Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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 2012
G. Aceto, A. Botta, W. de Donato, A. Pescape, Cloud monitoring: a survey. Comput. Netw. 57(9), 2093–2115 (2013)
F.-F. Han et al., Virtual resource monitoring in cloud computing. J. Shanghai Univ. (Engl. Ed.) 15(5), 381–385 (2011)
J. Montes et al., GMonE: a complete approach to cloud monitoring. Future Gener. Comp. Syst. 29(8), 2026–2040 (2013)
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)
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 2012
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. 91–108
E. Gelenbe, Steps toward self-aware networks. Commun. ACM 52(7), 66–75 (2009)
B. Konig, C.J.M. Alcaraz, J. Kirschnick, Elastic monitoring framework for cloud infrastructures. IET Commun. 6(10), 1306–1315 (2012)
J. Spring, Monitoring cloud computing by layer, part 1. IEEE Secur. Priv. 9(2), 66–68 (2011)
J. Spring, Monitoring cloud computing by layer, part 2. IEEE Secur. Priv. 9(3), 52–55 (2011)
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 2013
J.S. Ward and A. Baker, Monitoring large-scale cloud systems with layered gossip protocols, arXiv Computing Research Repository, vol. abs/1305.7403, May 2013
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 2011
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)
G. Katsaros et al., A self-adaptive hierarchical monitoring mechanism for clouds. J. Syst. Softw. 85(5), 1029–1041 (2010)
R. Lent, O.H. Abdelrahman, G. Gorbil, A Low-Latency and Self-Adapting Application Layer Multicast, Computer and Information Sciences (Springer, Netherlands, 2010), pp. 169–172
E. Gelenbe, R. Lent, A. Nunez, Self-aware networks and QoS. Proc. IEEE 92(9), 1478–1489 (2004)
E. Gelenbe, Z. Xu, E. Seref, Cognitive packet networks, in: Proceedings of 11th International Conference on Tools with Artificial Intelligence, pp. 47–54, November 1999
G. Sakellari, The cognitive packet network: a survey. Comp. J. 53(3), 268–279 (2009)
E. Gelenbe, Sensible decisions based on QoS. Comput. Manag. Sci. 1(1), 1–14 (2003)
E. Gelenbe, S. Timotheou, Random neural networks with synchronised interactions. Neural Comput. 20(9), 2308–2324 (2008)
E. Gelenbe, K. Hussain, Learning in the multiple class random neural network. IEEE Trans. Neural Netw. 13(6), 1257–1267 (2002)
U. Halici, Reinforcement learning with internal expectation for the random neural network. Eur. J. Oper. Res. 126(2), 288–307 (2000)
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. 2012
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 2013
Acknowledgments
The work presented in this paper was partially supported by the EU FP7 research project PANACEA (Proactive Autonomous Management of Cloud Resources) under grant agreement no. 610764.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Gorbil, G., Garcia Perez, D., Huedo Cuesta, E. (2014). Principles of Pervasive Cloud Monitoring. In: Czachórski, T., Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-09465-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-09465-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09464-9
Online ISBN: 978-3-319-09465-6
eBook Packages: Computer ScienceComputer Science (R0)