytics India magazine: 10 machine learning as a service (MLaaS) tools for data scientists (2018). https://www.analyticsindiamag.com/10-machine-learning-service-mlaas-tools-data-scientists/
  8. 8.
    N. Ansari, E. Hou, Computational Intelligence for Optimization (Springer Publishing Company, Incorporated, Berlin, 2012)zbMATHGoogle Scholar
  9. 9.
    N. Antonopoulos, L. Gillam, Cloud Computing: Principles, Systems and Applications, 1st edn. (Springer Publishing Company, Incorporated, Berlin, 2010)zbMATHGoogle Scholar
  10. 10.
    A.A. Bankole, S.A. Ajila, Predicting cloud resource provisioning using machine learning techniques, in Proceedings of the 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (2013), pp. 1–4Google Scholar
  11. 11.
    Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)Google Scholar
  12. 12.
    D. Bhamare, T. Salman, M. Samaka, A. Erbad, R. Jain, Feasibility of supervised machine learning for cloud security, in Proceedings of the 2016 International Conference on Information Science and Security (ICISS) (2016), pp. 1–5Google Scholar
  13. 13.
    R. Buyya, S.N. Srirama, G. Casale, R. Calheiros, Y. Simmhan, B. Varghese, E. Gelenbe, B. Javadi, L.M. Vaquero, M.A.S. Netto, A.N. Toosi, M.A. Rodriguez, I.M. Llorente, S. De Capitani, P. di Vimercati, D. Samarati, C. Milojicic, R. Varela, M.D.D. Bahsoon, O. Assuncao, W. Rana, H. Zhou, W. Jin, A.Y. Gentzsch, H. Shen Zomaya, A manifesto for future generation cloud computing: research directions for the next decade. ACM Comput. Surv. 51(5), 105:1–105:38 (2018)Google Scholar
  14. 14.
    C. Campbell, An introduction to kernel methods, in Radial Basis Function Networks: Design and Applications, ed. by R.J. Howlett, L.C. Jain (Springer, Berlin, 2000)Google Scholar
  15. 15.
    S. Chaisiri, B. Lee, D. Niyato, Optimization of resource provisioning cost in cloud computing. IEEE Trans. Serv. Comput. 5(2), 164–177 (2012)Google Scholar
  16. 16.
    X. Chen, X. Lin, Big data deep learning: challenges and perspectives. IEEE Access 2, 514–525 (2014)Google Scholar
  17. 17.
    Z. Chen, Z. Zhan, Y. Lin, Y. Gong, T. Gu, F. Zhao, H. Yuan, X. Chen, Q. Li, J. Zhang, Multiobjective cloud workflow scheduling: a multiple populations ant colony system approach. IEEE Trans. Cybern. 1–15 (2018)Google Scholar
  18. 18.
    H. Cheng, X. Yao, S. Yang, M. Zhang, Guest editorial: special issue on computational intelligence for cloud computing. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 1–2 (2018)Google Scholar
  19. 19.
    M. Chiang, T. Zhang, Fog and Iot: an overview of research opportunities. IEEE Internet Things J. 3(6), 854–864 (2016)Google Scholar
  20. 20.
    Dazeinfo: 400 million new servers might be needed by 2020 (2014). https://dazeinfo.com/2014/04/22/internet-comprises-5-million-terabytes-data-weighs-much-grain-sand/
  21. 21.
    DeepMind: DeepMind AI reduces google data centre cooling bill by 40% (2016). https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/
  22. 22.
    Densify: cost optimization through machine learning (2018). https://www.densify.com/service/cloud-cost-optimization
  23. 23.
    L.D. Dhinesh Babu, P.V. Krishna, Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)Google Scholar
  24. 24.
    H.T. Dinh, C. Lee, D. Niyato, P. Wang, A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 13(18), 1587–1611 (2013)Google Scholar
  25. 25.
    R. Donida Labati, A. Genovese, E. Muñoz, V. Piuri, F. Scotti, Applications of computational intelligence in industrial and environmental scenarios, in Learning Systems: From Theory to Practice, ed. by V. Sgurev, V. Piuri, V. Jotsov (Springer International Publishing, Cham, 2018), pp. 29–46Google Scholar
  26. 26.
    R. Donida Labati, A. Genovese, E. Muñoz, V. Piuri, F. Scotti, G. Sforza, Computational intelligence for biometric applications: a survey. Int. J. Comput. 15(1), 40–49 (2016)Google Scholar
  27. 27.
    R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, G. Sforza, A decision support system for wind power production. IEEE Trans. Syst. Man Cybern. Syst. 1–15 (2018)Google Scholar
  28. 28.
