International Journal of Advanced Networking and Applications

Open Access Open Access  Restricted Access Subscription Access

Scalability of M/M/c Queue based Cloud-Fog Distributed Internet of Things Middleware


Affiliations
1 Dept. of Computer Science and Engineering, Research Center: Shri Guru Gobind Singhji Institute of Engineering and Technology, Under SRTM University, Nanded, Maharashtra, India
2 School of Computational Science, Swami Ramanand Teerth Marathwada (SRTM) University, Nanded, Maharashtra, India
 

The Internet of Things (IoT) extends the Internet wherein real world things are part of a computing network. The IoT has seen exponential growth and according to Cisco prediction about 50 billion devices will be connected by 2020. Handling this massive scale is challenging research issue. In this paper, we use cloud-fog-edge based IoT middleware for distributed IoT service provisioning. We model IoT middleware using queueing network, perform analytical analysis of IoT middleware components. Followed by dynamic scaling algorithm which considers contention and coherency as limiting factors for scalability. It is used for quantitative analysis of IoT middleware with the increasing workload. The scalability function was evaluated using the simulation for important performance and scalability parameters namely throughput, CPU utilization and response time. It is observed that because of contention and coherency overhead, the proposed approach is able to scale sub-linearly which is practical compared ideal scalability of multi-server queueing network and not very restrictive as given universal scalability law(USL) applied for tightly coupled systems.

Keywords

Fog Computing, Internet of Things, Middleware, Queueing Network, and Scalability Etc.
User
Notifications
Font Size

  • D. Evans, The Internet of Things: How the next evolution of the Internet is changing everything, white paper, Cisco, 2011.

  • O.Sezer, E. Dogdu, A. Ozbayoglu, Context-Aware computing, learning, and big data in Internet of things: a survey, IEEE IoT Journal, 5(1), 2018, 1-27.

  • D. Rathod, G. Chowdhary, Survey of middlewares for Internet of things, Proc. IEEE Int. Conf. on Recent Trends in Advanced Computing: CPS, Chennai, India, Sep. 10-11, 2018, 129-135.

  • M. Razzaque, M. Milojevic-Jevric, A. Palade, S. Clarke, Middleware for Internet of things: a survey, IEEE IoT Journal, 3(1), 2016, 70-95.

  • M. Harchol-Balter, Performance modeling and design of computer systems:queueing theory in action, Cambridge University Press, 2013.

  • M. Gomes, R. Righi, C. da Costa, Internet of things scalability: analyzing the bottlenecks and proposing alternatives, Proc. 6th Int. Congress on Ultra Modern Telecom. and Control Systems and Workshops (ICUMT), 2014, 269-276.

  • V. Soto, Performance evaluation of scalable and distributed IoT platforms for smart regions, Masters thesis, Dept. of CS, Ele. and Space Engg., Luleå University of Technology, Skellefteå, 2017.

  • Fiware: the open source platform for smart digital future, https://www.fiware.org/, (accessed 5 April 2019).

  • A. Esposte, E. Santana, L. Kanashiro, F. Costa, K. Braghetto, N. Lago, F. Kon, Design and evaluation of a scalable smart city software platform with large-scale simulations, Future Gen. Comp. Sys. 93, 2018, 427-441.

  • S. Maheshwari, D. Raychaudhuri, I. Seskar, F. Bronzino, Scalability and performance evaluation of edge cloud systems for latency constrained applications, Proc. 3rd ACM/IEEE Symposium on Edge Computing, 2018, 286-289.

  • Q. Fan, N. Ansari, Towards workload balancing in fog computing empowered IoT, IEEE Tran. on Network Sci. and Engg., Early Access.

  • B. Liu, X. Chang, B. Liu, Z. Chen, Performance analysis model for fog services under multiple resource types, Proc. IEEE Int. Conf. on Dependable Sys.and their Applications, 2017, 110-117.

  • R. Pinciroli, M. Gribaudo, M. Roveri, G. Serazzi, Capacity Planning of fog computing infrastructures for smart monitoring, New Frontiers in Quantitative Methods in Informatics, Comm. in Computer and Information Sci. 825, 2018, 72-81.

