Principles of Pervasive Cloud Monitoring

  • Gokce Gorbil
  • David Garcia Perez
  • Eduardo Huedo Cuesta
Conference paper


Accurate and fine-grained monitoring of dynamic and heterogeneous cloud resources is essential to the overall operation of the cloud. In this paper, we review the principles of pervasive cloud monitoring, and discuss the requirements of a pervasive monitoring solution needed to support proactive and autonomous management of cloud resources. This paper reviews existing monitoring solutions used by the industry and assesses their suitability to support pervasive monitoring. We find that the collectd daemon is a good candidate to form the basis of a lightweight monitoring agent that supports high resolution probing, but it needs to be supplemented by high-level interaction capabilities for pervasive monitoring.


Cloud monitoring Pervasive monitoring Cloud monitoring tools 


  1. 1.
    M. Armbrust, A. Fox, R. Griffth, A.D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, M. Zaharia, A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)CrossRefGoogle Scholar
  2. 2.
    T. Lorimer and R. Sterritt, Autonomic management of cloud neighborhoods through pulse monitoring, in: Proceedings of 5th IEEE International Conference on Utility and Cloud Computing (UCC’12), pp. 295–302, November 2012Google Scholar
  3. 3.
    G. Aceto, A. Botta, W. de Donato, A. Pescape, Cloud monitoring: a survey. Comput. Netw. 57(9), 2093–2115 (2013)CrossRefGoogle Scholar
  4. 4.
    F.-F. Han et al., Virtual resource monitoring in cloud computing. J. Shanghai Univ. (Engl. Ed.) 15(5), 381–385 (2011)CrossRefGoogle Scholar
  5. 5.
    J. Montes et al., GMonE: a complete approach to cloud monitoring. Future Gener. Comp. Syst. 29(8), 2026–2040 (2013)CrossRefGoogle Scholar
  6. 6.
    J. Povedano-Molina et al., DARGOS: A highly adaptable and scalable monitoring architecture for multi-tenant clouds. Future Gener. Comp. Syst. 29(8), 2041–2056 (2013)CrossRefGoogle Scholar
  7. 7.
    K. Alhamazani et al, Cloud monitoring for optimizing the QoS of hosted applications, in: Proceedings of 4th IEEE International Conference on Cloud Computing Technology and Science (CloudCom’12), pp. 765–770, December 2012Google Scholar
  8. 8.
    L. Atzori, F. Granelli, A. Pescape, A network-oriented survey and open issues in cloud computing, Cloud Computing: Methodology, Systems, and Applications (CRC Press, Florida, 2011), pp. 91–108CrossRefGoogle Scholar
  9. 9.
    E. Gelenbe, Steps toward self-aware networks. Commun. ACM 52(7), 66–75 (2009)CrossRefGoogle Scholar
  10. 10.
    B. Konig, C.J.M. Alcaraz, J. Kirschnick, Elastic monitoring framework for cloud infrastructures. IET Commun. 6(10), 1306–1315 (2012)CrossRefGoogle Scholar
  11. 11.
    J. Spring, Monitoring cloud computing by layer, part 1. IEEE Secur. Priv. 9(2), 66–68 (2011)CrossRefGoogle Scholar
  12. 12.
    J. Spring, Monitoring cloud computing by layer, part 2. IEEE Secur. Priv. 9(3), 52–55 (2011)CrossRefGoogle Scholar
  13. 13.
    Y. Meng, Z. Luan, Z. Cheng, and D. Qian, Differentiating data collection for cloud environment monitoring, in: Proceedings of 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM’13), pp. 868–871, May 2013Google Scholar
  14. 14.
    J.S. Ward and A. Baker, Monitoring large-scale cloud systems with layered gossip protocols, arXiv Computing Research Repository, vol. abs/1305.7403, May 2013Google Scholar
  15. 15.
    H.T. Kung, C.-K. Lin, and D. Vlah, CloudSense: Continuous fine-grain cloud monitoring with compressive sensing, in Proceedings of 3rd USENIX W’orkshop on Hot Topics in Cloud Computing (HotCloud’11), June 2011Google Scholar
  16. 16.
    C. Canali, R. Lancellotti, Improving scalability of cloud monitoring through PCA-based clustering of virtual machines. J. Comput. Sci. Technol. 29(1), 38–52 (2014)CrossRefGoogle Scholar
  17. 17.
    G. Katsaros et al., A self-adaptive hierarchical monitoring mechanism for clouds. J. Syst. Softw. 85(5), 1029–1041 (2010)CrossRefGoogle Scholar
  18. 18.
    R. Lent, O.H. Abdelrahman, G. Gorbil, A Low-Latency and Self-Adapting Application Layer Multicast, Computer and Information Sciences (Springer, Netherlands, 2010), pp. 169–172Google Scholar
  19. 19.
    E. Gelenbe, R. Lent, A. Nunez, Self-aware networks and QoS. Proc. IEEE 92(9), 1478–1489 (2004)CrossRefGoogle Scholar
  20. 20.
    E. Gelenbe, Z. Xu, E. Seref, Cognitive packet networks, in: Proceedings of 11th International Conference on Tools with Artificial Intelligence, pp. 47–54, November 1999Google Scholar
  21. 21.
    G. Sakellari, The cognitive packet network: a survey. Comp. J. 53(3), 268–279 (2009)CrossRefGoogle Scholar
  22. 22.
    E. Gelenbe, Sensible decisions based on QoS. Comput. Manag. Sci. 1(1), 1–14 (2003)CrossRefMathSciNetGoogle Scholar
  23. 23.
    E. Gelenbe, S. Timotheou, Random neural networks with synchronised interactions. Neural Comput. 20(9), 2308–2324 (2008)CrossRefMATHMathSciNetGoogle Scholar
  24. 24.
    E. Gelenbe, K. Hussain, Learning in the multiple class random neural network. IEEE Trans. Neural Netw. 13(6), 1257–1267 (2002)CrossRefGoogle Scholar
  25. 25.
    U. Halici, Reinforcement learning with internal expectation for the random neural network. Eur. J. Oper. Res. 126(2), 288–307 (2000)CrossRefMATHMathSciNetGoogle Scholar
  26. 26.
    R. Aversa, L. Tasquier, and S. Venticinque, Management of cloud infrastructures through agents, in: Proceedings of 3rd International Conference on Emerging Intelligent Data and Web Technologies (EIDWT’12), pp. 46–52, Sep. 2012Google Scholar
  27. 27.
    K. Alhamazani et al. An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art, arXiv Computing Research Repository, vol. abs/1312.6170, December 2013Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gokce Gorbil
    • 1
  • David Garcia Perez
    • 2
  • Eduardo Huedo Cuesta
    • 3
  1. 1.Department of Electrical and Electronic Engineering, Intelligent Systems and Networks GroupImperial College LondonLondonUK
  2. 2.Atos OriginBarcelonaSpain
  3. 3.Department of Computer Architecture and Automation, Distributed Systems Architecture GroupComplutense University of MadridMadridSpain

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