    A. Engelbrecht, Computational Intelligence: An Introduction (Wiley, Chichester, 2007)Google Scholar
  29. 29.
    eWEEK: Google turns to AI, machine learning to improve cloud security (2018). http://www.eweek.com/cloud/google-turns-to-ai-machine-learning-to-improve-cloud-security
  30. 30.
    Z.M. Fadlullah, F. Tang, B. Mao, N. Kato, O. Akashi, T. Inoue, K. Mizutani, State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surv. Tutor. 19(4), 2432–2455 (2017)Google Scholar
  31. 31.
    J. Feng, L. Kong, A fuzzy multi-objective genetic algorithm for QoS-based cloud service composition, in Proceedings of the 11th International Conference on Semantics, Knowledge and Grids (2015), pp. 202–206Google Scholar
  32. 32.
    S. Ferrari, I. Frosio, V. Piuri, N.A. Borghese, Automatic multiscale meshing through HRBF networks. IEEE Trans. Instrum. Meas. 54(4), 1463–1470 (2005)Google Scholar
  33. 33.
    Fortune: amazon announces a security change that may help companies using AWS to avoid data breaches (2018). http://fortune.com/2018/11/16/amazon-secures-cloud-password-breach-safer
  34. 34.
    Fugue: regions beyond regions: global cloud infrastructure expansions (2016). https://www.fugue.co/blog/2016-04-12-regions-beyond-regions-global-cloud-infrastructure-expansions.html
  35. 35.
    T. Gonsalves, K. Itoh, GA optimization of Petri net-modeled concurrent service systems. Appl. Soft Comput. 11(5), 3929–3937 (2011)Google Scholar
  36. 36.
    Google: google cloud platform (GCP) (2018). https://cloud.google.com/whitepapers
  37. 37.
    P. Guo, Z. Xue, An adaptive PSO-based real-time workflow scheduling algorithm in cloud systems, in Proceedings of the 17th IEEE International Conference on Communication Technology (ICCT) (2017), pp. 1932–1936Google Scholar
  38. 38.
    M. Guzek, P. Bouvry, E. Talbi, A survey of evolutionary computation for resource management of processing in cloud computing. IEEE Comput. Intell. Mag. 10(2), 53–67 (2015)Google Scholar
  39. 39.
    M. Guzek, J.E. Pecero, B. Dorronsoro, P. Bouvry, Multi-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems. Appl. Soft Comput. 24(C), 432–446 (2014)Google Scholar
  40. 40.
    S. Haykin, Neural networks and learning machines, vol. 10 (Prentice Hall, Englewood Cliffs, 2009)Google Scholar
  41. 41.
    W. Huang, J.W. Stokes, MtNet: a multi-task neural network for dynamic malware classification, in: Detection of Intrusions and Malware, and Vulnerability Assessment ed. by J. Caballero, U. Zurutuza, R.J. Rodríguez (Springer International Publishing, Berlin, 2016), pp. 399–418Google Scholar
  42. 42.
    Y. Huang, X. Ma, X. Fan, J. Liu, W. Gong, When deep learning meets edge computing, in Proceedings of the 2017 IEEE 25th International Conference on Network Protocols (ICNP) (2017), pp. 1–2Google Scholar
  43. 43.
  44. 44.
  45. 45.
    IBM: security in the IBM cloud (2018). https://www.ibm.com/cloud/security
  46. 46.
  47. 47.
  48. 48.
    S. Iturriaga, S. Nesmachnow, B. Dorronsoro, E. Talbi, P. Bouvry, A parallel hybrid evolutionary algorithm for the optimization of broker virtual machines subletting in cloud systems, in Proceedings of the 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (2013), pp. 594–599Google Scholar
  49. 49.
    A.K. Jain, R.P.W. Duin, J. Mao, Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)Google Scholar
  50. 50.
    S. Jajodia, K. Kant, P. Samarati, A. Singhal, V. Swarup, C. Wang (eds.), Secure Cloud Computing (Springer, Berlin, 2014)Google Scholar
  51. 51.
    B. Joshi, A.S. Vijayan, B.K. Joshi, Securing cloud computing environment against DDoS attacks, in Proceedings of the 2012 International Conference on Computer Communication and Informatics (2012), pp. 1–5Google Scholar
  52. 52.
    Y. Kessaci, N. Melab, E. Talbi, A pareto-based genetic algorithm for optimized assignment of VM requests on a cloud brokering environment, in Proceedings of the IEEE Congress on Evolutionary Computation (2013), pp. 2496–2503Google Scholar
  53. 53.
