Cloud Monitoring Data Challenges: A Systematic Review

Abstract

Organizations need to continuously monitor, source and process large amount of operational data for optimizing the cloud computing environment. The research problem is: what are cloud monitoring data challenges – in particular virtual CPU monitoring data? This paper adopts a Systematic Literature Review (SLR) approach to identify and report cloud monitoring data challenges. SLR approach was applied to initially identify a large set of 1861 papers. Finally, 24 of 1861 relevant papers were selected and reviewed to identify the five major challenges of cloud monitoring data: monitoring technology, virtualization technology, energy, availability and performance. The results of this review are expected to help researchers and practitioners to understand cloud computing data challenges and develop innovative techniques and strategies to deal with these challenges.

Appendix: Selected Papers Included in This Review

[S1] Anand, Ankit, et al. “Resource usage monitoring for KVM based virtual machines.” Advanced Computing and Communications (ADCOM), 2012 18th Annual International Conference on. IEEE, 2012.

[S2] Du, Jiaqing, Nipun Sehrawat, and Willy Zwaenepoel. “Performance profiling of virtual machines.” ACM SIGPLAN Notices 46.7 (2011): 3-14.

[S3] P. Vijaya Vardhan Reddy, P. Vijaya Vardhan Reddy, and Dr Lakshmi Rajamani Dr. Lakshmi Rajamani. “Performance Evaluation of Hypervisors in the Private Cloud based on System Information using SIGAR Framework and for System Workloads using Passmark.” International Journal of Advanced Science and Technology 70 (2014): 17-32.

[S4] Reddy, P., and Lakshmi Rajamani. “Performance comparison of different operating systems in the private cloud with KVM hypervisor using SIGAR framework.” Communication, Information & Computing Technology (ICCICT), 2015 International Conference on. IEEE, 2015.

[S5] Reddy, P. Vijaya Vardhan, and Lakshmi Rajamani. “Evaluation of Different Hypervisors Performance in the Private Cloud with SIGAR Framework.”International Journal of Advanced Computer Science and Applications (IJACSA) 5.2 (2014).

[S6] Reddy, P., and Lakshmi Rajamani. “Virtualization overhead findings of four hypervisors in the CloudStack with SIGAR.” Information and Communication Technologies (WICT), 2014 Fourth World Congress on. IEEE, 2014.

[S7] Houlihan, Ryan, et al. “Auditing cloud service level agreement on VM CPU speed.” Communications (ICC), 2014 IEEE International Conference on. IEEE, 2014.

[S8] Huang, Qiang, et al. “Auditing CPU Performance in Public Cloud.” Services (SERVICES), 2013 IEEE Ninth World Congress on. IEEE, 2013.

[S9] Alhamazani, Khalid, et al. “An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art.” Computing (2014): 1-21.

[S10] Madani, N., et al. “Power-aware Virtual Machines consolidation architecture based on CPU load scheduling.” Computer Systems and Applications (AICCSA), 2014 IEEE/ACS 11th International Conference on. IEEE, 2014.

[S11] Vrbsky, Susan V., et al. “Decreasing power consumption with energy efficient data aware strategies.” Future Generation Computer Systems 29.5 (2013): 1152-1163.

[S12] Katsaros, Gregory, et al. “A service framework for energy-aware monitoring and VM management in Clouds.” Future Generation Computer Systems 29.8 (2013): 2077-2091.

[S13] Verma, Akshat, Puneet Ahuja, and Anindya Neogi. “pMapper: power and migration cost aware application placement in virtualized systems.” Middleware 2008. Springer Berlin Heidelberg, 2008. 243-264.

[S14] Katsaros, Gregory, et al. “A Self-adaptive hierarchical monitoring mechanism for Clouds.” Journal of Systems and Software 85.5 (2012): 1029-1041.

[S15] Povedano-Molina, Javier, et al. “DARGOS: A highly adaptable and scalable monitoring architecture for multi-tenant Clouds.” Future Generation Computer Systems 29.8 (2013): 2041-2056.

[S16] Smit, Michael, Bradley Simmons, and Marin Litoiu. “Distributed, application-level monitoring for heterogeneous clouds using stream processing.” Future Generation Computer Systems 29.8 (2013): 2103-2114.

[S17] Lee, Hyungro, et al. “Towards Understanding Cloud Usage through Resource Allocation Analysis on XSEDE.”

[S18] Ranjan, Rajiv, et al. “A note on software tools and techniques for monitoring and prediction of cloud services.” Software: Practice and Experience 44.7 (2014): 771-775.

[S19] Serrano, Nicolas, Gorka Gallardo, and Josune Hernantes. “Infrastructure as a Service and Cloud Technologies.” IEEE Software 2 (2015): 30-36.

[S20] Manvi, Sunilkumar S., and Gopal Krishna Shyam. “Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey.” Journal of Network and Computer Applications 41 (2014): 424-440.

[S21] Dhingra, Mohit, J. Lakshmi, and S. K. Nandy. “Resource usage monitoring in clouds.” Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing. IEEE Computer Society, 2012.

[S22] Reddy, P., and Lakshmi Rajamani. “Performance comparison of different operating systems in the private cloud with KVM hypervisor using SIGAR framework.” Communication, Information & Computing Technology (ICCICT), 2015 International Conference on. IEEE, 2015.

[S23] Janpan, Tanasak, Vasaka Visoottiviseth, and Ryousei Takano. “A virtual machine consolidation framework for CloudStack platforms.” Information Networking (ICOIN), 2014 International Conference on. IEEE, 2014.

[S24] Miao, Tianxiang, and Haibo Chen. “FlexCore: Dynamic virtual machine scheduling using VCPU ballooning.” Tsinghua Science and Technology 20.1 (2015): 7-16.