Abstract
Last two decades have witnessed a tremendous evolution and growth of cloud computing paradigm and reliability estimation techniques. In spite of recent advancements in this field, very little work has been reported over population bounded personal and private clouds. In this work, a highly responsive and reliable queuing model for finite population clouds has been proposed. The model has an explicit feature of varying length of the waiting queue in accordance with the incoming requests and suggesting the number of virtual machines needed per physical machine. This model has been represented in a generalized hypergeometeric representation which is a simple way to represent queuing based cloud service model. The performance parameters like server utilization, response time and request stage reliability of the proposed model were thoroughly analyzed and compared with the popular existing cloud queuing model and improvement in server utilisation has been observed.
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References
Mell, P., & Grance, T. et al. (2011). The NIST definition of cloud computing
Costello, K., & Hippold, S. (2019). Gartner forecasts worldwide public cloud revenue to grow 17.5 percent in 2019. Connecticut: Stamford. Dostupno na: https://www.gartner.com/en/newsroom/press-releases/2019-04-02-gartner-forecastsworldwide-public-cloud-revenue-to-g [10. rujna 2019
Karpovich, B., Kesterson-Townes, L., & Rishi, S. (2017). Beyond agility-How cloud is driving enterprise innovation. New York, USA: IBM Institute for Business Value.
Liu, G., Xiao, Z., Tan, G. H., Li, K., & Chronopoulos, A. T. (2020). Game theory-based optimization of distributed idle computing resources in cloud environments. Theoretical Computer Science, 806, 468. https://doi.org/10.1016/j.tcs.2019.08.019
Fisher, C., et al. (2018). Cloud versus on-premise computing. American Journal of Industrial and Business Management, 8(09), 1991.
Santhi, K., & Saravanan, R. (2017). Performance analysis of cloud computing using batch queueing models in healthcare systems. Research Journal of Pharmacy and Technology, 10(10), 3331. https://doi.org/10.5958/0974-360X.2017.00591.1
Kameda, H. (1982). A Finite-source queue with different customers. Journal of ACM, 29(2), 478. https://doi.org/10.1145/322307.322320.
Chiang, Y. J., Ouyang, Y. C., & Hsu, C. H. R. (2016). Performance and cost-effectiveness analyses for cloud services based on rejected and impatient users. IEEE Transactions on Services Computing, 9(3), 446. https://doi.org/10.1109/TSC.2014.2365783.
Louchard, G. (1994). Large finite population queueing systems. The single-server model. Stochastic Processes and their Applications, 53(1), 117. https://doi.org/10.1016/0304-4149(94)90060-4.
El Kafhali, S., & Salah, K. (2017). Stochastic modelling and analysis of cloud computing data center. In 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN) (IEEE, 2017). https://doi.org/10.1109/ICIN.2017.7899401
Chen, H.P., & Li, S.C. (2010). A queueing-based model for performance management on cloud. Proc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications pp. 83–88
Kalyanaraman, R., & Nagarajan, P. (2019). Bulk arrival, fixed batch service queue with unreliable server, Bernoulli vacation and with delay time. In AIP Conference Proceedings, vol. 2177, p. 020034. https://doi.org/10.1063/1.5135209
Sahoo, C. N., & Goswami, V. (2016). Performance evaluation of cloud centers with high degree of virtualization to provide mapreduce as service. International Journal of Advances in Soft Computing and its Applications, 8(3), 193.
Andrews, G.E., & Garvan, F. (2018). Analytic Number Theory, Modular Forms and Q-Hypergeometric Series: In Honor of Krishna Alladi’s 60th Birthday, University of Florida, Gainesville, March 2016, vol. 221 (Springer)
Bringmann, K., Lovejoy, J., & Rolen, L. (2018). On Some Special Families of q-hypergeometric Maass Forms. International Mathematics Research Notices, 2018(18), 5537. https://doi.org/10.1093/imrn/rnx057
Andrews, G. E. (1974). Applications of basic hypergeometric functions. SIAM review, 16(4), 441.
Pradhan, S., Damodaran, P., & Srihari, K. (2008). Predicting performance measures for Markovian type of manufacturing systems with product failures. European Journal of Operational Research, 184(2), 725. https://doi.org/10.1016/j.ejor.2006.11.016. https://www.sciencedirect.com/science/article/pii/S0377221706011520
Dai, Y. S., Yang, B., Dongarra, J., & Zhang, G. (2009). in 15th IEEE Pacific Rim International Symposium on Dependable Computing (Citeseer, 2009). Cloud service reliability: Modeling and analysis. (pp. 1–17)
Kaur, G., & Kumar, R. (2015). A review on reliability issues in cloud service. International Journal of Computer Applications, 975, 8887.
Gill, S. S., & Buyya, R. (2020). Failure management for reliable cloud computing: A taxonomy, model, and future directions. Computing in Science & Engineering, 22(3), 52. https://doi.org/10.1109/MCSE.2018.2873866.
Zio, E., & Podofillini, L. (2003). Monte Carlo simulation analysis of the effects of different system performance levels on the importance of multi-state components. Reliability Engineering & System Safety, 82(1), 63. https://doi.org/10.1016/S0951-8320(03)00124-8.