    H.Y. Kim, J.M. Kim, A load balancing scheme based on deep-learning in IoT. Clust. Comput. 20(1), 873–878 (2017)MathSciNetGoogle Scholar
  54. 54.
    J.Z. Kolter, M.A. Maloof, Learning to detect and classify malicious executables in the wild. J. Mach. Learn. Res. 7, 2721–2744 (2006)MathSciNetzbMATHGoogle Scholar
  55. 55.
    A. Konar, D. Bhattacharya, Time-Series Prediction and Applications: A Machine Intelligence Approach, Intelligent Systems Reference Library (Springer International Publishing, Berlin, 2017)Google Scholar
  56. 56.
    G. Kousiouris, A. Menychtas, D. Kyriazis, K. Konstanteli, S.V. Gogouvitis, G. Katsaros, T.A. Varvarigou, Parametric design and performance analysis of a decoupled service-oriented prediction framework based on embedded numerical software. IEEE Trans. Serv. Comput. 6(4), 511–524 (2013)Google Scholar
  57. 57.
    J. Kumar, A.K. Singh, Workload prediction in cloud using artificial neural network and adaptive differential evolution. Futur. Gener. Comput. Syst. 81, 41–52 (2018)Google Scholar
  58. 58.
    T. Kumrai, K. Ota, M. Dong, J. Kishigami, D.K. Sung, Multiobjective optimization in cloud brokering systems for connected internet of things. IEEE Internet Things J. 4(2), 404–413 (2017)Google Scholar
  59. 59.
    Y. Lecun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)Google Scholar
  60. 60.
    L. Li, K. Ota, M. Dong, Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Trans. Ind. Inform. 14(10), 4665–4673 (2018)Google Scholar
  61. 61.
    M. Lin, A. Wierman, L.L.H. Andrew, E. Thereska, Dynamic right-sizing for power-proportional data centers. IEEE/ACM Trans. Netw. 21(5), 1378–1391 (2013)Google Scholar
  62. 62.
    D.S. Linthicum, Making sense of AI in public clouds. IEEE Cloud Comput. 4(6), 70–72 (2017)Google Scholar
  63. 63.
    L. Liu, S. Gu, M. Zhang, D. Fu, A hybrid evolutionary algorithm for inter-cloud service composition, in Proceedings of the 9th International Conference on Modelling, Identification and Control (2017), pp. 482–487Google Scholar
  64. 64.
    N. Liu, Z. Li, J. Xu, Z. Xu, S. Lin, Q. Qiu, J. Tang, Y. Wang, A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning, in Proceedings of the 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (2017), pp. 372–382Google Scholar
  65. 65.
    X. Ma, F. Zhang, X. Chen, J. Shen, Privacy preserving multi-party computation delegation for deep learning in cloud computing. Inf. Sci. 459, 103–116 (2018)Google Scholar
  66. 66.
    M. Masdari, S. ValiKardan, Z. Shahi, S.I. Azar, Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64–82 (2016)Google Scholar
  67. 67.
  68. 68.
    Microsoft: detecting fileless attacks with Azure security center (2018). https://azure.microsoft.com/en-us/blog/detecting-fileless-attacks-with-azure-security-center/
  69. 69.
  70. 70.
    B. Mitra, N. Craswell, Neural models for information retrieval. CoRR (2017)Google Scholar
  71. 71.
    A. Mozo, B. Ordozgoiti, S. Gomez-Canaval, Forecasting short-term data center network traffic load with convolutional neural networks. PLoS One (2018)Google Scholar
  72. 72.
    V. Nae, A. Iosup, R. Prodan, Dynamic resource provisioning in massively multiplayer online games. IEEE Trans. Parallel Distrib. Syst. 22(3), 380–395 (2011)Google Scholar
  73. 73.
  74. 74.
    Oracle: oracle cloud (2018). https://docs.cloud.oracle.com
  75. 75.
    S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M.P. Reyes, M.L. Shyu, S.C. Chen, S.S. Iyengar, a survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 51(5), 92:1–92:36 (2018)Google Scholar
  76. 76.
    J. Qiu, Q. Wu, G. Ding, Y. Xu, S. Feng, A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. 2016(1) (2016)Google Scholar
  77. 77.
    Rack solutions: the internet is on 75 million servers with 5 million terabytes of data (2014). https://dazeinfo.com/2014/04/22/internet-comprises-5-million-terabytes-data-weighs-much-grain-sand/
  78. 78.
    S. Rahman, T. Ahmed, M. Huynh, M. Tornatore, B. Mukherjee, Auto-scaling vnfs using machine learning to improve qos and reduce cost, in Proceedings of the 2018 IEEE International Conference on Communications (ICC) (2018), pp. 1–6Google Scholar
  79. 79.
