Abstract
Cloud Computing has been increasingly incorporated by companies as a cost-effective way to make resources and services continuously available. However, as a consequence of service downtimes at cloud providers, achieving operational reliability and resource availability are still a concern, since they can lead to loss of revenue and customer mistrust. This work presents Apache CloudStack AMFC (Auditing and Monitoring For Cloud Computing), a cloud auditing and monitoring tool aimed to perform the removal of unused data and inconsistencies, improve failure detection (reducing false positive and false negative alerts), and reduce the cost for storing persistent cloud data. All these characteristics are achieved through the synchronization of current state information with persistent orchestration data. The effectiveness of the tool is evidenced through testing on experimental scenarios generated in a controlled test environment. The experiments involved 1,320 administrative routines for virtual machine instances. It was possible to identify and eliminate inconsistencies in the persistent database, allowing a reduction in the storage cost and, consequently, an improvement on database integrity. Overall, the AMFC provided the cloud administrator with more accurate data, enhancing decision-making, allowing a better identification of problems occurring in the cloud environment.
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Notes
- 1.
Monitoring tools such as Zabbix (www.zabibix.com), Nagios (www.nagios.org) and Ganglia (ganglia.sourceforge.net/) are used in cloud environments.
References
Alnazir, M.K.A.M., Mustafa, A.B.A.N., Ali, H.A., Yousif, A.A.O.: Performance analysis of Cloud Computing for distributed data center using cloud-sim. In: 2017 International Conference on Communication, Control, Computing and Electronics Engineering, pp. 1–6 (2017)
Mdhaffar, A., Halima, R.B., Jmaiel, M., Freisleben, B.: A dynamic complex event processing architecture for cloud monitoring and analysis. In: 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, vol. 2, pp. 270–275 (2013)
Suciu, G., Halunga, S., Ochian, A., Suciu, V.: Network management and monitoring for cloud systems. In: Proceedings of the 2014 6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–4 (2014). https://doi.org/10.1109/ECAI.2014.7090169
Xu, X., Zhu, L., Weber, I., Bass, L., Sun, D.: POD-diagnosis: error diagnosis of sporadic operations on cloud applications. In: 2014 Annual IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 252–263 (2014). https://doi.org/10.1109/DSN.2014.94
Xu, X., et al.: Crying wolf and meaning it: reducing false alarms in monitoring of sporadic operations through POD-monitor. In: 2015 IEEE/ACM 1st International Workshop on Complex Faults and Failures in Large Software Systems (COUFLESS), pp. 69–75 (2015)
Saleh, O., Gropengießer, F., Betz, H., Mandarawi, W., Sattler, K.U.: Monitoring and autoscaling IaaS clouds: a case for complex event processing on data streams. In: IEEE/ACM 6th International Conference on Utility and Cloud Computing, pp. 387–392 (2013)
de Carvalho, M.B., Esteves, R.P., da Cunha Rodrigues, G., Granville, L.Z., Tarouco, L.M.R.: A cloud monitoring framework for self-configured monitoring slices based on multiple tools. In: 9th International Conference on Network and Service Management, pp. 180–184 (2013)
Calero, J.M.A., Aguado, J.G.: MonPaaS: an adaptive monitoring platformas a service for cloud computing infrastructures and services. IEEE Trans. Serv. Comput. 8, 65–78 (2015). https://doi.org/10.1109/TSC.2014.2302810
Montes, J., Sánchez, A., Memishi, B., Pérez, M.S., Antoniu, G.: GMonE: a complete approach to cloud monitoring. Future Gener. Comput. Syst. 29, 2026–2040 (2013)
Dongmyoung, B., Bumchul, L.: Analysis of telemetering service in OpenStack. In: 2015 International Conference on Information and Communication Technology Convergence (ICTC), pp. 272–274 (2015). https://doi.org/10.1109/ICTC.2015.7354546
Li, Y., Wu, Z., Wei, J., Plaza, A., Li, J., Wei, Z.: Fast principal component analysis for hyperspectral imaging based on cloud computing. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 513–516 (2015)
Persico, V., Montieri, A., Pescapé, A.: CloudSurf: a platform for monitoring public-cloud networks. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp. 1–6 (2016)
Zhang, T., Lee, R.B.: Monitoring and attestation of virtual machine security health in cloud computing. IEEE Micro 36, 28–37 (2016). https://doi.org/10.1109/MM.2016.86
Wang, T., Zhang, W., Wei, J., Zhong, H.: Fault detection for cloud computing systems with correlation analysis. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 652–658 (2015)
Turk, A., et al.: DeltaSherlock: identifying changes in the cloud. In: 2016 IEEE International Conference on Big Data, pp. 763–772. 439 (2016). https://doi.org/10.1109/BigData.2016.7840669
Du, M., Li, F.: ATOM: efficient tracking, monitoring, and orchestration of cloud resources. IEEE Trans. Parallel Distrib. Syst. 28, 2172–2189 (2017)
Weng, C., Liu, Q., Li, K., Zou, D.: CloudMon: monitoring virtual machines in clouds. IEEE Trans. Comput. 65, 3787–3793 (2016). https://doi.org/10.1109/TC.2016.2560809
Lin, C.Y., Chang, M.C., Chiu, H.C., Shyu, K.H.: Secure logging framework integrating with cloud database. In: International Carnahan Conference on Security Technology, pp. 13–17 (2015)
Cloudstack: Open source cloud computing documentation, 2 April 2018. http://cloudstack.apache.org/
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Pauro, L., Spolon, R., Bruschi, G., Manacero, A., Lobato, R., Cavenaghi, M. (2019). AMFC Tool: Auditing and Monitoring for Cloud Computing. In: Coppola, M., Carlini, E., D’Agostino, D., Altmann, J., Bañares, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2018. Lecture Notes in Computer Science(), vol 11113. Springer, Cham. https://doi.org/10.1007/978-3-030-13342-9_11
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