  • S. Duttagupta, M. Kumar, R. Ranjan, M. Nambiar, Performance prediction of IoT application an experimental analysis, Proc. ACM 6th Int. Conf. on the IoT, 2016, 43-51.

  • J. Huang, S. Li, Y. Chen, J. Chen, Performance modeling and analysis for IoT services, Int. J. Web and Grid Services, 14(2), 2018, 146-169.

  • S. Kafhali, K. Salah, Efficient and dynamic scaling of fog nodes for IoT devices, Springer J Supercomp. 73(12), 2017, 5261-5284.

  • C. Mouradian, D. Naboulsi, S. Yangui, R.H. Glitho, M. J. Morrow, P. A. Polakos, A comprehensive survey on fog computing: state-of-the-art and research challenges, IEEE Comm. Surveys and Tutorials 20(1) 2018, 416-464.

  • Fog computing and the Internet of things: extend the cloud to where the things are, Cisco, White paper, 2015.

  • A. Bondi, Characteristics of scalability and their impact on performance, Proc. 2nd int. workshop on sw and performance, 2000, 195-203.

  • C. Weinstock J. Goodenough, On System Scalability, Software Engineering Institute, Technical Note, CMU/SEI-2006-TN-012, 2006.

  • M. Abbott, M. Fisher, The Art of scalability: scalable web architecture, processes, and organizations for the modern enterprise 2nd edition, Pearson Education, 2015.

  • B. Neuman, Scale in distributed systems, IEEE Computer Society Press, 1994, 1-28.

  • P. Jogalekar, M. Woodside, Evaluating the scalability of distributed systems, IEEE Tran. on Parallel and Distributed Systems, 11(6), 2000, 589-603.

  • N. Gunther, Guerrilla capacity planning: a tactical approach to planning for highly scalable applications and services Springer-Verlag Berlin Heidelberg, 2007.

  • Neil Gunther, Paul Puglia, KristoferTomasette, HadoopSuperlinear Scalability, Comm. of the ACM 58(4), 2015, 46-55.

  • Rajkumar, H. Chandrakanth, D. Anand, T. Peter, Research Challenges andCharacteristic Features in Wireless Sensor Networks, Int. J. Advanced Networking and Applications vol.09(01), 2017, pp: 3321-3328.

  • Patel Rinkuben N., N. Bhatt, Design and Implementation of QoS Aware Priority based MAC for Delay Sensitive Areas of WSN, Int. J. Advanced Networking and Applications, vol.09(03), 2017, pp.3411-3420.


Abstract Views: 164

PDF Views: 0




  • Scalability of M/M/c Queue based Cloud-Fog Distributed Internet of Things Middleware

Abstract Views: 164  |  PDF Views: 0

Authors

Dilip Rathod
Dept. of Computer Science and Engineering, Research Center: Shri Guru Gobind Singhji Institute of Engineering and Technology, Under SRTM University, Nanded, Maharashtra, India
Girish Chowdhary
School of Computational Science, Swami Ramanand Teerth Marathwada (SRTM) University, Nanded, Maharashtra, India

Abstract


The Internet of Things (IoT) extends the Internet wherein real world things are part of a computing network. The IoT has seen exponential growth and according to Cisco prediction about 50 billion devices will be connected by 2020. Handling this massive scale is challenging research issue. In this paper, we use cloud-fog-edge based IoT middleware for distributed IoT service provisioning. We model IoT middleware using queueing network, perform analytical analysis of IoT middleware components. Followed by dynamic scaling algorithm which considers contention and coherency as limiting factors for scalability. It is used for quantitative analysis of IoT middleware with the increasing workload. The scalability function was evaluated using the simulation for important performance and scalability parameters namely throughput, CPU utilization and response time. It is observed that because of contention and coherency overhead, the proposed approach is able to scale sub-linearly which is practical compared ideal scalability of multi-server queueing network and not very restrictive as given universal scalability law(USL) applied for tightly coupled systems.

Keywords


Fog Computing, Internet of Things, Middleware, Queueing Network, and Scalability Etc.

References