Bahwaireth, K., Tawalbeh, L., Benkhelifa, E., Jararweh, Y., & Tawalbeh, M. A. (2016). Experimental comparison of simulation tools for efficient cloud and mobile cloud computing applications. EURASIP Journal on Information Security, 2016(1), 15. https://doi.org/10.1186/s13635-016-0039-y
Fakhfakh, F., Kacem, H. H., & Kacem, A. H. (2017). Simulation tools for cloud computing: A survey and comparative study. In 2017 IEEE/ACIS 16th International conference on Computer and Information Science (ICIS) (IEEE, 2017) (pp. 221–226). https://doi.org/10.1109/ICIS.2017.7959997
Snyder, B., Green, R. C., Devabhaktuni, V., & Alam, M. (2018). ReliaCloud-NS: A scalable web-based simulation platform for evaluating the reliability of cloud computing systems. Software: Practice and Experience, 48(3), 665. https://doi.org/10.1002/spe.2541.
Garg, S.K., & Buyya, R. (2011). NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations. In 2011 Fourth IEEE Int. Conf. Util. Cloud Comput. (IEEE), Vm, pp. 105–113. https://doi.org/10.1109/UCC.2011.24
Goswami, V., Patra, S. S., & Mund, G. B. (2012). Performance analysis and optimal resource usage in finite population cloud environment. In 2012 2nd IEEE International conference on parallel, distributed and grid computing (IEEE, 2012). pp. 679–684. https://doi.org/10.1109/PDGC.2012.6449902
Chiang, Y. J., & Ouyang, Y. C. (2014). Profit optimization in sla-aware cloud services with a finite capacity queuing model. Mathematical Problems in Engineering. https://doi.org/10.1155/2014/534510.
Kumar, R., Sahoo, G., Yadav, V., & Malik, P. (2017). Minimizing the energy consumption of cloud computing data centers using queueing theory. In Advances in Computational Intelligence (Springer, 201–210.
Ellens, W., Ivkovic, M., Akkerboom, J., Litjens, R., & van den Berg, H. (2012). Performance of Cloud Computing Centers with Multiple Priority Classes. In 2012 IEEE Fifth International conference on cloud computing (IEEE, 2012). pp. 245–252. https://doi.org/10.1109/CLOUD.2012.96
Ahuja, A., Jain, A., & Jain, M. (2019). Finite population multi-server retrial queueing system with an optional service and balking. International Journal of Computers and Applications, 41(1), 53. https://doi.org/10.1080/1206212X.2018.1505023
Andrews, G. E. (1974). Applications of basic hypergeometric functions. SIAM Review, 16(4), 441. https://doi.org/10.1137/1016081
Beukers, F. (2014). Hypergeometric Functions, How Special Are They? Notices of the American Mathematical Society, 61(01), 48. https://doi.org/10.1090/noti1065
Economou, A., & Kapodistria, S. (2010). Synchronized abandonments in a single server unreliable queue. European Journal of Operational Research, 203(1), 143. https://doi.org/10.1016/j.ejor.2009.07.014.
Ammar, S. I. (2015). Transient analysis of an M/M/1 queue with impatient behavior and multiple vacations. Applied Mathematics and Computation, 260, 97. https://doi.org/10.1016/j.amc.2015.03.066.
El Kafhali, S., & Salah, K. (2018). Modeling and analysis of performance and energy consumption in cloud data centers. Arabian Journal for Science and Engineering, 43(12), 7789. https://doi.org/10.1007/s13369-018-3196-0.
Xianrong Zheng, P., & Martin, K. Brohman. (2014). Li Da Xu, CLOUDQUAL: A Quality Model for Cloud Services. IEEE Transactions on Industrial Informatics, 10(2), 1527. https://doi.org/10.1109/TII.2014.2306329. http://ieeexplore.ieee.org/document/6740846/.
Badian-Pessot, P., Lewis, M. E., & Down, D. G. (2019). Optimal control policies for an M/M/1 queue with a removable server and dynamic service rates. Probability in the Engineering and Informational Sciences. https://doi.org/10.1017/S0269964819000299
Toka, L., Haja, D., Korosi, A., & Sonkoly, B. (2019). Resource provisioning for highly reliable and ultra-responsive edge applications. In 2019 IEEE 8th International conference on cloud networking (CloudNet) (IEEE, 2019). pp. 1–6. https://doi.org/10.1109/CloudNet47604.2019.9064131
Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30.
Robertazzi, T. G. (2012). Computer networks and systems: queueing theory and performance evaluation. Springer.
Borovkov, A. (2012). Stochastic processes in queueing theory (Vol. 4). Springer.
Medhi, J. (2002). Stochastic models in queueing theory. Elsevier.
Little, J. D. C. (1961). A proof for the queuing formula: L = \(\lambda \) W. Operations Research, 9(3), 383. https://doi.org/10.1287/opre.9.3.383.
Ghobaei-Arani, M., Jabbehdari, S., & Pourmina, M. A. (2016). An autonomic approach for resource provisioning of cloud services. Cluster Computing, 19(3), 1017. https://doi.org/10.1007/s10586-016-0574-9.
Singh, S., & Chana, I. (2016). A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing, 14(2), 217. https://doi.org/10.1007/s10723-015-9359-2.
Madhu, J., & Amita, B. (2012). Retrial queue with threshold recovery, geometric arrivals and finite capacity. Journal of Operations Management, 3(1), 1039. https://doi.org/10.1007/978-81-322-0491-6_96.
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Sharma, R., Gupta, P. & Singh, R. Hypergeometrically Represented Responsive and Reliable Cloud Service Model for Personal and Private Clouds. Wireless Pers Commun 125, 1501–1521 (2022). https://doi.org/10.1007/s11277-022-09618-w
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DOI: https://doi.org/10.1007/s11277-022-09618-w