Exploring Analytical Models for Performability Evaluation of Virtualized Servers using Dynamic Resource
AbstractVirtualization of resources is a widely accepted technique to optimize resources in recent technologies. Virtualization allows users to execute their services on the same physical machine, keeping these services isolated from each other. This paper proposes the analytical models for performability evaluation of virtualized servers with dynamic resource utilization. The performance and avalability models are considered separately due to the behaviour of the proposed system. The well-known Markov Reward Model (MRM) is used for the solution of the analytical model considered together with an exact spectral expansion and product form solution. The dynamic resource utilization is employed to enhance the QoS of the proposed model which is another major issue in the performance characterization of virtulazilation. In this paper, the performability output parameters, such as mean queue length, mean response time and blocking probability are computed and presented for the proposed model. In addition, the performability results obtained from the analytical models are validated by the simulation (DES) results to show the accuracy and effectiveness of the proposed work. The results indicate the proposed modelling results show good agreement with DES and understand the factors are very important to improve the QoS.
 Bi, J.; Yuan, H.; Tan, W.; Zhou, M.C.; Fan, Y.; Zhang, J.; Li, J.G. (2017). Application- Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center, IEEE Transactions on Automation Science and Engineering, 14(2), 1172-1184, 2017.
 Chakka, R. (1995). Performance and reliability modelling of computing systems using spectral expansion, Ph.D. thesis, University of Newcastle, Upon Tyne, UK, 1995.
 Ever, Y.K.; Kirsal, Y.; Ever, E.; Gemikonakli, O. (2015). Analytical modelling and performability evaluation of multi channel WLANs with global failures. International Journal of Computers Communications & Control, 10(10), 551-566, 2015.
 Gemikonakli, O.; Ever, E.; Gemikonakli, E. (2009). Performance modelling of virtualized servers. International Conference on Computer Modelling and Simulation, 434-438, 2009.
 Goswami, V.; Patra, S.S.; Mund, G.B. (2012). Performance analysis of cloud with queue dependent virtual machines. International Conference on Recent Advances in Information Technology (RAIT), 357-362, 2012.
 Iyer, R.; Illikkal, R.; Tickoo, O.; Zhao, L.; Apparao, P.; Newell, D. (2009). VM3: Measuring, modeling and managing VM shared resources. Computer Networks, 53(17), 2873-2887, 2009.
 Kim, D. S.; Hong, J. B.; Nguyen, T. A.; Machida, F.; Park, J. S.; Trivedi, K. S. (2016). Availability modeling and analysis of a virtualized system using stochastic reward nets. In IEEE International Conference on Computer and Information Technology (CIT), 210-218, 2016.
 Kim, D. S.; Machida, F.; Trivedi, K. S. (2009). Availability modeling and analysis of a virtualized system. In IEEE Pacific Rim International Symposium on Dependable Computing,365-371, 2009.
 Kirsal, Y. (2016). Analytical modelling of a new handover algorithm for improve allocation of resources in highly mobile environments. International Journal of Computers Communications & Control, 11(6), 789-803, 2016.
 Kirsal, Y.; Paranthaman, V. V.; Mapp, G. (2018). Exploring Analytical Models for Proactive Resource Management in Highly Mobile Environments. International Journal of Computers Communications & Control, 13(5), 837-852, 2018.
 Liu, N.; Li, X.; Wang, Q. (2011). A resource and capability virtualization method for cloud manufacturing systems, IEEE Int. Conf. on Systems, Man, and Cybernetics, 1003-1008, 2011.
 Magalhaes, D.; Calheiros, R. N.; Buyya, R.; Gomes, D. G. (2015). Workload modeling for resource usage analysis and simulation in cloud computing, Computers and Electrical Engineering, 47, 69-81, 2015.
 Mitrani. I. (2001). Queues with Breakdowns, Performability Modelling:Techniques and Tools, Wiley, Chichester, 2001.
 Odun-Ayo, I.; Ajayi, O.; Falade, A. (2018). Cloud Computing and Quality of Service: Issues and Developments,In International Multi-Conference of Engineers and Computer Scientists, 2018.
 Oliveira, D.; Brinkmann, A.; Rosa, N.; Maciel, P. (2019). Performability evaluation and optimization of workflow applications in cloud environments, Journal of Grid Computing, 1-22, 2019.
 Peng, C. H.; Chong, L.S. (2010). A queueing-based model for performance management on cloud. International Conference on Advanced Information Management and Service (IMS), 83-88, 2010.
 Sotomayor, B.; Montero, R.S.; Llorente, I.M. (2009). Virtual infrastructure management in private and hybrid clouds, IEEE Internet Comput., 13(5), 14-22, 2009.
 Tian, W.; He, M.; Guo, W.; Huang, W.; Shi, X.; Shang, M.; Buyya, R. (2018). On minimizing total energy consumption in the scheduling of virtual machine reservations. Journal of Network and Computer Applications, 113, 64-74, 2018.
 Wu, Y.; Zhao, M. (2011). Performance modeling of virtual machine live migration. In IEEE 4th International Conference on Cloud Computing, 492-499, 2011.
 Zhang, X.; Wu, T.; Chen, M.; Wei, T.; Zhou, J.; Hu, S.; Buyya, R. (2019). Energy-aware virtual machine allocation for cloud with resource reservation. Journal of Systems and Software, 147, 147-161, 2019.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.