    Y. Rahulamathavan, R.C. Phan, S. Veluru, K. Cumanan, M. Rajarajan, Privacy-preserving multi-class support vector machine for outsourcing the data classification in cloud. IEEE Trans. Dependable Secur. Comput. 11(5), 467–479 (2014)Google Scholar
  80. 80.
    O. Rana, The costs of cloud migration. IEEE Cloud Comput. 1(1), 62–65 (2014)Google Scholar
  81. 81.
    M. Rasheduzzaman, M.A. Islam, R.M. Rahman, Workload prediction on google cluster trace. Int. J. Grid High Perform. Comput. 6(3), 34–52 (2014)Google Scholar
  82. 82.
    A. Saiyeda, M.A. Mir, Cloud computing for deep learning analytics: a survey of current trends and challenges. Int. J. Adv. Res. Comput. Sci. 8(2) (2017)Google Scholar
  83. 83.
    P. Samarati, S. Jajodia, Data security, in Wiley Encyclopedia of Electrical and Electronics Engineering, ed. by J. Webster (Wiley, New York, 1999)Google Scholar
  84. 84.
    F. Samreen, Y. Elkhatib, M. Rowe, G.S. Blair, Daleel: simplifying cloud instance selection using machine learning, in Proceedings of the 2016 IEEE/IFIP Network Operations and Management Symposium (NOMS) (2016), pp. 557–563Google Scholar
  85. 85.
    P.D. Sanzo, D. Rughetti, B. Ciciani, F. Quaglia, Auto-tuning of cloud-based in-memory transactional data grids via machine learning, in Proceedings of the 2012 2nd Symposium on Network Cloud Computing and Applications (NCCA) (2012), pp. 9–16Google Scholar
  86. 86.
    T. Shabeera, S.M. Kumar, S.M. Salam, K.M. Krishnan, Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm. Eng. Sci. Technol. Int. J. 20(2), 616–628 (2017)Google Scholar
  87. 87.
    S. Sharifian, S.A. Motamedi, M.K. Akbari, A predictive and probabilistic load-balancing algorithm for cluster-based web servers. Appl. Soft Comput. 11(1), 970–981 (2011)Google Scholar
  88. 88.
    D. Simon, Evolutionary Optimization Algorithms (Wiley, New York, 2013)Google Scholar
  89. 89.
    K. Sundararajan, D.L. Woodard, Deep learning for biometrics: a survey. ACM Comput. Surv. 51(3), 65:1–65:34 (2018)Google Scholar
  90. 90.
  91. 91.
    Synergy research group: cloud revenues continue to grow by 50% as top four providers tighten grip on market (2018). https://globenewswire.com/news-release/2018/07/27/1543412/0/en/Cloud-Revenues-Continue-to-Grow-by-50-as-Top-Four-Providers-Tighten-Grip-on-Market.html
  92. 92.
    S. Tobiyama, Y. Yamaguchi, H. Shimada, T. Ikuse, T. Yagi, Malware detection with deep neural network using process behavior, in Proceedings of the 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), vol. 2 (2016), pp. 577–582Google Scholar
  93. 93.
    E. Trillas, L. Eciolaza, Fuzzy logic: an introductory course for engineering students (Springer Publishing Company, Incorporated, Berlin, 2015)Google Scholar
  94. 94.
    B. Varghese, R. Buyya, Next generation cloud computing: New trends and research directions. Futur. Gener. Comput. Syst. 79, 849–861 (2018)Google Scholar
  95. 95.
    J. Xu, J.A.B. Fortes, Multi-objective virtual machine placement in virtualized data center environments, in Proceedings of the 2010 IEEE/ACM International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (2010), pp. 179–188Google Scholar
  96. 96.
    M. Xu, W. Tian, R. Buyya, A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurr. Comput. Pract. Exp. 29(12) (2017)Google Scholar
  97. 97.
  98. 98.
    Y. Zhang, J. Yao, H. Guan, Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Comput. 4(6), 60–69 (2017)Google Scholar
  99. 99.
    L. Zhao, S. Sakr, A. Liu, A. Bouguettaya, Cloud computing, in Cloud Data Management (Springer International Publishing, Cham, 2014), pp. 9–20Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ruggero Donida Labati
    • 1
  • Angelo Genovese
    • 1
    Email author
  • Vincenzo Piuri
    • 1
  • Fabio Scotti
    • 1
  • Sarvesh Vishwakarma
    • 1
  1. 1.Department of Computer ScienceUniversità degli Studi di MilanoMilan (MI)Italy

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