Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment
Source abstract
Abstract
Elasticity is one of the key features of cloud-hosted services built on virtualization technology. To utilize the elasticity of cloud environments, administrators should accurately capture the operational status of server systems, which changes constantly according to service requests incoming irregularly. However, it is difficult to detect and avoid in advance that operating services are falling into an undesirable state. In this paper, we focus on the management of server systems that include cloud systems, and propose a new method for detecting the sign of undesirable scenarios before the system becomes overloaded as a result of various causes. In this method, a measure that utilizes the fluctuation of the macroscopic operational state observed in the server system is introduced. The proposed measure has the property of drastically increasing before the server system is in an undesirable state. Using the proposed measure, we realize a function to detect that the server system is falling into an overload scenario, and we demonstrate its effectiveness through experiments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cloud Computing and Resource Management · Software System Performance and Reliability · IoT and Edge/Fog Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Information Sciences, Tohoku University, Sendai 9808579, Japan
Research Institute of Electrical Communication, Tohoku University, Sendai 9808577, Japan
References (30)
Show source-provided references
1. Martin, P., Brown, A., Powley, W., and Vazquez-Poletti, J.L. (July, January 28). Autonomic management of elastic services in the cloud. Proceedings of the 2011 IEEE Symposium on Computers and Communications (ISCC), Kerkyra, Greece.10.1109/ISCC.2011.5984006 ↗
2. Towards an architecture for deploying elastic services in the cloud10.1002/spe.1090 ↗
3. Kim, I.K., Steele, J., Qi, Y., and Humphrey, M. (2014, January 8–11). Comprehensive elastic resource management to ensure predictable performance for scientific applications on public IaaS clouds. Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, London, UK.10.1109/UCC.2014.45 ↗
4. Vmware distributed resource management: Design, implementation, and lessons learned
5. Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., and Warfield, A. (2003, January 19–22). Xen and the art of virtualization. Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles—SOSP’03, Bolton Landing, NY, USA.10.1145/945461.945462 ↗
6. (2017, September 06). Hyper-V. Available online: https://www.microsoft.com/en-us/cloud-platform/server-virtualization.
7. Cockcroft, A. (2006, January 3–8). Utilization is virtually useless as a metric!. Proceedings of the Annual International Conference of the Computer Measurement Group 2006 (CMG2006), Reno, NV, USA.
8. Adaptation in cloud resource configuration: A survey10.1186/s13677-016-0057-9 ↗
9. Ferretti, S., Ghini, V., Panzieri, F., Pellegrini, M., and Turrini, E. (2010, January 5–10). QoS-Aware Clouds. Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing, Miami, FL, USA.
10. QoS-aware autonomic resource management in cloud computing10.1145/2843889 ↗
11. Cost optimization of elasticity cloud resource subscription policy10.1109/TSC.2013.35 ↗
12. Dib, D., Parlavantzas, N., and Morin, C. (2014, January 26–29). SLA-based profit optimization in cloud bursting PaaS. Proceedings of the 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Chicago, IL, USA.10.1109/CCGrid.2014.78 ↗
13. Li, Z., and Wu, G. (2016, January 17–18). Optimizing VM live migration strategy based on migration time cost modeling. Proceedings of the 2016 Symposium on Architectures for Networking and Communications Systems—ANCS’16, Santa Clara, CA, USA.10.1145/2881025.2881035 ↗
14. Poellabauer, C., Schwan, A., and West, R. (2001, January 25–26). Lightweight kernel/user communication for real-time and multimedia applications. Proceedings of the 11th International Workshop on Network and Operating Systems Support for Digital Audio and Video—NOSSDAV’01, Port Jefferson, NY, USA.10.1145/378344.378365 ↗
15. Garroppo, R.G., Giordano, S., Spagna, S., and Niccolini, S. (December, January 30). Queueing strategies for local overload control in SIP server. Proceedings of the 2009 IEEE Global Telecommunications Conference, Honolulu, HI, USA.10.1109/GLOCOM.2009.5425591 ↗
16. Elleithy, K.M., and Komaralingam, A. (August, January 29). Using a queuing model to analyze the performance of web servers. Proceedings of the International Conference on Advances in Infrastructure for Electronic Business, Education, Science, and Medicine on the Internet, L’Aquila, Italy.
17. httperf—A tool for measuring web server performance10.1145/306225.306235 ↗
18. Measuring the capacity of a Web server under realistic loads10.1023/A:1019292504731 ↗
19. (2017, September 06). Apache JMeter. Available online: http://jmeter.apache.org/.
20. (2017, September 06). Tsung. Available online: http://tsung.erlang-projects.org/.
21. Web server performance measurement and modeling techniques10.1016/S0166-5316(98)00008-X ↗
22. Abdelzaher, T.F., and Lu, C.Y. (2000, January 12–15). Modeling and performance control of Internet servers. Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), Sydney, Australia.
23. Cao, J., Andersson, M., Nyberg, C., and Kihl, M. (March, January 23). Web server performance modeling using an M/G/1/K*PS queue. Proceedings of the 10th International Conference on Telecommunications, Papeete, French Polynesia.
24. Performance modeling and system management for multi-component online services
25. Tsakalozos, K., Kllapi, H., Sitaridi, E., Roussopoulos, M., Paparas, D., and Delis, A. (2011, January 11–16). Flexible use of cloud resources through profit maximization and price discrimination. Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, Hannover, Germany.10.1109/ICDE.2011.5767932 ↗
26. Maguluri, S.T., Srikant, R., and Ying, L. (2012, January 25–30). Stochastic models of load balancing and scheduling in cloud computing clusters. Proceedings of the 2012 Proceedings IEEE INFOCOM, Orlando, FL, USA.10.1109/INFCOM.2012.6195815 ↗
27. Basic Characteristics of a macroscopic measure for detecting abnormal changes in a multiagent system10.3390/s150409112 ↗
28. Moghal, M.R., Hussain, N., Mirza, M.S., Jarral, M.W., and Choudry, M.S. (2004, January 12–15). Performance evaluation and modeling of web server systems. Proceedings of the 8th WSEAS International Conference on Computers, Athens, Greece.
29. On the theory of the brownian motion II10.1103/RevModPhys.17.323 ↗
30. (2017, September 06). ab—Apache HTTP Server Benchmarking Tool. Available online: https://httpd.apache.org/docs/2.4/programs/ab.html.