Shared Cache Optimized Adaptive Load Balancing Strategy for IoT Devices

Authors

  • Tirupathi Puppala Kakatiya University, Warangal – 506009, INDIA
  • Niranjan Polala Kakatiya Institute of Science Technology, Warangal – 506015, INDIA

Keywords:

Adaptive Load, IoT, Shared Cache

Abstract

As the use of wireless communication expands, so does the need for more complex, easy-to-use, and low-cost solutions. The need for network solutions ranging from wireless sensor networks to wireless ad-hoc networks to the Internet of Things prompted academics to engage in the development of acceptable network solutions. Inventions made by researchers have led to an increase in the desire for additional advancements in current researchers. In the beginning, research and development focused on network protocols. IoT devices are being employed in a variety of industries and are amassing an enormous amount of data through sophisticated applications, regardless. This necessitates study into IoT network load balancing. As IoT networks become more overburdened, researchers have made many efforts to find ways to reduce the communication costs that result. In these studies, the IoT nodes were recommended to be evenly distributed in the network's load. The data gathered by IoT nodes and the applications that handle that data will eventually be moved to the cloud, but this will take time. A cloud-based load balancer meeting the needs of IoT network protocols is the difficulty here. A new technique is proposed in this study to deal with IoT network frameworks' load management. The main problem of this study is to develop a load balancer that considers the limited energy and processing capabilities of IoT nodes, yet with the goal of increasing the response time of the IoT network. Consideration has been given to the low-effort integrations with current IoT frameworks in the design of the suggested algorithm for load balancer.

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References

Q. Liu, T. Xia, L. Cheng, M. van Eijk, T. Ozcelebi and Y. Mao, "Deep Reinforcement Learning for Load-Balancing Aware Network Control in IoT Edge Systems," in IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 6, pp. 1491-1502, 1 June 2022.

C. Roy, S. Misra, J. Maiti and S. Nag, "EdgeSafe: Dynamic Load Balancing Among Edge Nodes for Provisioning Safety-as-a-Service in Vehicular IoT Applications," in IEEE Transactions on Vehicular Technology, vol. 70, no. 9, pp. 9320-9329, Sept. 2021.

A. Amjad, F. Azam, M. W. Anwar and W. H. Butt, "A Systematic Review on the Data Interoperability of Application Layer Protocols in Industrial IoT," in IEEE Access, vol. 9, pp. 96528-96545, 2021.

A. Asghar, A. Abbas, H. A. Khattak and S. U. Khan, "Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems," in IEEE Access, vol. 9, pp. 96189-96200, 2021.

Y. Shao, S. C. Liew, H. Chen and Y. Du, "Flow Sampling: Network Monitoring in Large-Scale Software-Defined IoT Networks," in IEEE Transactions on Communications, vol. 69, no. 9, pp. 6120-6133, Sept. 2021.

H. Choi, T. Kim, H. -S. Park and J. K. Choi, "A Cooperative Online Learning-Based Load Balancing Scheme for Maximizing QoS Satisfaction in Dense HetNets," in IEEE Access, vol. 9, pp. 92345-92357, 2021.

Z. Nezami, K. Zamanifar, K. Djemame and E. Pournaras, "Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things," in IEEE Access, vol. 9, pp. 64983-65000, 2021.

S. Aljanabi and A. Chalechale, "Improving IoT Services Using a Hybrid Fog-Cloud Offloading," in IEEE Access, vol. 9, pp. 13775-13788, 2021.

Y. Dong, G. Xu, M. Zhang and X. Meng, "A High-Efficient Joint ’Cloud-Edge’ Aware Strategy for Task Deployment and Load Balancing," in IEEE Access, vol. 9, pp. 12791-12802, 2021.

W. -Z. Zhang et al., "Secure and Optimized Load Balancing for Multitier IoT and Edge-Cloud Computing Systems," in IEEE Internet of Things Journal, vol. 8, no. 10, pp. 8119-8132, 15 May15, 2021.

N. A. S. Al-Jamali and H. S. Al-Raweshidy, "Intelligent Traffic Management and Load Balance Based on Spike ISDN-IoT," in IEEE Systems Journal, vol. 15, no. 2, pp. 1640-1651, June 2021.

X. Zheng et al., "A Reliable Communication and Load Balancing Scheme for Resource-Limited Networks," in IEEE Access, vol. 8, pp. 179921-179930, 2020.

W. Zhang, X. Li, L. Zhao, X. Yang, T. Liu and W. Yang, "Service Pricing and Selection for IoT Applications Offloading in the Multi-Mobile Edge Computing Systems," in IEEE Access, vol. 8, pp. 153862-153871, 2020.

J. Li et al., "A Secured Framework for SDN-Based Edge Computing in IoT-Enabled Healthcare System," in IEEE Access, vol. 8, pp. 135479-135490, 2020.

C. S. M. Babou et al., "Hierarchical Load Balancing and Clustering Technique for Home Edge Computing," in IEEE Access, vol. 8, pp. 127593-127607, 2020.

S. Durkovic and Z. Čiča, "Multicast Load-Balanced Birkhoff-Von Neumann Switch With Greedy Scheduling," in IEEE Access, vol. 8, pp. 120654-120667, 2020.

M. M. Shahriar Maswood, M. R. Rahman, A. G. Alharbi and D. Medhi, "A Novel Strategy to Achieve Bandwidth Cost Reduction and Load Balancing in a Cooperative Three-Layer Fog-Cloud Computing Environment," in IEEE Access, vol. 8, pp. 113737-113750, 2020.

Y. Chen, Y. Sun, T. Feng and S. Li, "A Collaborative Service Deployment and Application Assignment Method for Regional Edge Computing Enabled IoT," in IEEE Access, vol. 8, pp. 112659-112673, 2020.

J. Wang, H. Zhang, Z. Ruan, T. Wang and X. Wang, "A Machine Learning Based Connectivity Restoration Strategy for Industrial IoTs," in IEEE Access, vol. 8, pp. 71136-71145, 2020.

https://www.kaggle.com/caesarlupum/iot-sensordata

Kumar, L. R. ., Ashokkumar, C. ., Pandey, P. S. ., Kannaiah, S. K. ., J, B. ., & Hussan, M. I. T. . (2023). Security Enhancement in Surveillance Cloud Using Machine Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 46–55. https://doi.org/10.17762/ijritcc.v11i3s.6154

Robert Roberts, Daniel Taylor, Juan Herrera, Juan Castro, Mette Christensen. Leveraging Machine Learning for Educational Data Mining. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/176

Dhabliya, D., Parvez, A. Protocol and its benefits for secure shell (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 19-23.

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Published

21.09.2023

How to Cite

Puppala, T. ., & Polala, N. . (2023). Shared Cache Optimized Adaptive Load Balancing Strategy for IoT Devices . International Journal of Intelligent Systems and Applications in Engineering, 11(4), 402–412. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3537

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Section

Research Article