Monitoring IaaS using various cloud monitors

  • Absa StephenEmail author
  • Shajulin Benedict
  • R. P. Anto Kumar


In cloud computing, the performance of infrastructure as a service is critical because of its divergence in area. The cloud providers guarantee that the resources will be available around the clock. The providers assure that the period of unavailability of resources is very less. Recently the cloud users have increased rapidly; therefore the providers also have increased, basically increasing the complexity of the infrastructure. This complex infrastructure should be allocated properly to the users and the availability of resources should be notified to the users. So monitoring of these resources constantly is critical. In this analysis, comparison of various monitoring tools in terms of SLA parameters are measured and tabulated. For the comparison, the Amazon cloud instances are monitored with three different monitoring tools like CloudWatch monitoring, IDERA uptime cloud monitor and ManageEngine applications manager. The SLA parameters of IaaS are CPU utilization, network in, network out, disk read, disk write, response time and memory usage. In addition with Amazon instances, servers like Tomcat and data base like PostgreSQL are also monitored and their performance parameters are also analyzed. The instances monitored by cloudwatch monitoring gives twice the range of CPU Utilization than the others. The network data transfer is also high using cloudwatch.


Performance analysis Availability Monitoring Software as a service Servers 


  1. 1.
    Montesa, J., Sánchez, A., Memishi, B., Pérez, M.S., Antoniu, G.: GMonE: a complete approach to cloud monitoring. Futur. Gener. Comput. Syst. 29(8), 2026–2040 (2013).
  2. 2.
    Absa, S., Benedict, S.: A survey on SLA based cloud architectures. J. Converg. Inf. Technol. 11(1), 1–12 (2016)Google Scholar
  3. 3.
    Kertesz, A., Kecskemeti, G., Brandic, I.: An interoperable and self-adaptive approach for SLA-based service virtualization in heterogeneous cloud environments. Futur. Gener. Comput. Syst. 32, 54–68 (2014).
  4. 4.
  5. 5.
  6. 6.
    Da Cunha Rodrigues, G., Calheiros, R.N.: Monitoring of cloud computing environments: concepts, solutions, trends, and future directions. ACM 2016, SAC 2016 (2016).
  7. 7.
    Weingärtner, R., Bräscher, G.B., Westphall, C.B.: Cloud resource management: a survey on forecasting and profiling models. J. Netw. Comput. Appl. 47, 99–106 (2015).
  8. 8.
    Alecsandru, P., Patriciu, V.V.: Digital forensics in Cloud computing. Adv. Electr. Comput. Eng. 14(2), 101–108 (2014).
  9. 9.
    Aceto, G., Botta, A., de Donato, W., Pescapè, A.: Cloud monitoring: a survey. Comput. Netw. 57(9), 2093–2115 (2013).
  10. 10.
    Alhamazani, K.: An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art. Computing 97(4), 357–377 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Computing Performance issues and performance analysis tools for HPC cloud applications: a survey. 95(2), 89–108 (2013).
  12. 12.
    Giannakou, A., Rillingy, L., Pazatz, J.-L., Majorczyky, F., Morin, C.: Towards self adaptable security monitoring in IaaS clouds,”15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CC-GRID 2015) (2015)Google Scholar
  13. 13.
    de Chaves, S.A., Uriarte, R.B., Westphall, C.B.: Toward an architecture for monitoring private clouds. IEEE Commun. Mag. 49(12), 130–137 (2011)CrossRefGoogle Scholar
  14. 14.
    Petcu, D., Cr\({\breve{{\rm a}}}\)ciun, C.: Towards a security SLA-based cloud monitoring service. In: CLOSER 2014 - 4th International Conference on Cloud Computing and Services Science, pp. 593-603Google Scholar
  15. 15.
    Trapero, R., Modic, J., Stopar, M., Taha, A., Suri, N.: A novel approach to manage cloud security SLA incidents. Futur. Gener. Comput. Syst. 72, 193–205 (2017).
  16. 16.
    Ghosha, R., Longo, F., Naik, V.K., Trivedi, K.S.: Modeling and performance analysis of large scale IaaS Clouds. Futur. Gener. Comput. Syst. 29(5), 1216–1234 (2013).
  17. 17.
    Stantchev, V., Schröpfer, C.: Negotiating and enforcing QoS and SLAs in grid and cloud computing. GPC 2009, LNCS 5529, 25–35 (2009)Google Scholar
  18. 18.
    Casalicchio, E., Silvestri, L.: Mechanisms for SLA provisioning in cloud-based service providers. Comput. Netw. 57(3), 795–810 (2013).
  19. 19.
    Vincent, C., Emeakaroha, V.C., Ferreto, T.C., Netto, M.A., Brandic, I., De Rose, C.A.: CASViD: application level monitoring for SLA violation detection in clouds. In: IEEE 36th Annual Conference on Computer Software and Applications (COMPSAC) (2012).
  20. 20.
    Larsson, L., Henriksson, D., Elmroth, E.: Scheduling and monitoring of internally structured services in cloud federations. In: IEEE Symposium on Computers and communications (ISCC) (2011).
  21. 21.
    Grati, R., Boukadi, K., Ben-Abdallah, H.: Overview of IaaS monitoring tools. In: IEEE/ACS 12th International Conference on Computer Systems and Applications (AICCSA) (2015).
  22. 22.
    Vijayakumar, K., Arun, C.: Automated risk identification using NLP in cloud based development environments. J. Ambient Intell. Hum. Comput., 1–13 (2017).
  23. 23.
    Vijayakumar, K., Arun, C.: Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC. Clus. Comput. 1–12 (2017).
  24. 24.
    Vijayakumar, K., Arun, C.: Analysis and selection of risk assessment frameworks for cloud based enterprise applications. Biomed Res ISSN: 0976-1683 (Electronic) (2017)Google Scholar
  25. 25.
    Dawoud, W., Takouna, I., Meine, C.: Infrastructure as a service security: challenges and solutions. In: The 7th International Conference on Informatics and Systems (INFOS) (2010)Google Scholar
  26. 26.
    Varatharajan, R., Manogaran, G., Priyan, M.K., Sundarasekar, R.: Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clus. Comput.
  27. 27.
    Varatharajan, R., Hariharan, N., Perumal, S., Sankar, A.: A Novel Method to Increase the coupling efficiency of laser to single mode fibre. Wirel. Pers. Commun. 87, 419–430 (2016).
  28. 28.
    Katsaros, G., et al.: A self-adaptive hierarchical monitoring mechanism for clouds. J. Syst. Softw. 85(5), 1029–1041 (2012)CrossRefGoogle Scholar
  29. 29.
    Fatema, K., Emeakaroha, V.C., Healy, P.D., Morrison, J.P., Lynn”, T.: A survey of cloud monitoring tools: taxonomy, capabilities and objectives. J. Parallel Distrib. Comput. 74(10), 2918–2933 (2014).
  30. 30.
  31. 31.
  32. 32.
  33. 33.
    ManageEngine application manager user guide.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSt.Xavier’s Catholic College of Engineering, Anna UniversityNagercoilIndia
  2. 2.Indian Institute of Information Technology KottayamKottayamIndia
  3. 3.Department of Computer Science and EngineeringSt.Xavier’s Catholic College of Engineering, Anna UniversityNagercoilIndia

Personalised recommendations