An Alleviation of Cloud Congestion Analysis of Fluid Retrial User on Matrix Analytic Method in IoT-based Application

K. Nandhini; V. Vidhya

doi:10.46481/jnsps.2023.1148

Authors

K. Nandhini
Division of Mathematics, Vellore Institute of Technology, Chennai, Tamilnadu, India
V. Vidhya
[email protected]

Division of Mathematics, Vellore Institute of Technology, Chennai, Tamilnadu, India

Keywords:

Retrial queue, Stationary distribution, Congestion, Markov Fluid queue, Matrix analytic Method, IoT cloud computing

Abstract

Cloud Computing (CC) and Internet of Things (IoT) are upgrowing human intervention to enhance the daily lifestyle. Currently, the heavy loaded traffic congestion is a very big challenge over IoT-based applications. For that purpose, the researchers approached various ways to overcome the congestion mechanism in recent years. Even though, they have futile to acheive the best resource storage accessing capacity expectation other than, Cloud Computing. Data sharing is a key impediment of Cloud Computing as well as Internet of Things. These are the constituent that give rise to the combination of the IoT and cloud computing paradigm as IoT Cloud. Though, preserving the missed data during the execution time is a key factor to indulge the Retrial Queueing Theory (RQT), who is facing issue upon accessing Cloud Service Provider (CSP) enter into virtual pool to preserve the data for reuse. The paper imposes Markov Fluid analysis with Matrix Analytic Method (MAM) allows the data as continuous length of data rather than individual data to avoid the congestion. The virtual orbit queue follow constant retrial rate discipline, that is, head of the orbital users makes attempt to occupy the server are assumed to be independent and identically distributed (i.i.d). Steady-state expression presented to study the behaviour of congestion. An illustrative analysis is produced to gain deep perception into the system model.

Dimensions

REFERENCES

K. Vijayashree & A. Anjuka, “Fluid Queue Driven by an M/M/1 Queue Subject to Bernoulli-Schedule-Controlled Vacation and Vacation Interruption”, Advances in Operations Research (2016) 1.

B. Mao, F. Wang & N. Tian, “Fluid model driven by an M/M/1 queue with multiple vacations and N-policy”, Journal of Applied Mathematics And Computing 38 (2010) 119.

A. H. El-Baz, A. M. Tarabia & A. M. Darwiesh, “Cloud Storage Facility as a Fluid queue controlled by Markovian Queue”, Probability in the Engineering and Informational Sciences (2020) 1.

S. Ammar, “Fluid M/M/1 catastrophic queue in a random environment”, RAIRO - Operations Research 55 (2021) S2677.

S. Kapoor & S. Dharmaraja, “Steady state analysis of fluid queues driven by birth death processes with rational rates”, International Journal of Operational Research 37 (2020) 562.

B. Mao, F. Wang & N. Tian, “Fluid model driven by an M/M/1 queue with multiple exponential vacations and N-policy”, Journal of Applied Mathematics and Computing 38 (2012) 119.

Q.M. He, Matrix-analytic methods in stochastic models, Springer (2008).

R. Malhotra, M. Mandjes,W. Scheinhardt &W, J. Van den Berg, “A feedback fluid queue with two congestion control thresholds”, Mathematical Methods of Operations Research 70 (2008) 149.

V. Arunachalam, S. Kapoor & S. Dharmaraja, “Transient solution of fluid queue modulated by two independent birth-death processes”, International Journal of Operational Research 36 (2019) 1.

N. Starreveld, R. Bekker & M. Mandjes, “Occupation times for the finite buffer fluid queue with phase-type ON-times”, Operations Research Letters 46 (2018) 27.

G. Horv’th & B. Van Houdt, “A Multi-layer Fluid Queue with Boundary Phase Transitions and Its Application to the Analysis of Multi-type Queues with General Customer Impatience”, in 2012 Ninth International Conference on Quantitative Evaluation of Systems (2012) 23.

S. Kapoor & D. Selvamuthu, “On the exact transient solution of fluid queue driven by a birth death process with specific rational rates and absorption”, Operation Research 52 (2015) 746.

J. Bosman & R. N´u˜nez-Queija, “A spectral theory approach for extreme value analysis in a tandem of fluid queues”, Queueing Systems 78 (2014) 121.

V. Arunachalam, V. Gupta & S. Dharmaraja, “A fluid queue modulated by two independent birth–death processes”, Computers and Mathematics Wwth Applications 60 (2010) 2433.

A. R. Biswas, R. Gia reda, “Iot and cloud convergence: Opportunities and challenges”, in 2014 IEEE World Forum on Internet of Things (WFIoT) (2014) 375.

P. P. Ray, “A survey of iot cloud platforms”, Future Computing and Informatics Journal 1 (2016) 35.

A. Sajid, H. Abbas & K. Saleem, “Cloud-assisted iot-based scada systems security: A review of the state of the art and future challenges”, IEEE Access 4 (2016) 1375.

W.Wang, P. Xu & L. T. Yang, “Secure data collection, storage and access in cloud-assisted iot”, IEEE Cloud Computing, 5 (2018) 77.

J. S. Fu, Y. Liu, H. C. Chao, B. K. Bhargava & Z. J. Zhang, “Secure data storage and searching for industrial iot by integrating fog computing and cloud computing”, IEEE Transactions on Industrial Informatics 14 (2018) 4519.

K. Liu & L. J. Dong, “Research on cloud data storage technology and its architecture implementation”, Procedia Engineering 29 (2012) 133.

I. Odun-Ayo, O. Ajayi, B. Akanle, R. Ahuja, “An overview of data storage in cloud computing”, in 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS) (2017) 29.

https://doi.org/10.1109/ICNGCIS.2017.9.

S. Kuppusamy, V. Kaniappan, D. Thirupathi & T. Ramasubramanian, “Switch bandwidth congestion prediction in cloud environment”, Big Data, Cloud and Computing Challenges, Procedia Computer Science 50 (2015) 235.

R. Gu, K. Zhang, Z. Xu, Y. Che, B. Fan, H. Hou, H. Dai, L. Yi, Y. Ding, G. Chen & Y. Huang, “Fluid: Dataset abstraction and elastic acceleration for cloud-native deep learning training jobs”, in 2022 IEEE 38th International Conference on Data Engineering (ICDE) (2022) 2182. https://doi.org/10.1109/ICDE53745.2022.00209.

T. Yung, J. Martin, M. Takai & R. Bagrodia, “Integration of fluid-based analytical model with packet-level simulation for analysis of computer networks”, in Proceedings of SPIE - The International Society for Optical Engineering (2001).

M. Mandjes, D. Mitra & W. R. Scheinhardt, “Models of network access using feedback fluid queues”, Queueing Systems 44 (2003) 365.

O. E. Ojo, M. K. Kareem, O. Samuel & C. O. Ugwunna, “An internet-ofthings based real-time monitoring system for smart classroom”, Journal of the Nigerian Society of Physical Sciences 4 (2022) 573.

E. E. Akpanibah & U. O. Ini, “An Investor’s Investment Plan with Stochastic Interest Rate under the CEV Model and the Ornstein-Uhlenbeck Process”, Journal of the Nigerian Society of Physical Sciences 3 (2021) 172.

A. Vinodhini & V. Varadharajan, “Computational analysis of queues with catastrophes in a multiphase random environment”, Mathematical Problems in Engineering (2016) 1.

R. Sakthi, V. Varadharajan & K. Mahaboob, “Performance measures of state dependent mmpp/m/1 queue”, International Journal of Engineering and Technology 4 (2018) 942.