Abstract
The rapid change in computational strategy and delay-sensitive applications require intense power sources of computational resources. This creates a challenge of precise latency requirements in 5G network services. Task scheduling and offloading can be promising solutions to achieve high-performance optimized output with heterogeneity, handling of tasks, conservation of energy, and reliable latency factor. Blockchain (BC), Software defined networks (SDN) and the Internet of Things(IoT) are the most promising significant technologies researched in this article, and the fusion of the three has the potential to reinvent the relationship of trust in the networks and promote the integration of confidentiality and reliability in the respective use cases. Cloud infrastructure is used to provide clients with powerful computing and storage environments. A kind of expansion of cloud computing architecture, edge computing, has been trending. Now, it is used to build distributed secure architecture to promote the safety and integrity of data throughout its lifetime and bring much-needed efficiency to IoT data processing. This paper considers a Blockchain-Enabled Software-defined network-based IoT Edge Cloud(BESIEC) scenario for data integrity during task scheduling and offloading processes while achieving optimal computational resources and minimizing end-to-end delays. The strategy shows that it is better to implement the BESIEC model than the Traditional Floodlight implementation. The BESIEC model shows better time consumption performance regarding the number of tasks completed compared to local processing, cloud offloading, and edge offloading.
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References:
Mirzapour-Moshizi M, Sattari-Naeini V, Molahosseini AS. Application placement in fog-cum-cloud environment based on a low latency policy-making framework. Clust Comput. 2024;27(1):199–217.
Magotra B, Malhotra D, Dogra AK. Adaptive computational solutions to energy efficiency in cloud computing environment using VM consolidation. Arch Comput Methods Eng. 2023;30(3):1789–818.
Yuan H, Bi J, Wang Z, Yang J, Zhang J. Partial and cost-minimized computation offloading in hybrid edge and cloud systems. Expert Syst Appl. 2024;250: 123896.
Jeyaraj R, Balasubramaniam A. Resource management in cloud and cloud-influenced technologies for internet of things applications. ACM Comput Surv. 2023;55(12):1–37.
Kanellopoulos D, Sharma VK. Dynamic load balancing techniques in the IoT: a review. Symmetry. 2022;14(12):2554.
Lilhore UK, Imoize AL, Li CT, Simaiya S, Pani SK, Goyal N, Kumar A, Lee CC. Design and implementation of an ML and IoT based adaptive traffic-management system for smart cities. Sensors. 2022;22(8):2908.
Hazarika A, Choudhury N, Nasralla MM, Khattak SBA, Rehman IU. Edge ML technique for smart traffic management in intelligent transportation system. Access. 2024. https://doi.org/10.1109/ACCESS.2024.3365930.
Jarwan A, Ibnkahla M. Edge-based federated deep reinforcement learning for IoT traffic management. IEEE Internet Things J. 2022;10(5):3799–813.
Ahmed A, Abdullah S, Iftikhar S, Ahmad I, Ajmal S, Hussain Q. A novel blockchain based secured and QoS aware IoT vehicular network in edge cloud computing. IEEE Access. 2022;10:77707–22.
Karakus M. GATE-BC: genetic algorithm-powered QoS-aware cross-network traffic engineering in blockchain-enabled SDN. Access. 2024. https://doi.org/10.1109/ACCESS.2024.3374213.
Rahman A, Khan MSI, Montieri A, Islam MJ, Karim MR, Hasan M, Kundu D, Nasir MK, Pescapè A. BlockSD-5GNet: Enhancing security of 5G network through blockchain-SDN with ML-based bandwidth prediction. Trans Emerg Telecom Tech. 2024;35(4): e4965.
Sen P, Pandit R, Sarddar D. A survey on methods and apparatus of offloading in mobile cloud computing. Intern J Sys Eng. 2024;14(2):212–25.
Sen P, Islam T, Pandit R, Sarddar D. A comparative review on different techniques of computation offloading in mobile cloud computing. Fog Comp Intel Cloud IoT Sys. 2024. https://doi.org/10.1002/9781394175345.ch2.
Liao Z, Hu W, Huang J, Wang J. Joint multi-user DNN partitioning and task offloading in mobile edge computing. Ad Hoc Netw. 2023;144: 103156.
Chen J, Deng Q, Yang X. Non-cooperative game algorithms for computation offloading in mobile edge computing environments. J Par Distr Comput. 2023;172:18–31.
Sarfaraz A, Chakrabortty RK, Essam DL. Reputation based proof of cooperation: an efficient and scalable consensus algorithm for supply chain applications. J Ambient Intell Humaniz Comput. 2023;14(6):7795–811.
Zhang T, Huang Z. FPoR: Fair proof-of-reputation consensus for blockchain. ICT Express. 2023;9(1):45–50.
Buzzio-García J, Vergara J, Ríos-Guiral S, Garzón C, Gutiérrez S, Botero JF, Quiroz-Arroyo JL, Pérez-Díaz JA. Exploring traffic patterns through network programmability: introducing sdnflow, a comprehensive openflow-based statistics dataset for attack detection. IEEE Access. 2024;12:42163–80.
Zhou H, Zheng Y, Jia X, Shu J. Collaborative prediction and detection of DDoS attacks in edge computing: A deep learning-based approach with distributed SDN. Comput Netw. 2023;225: 109642.
Gawas, M. and Govekar, S., 2023. Energy efficient Qos aware decentralized blockchain framework for fog computing.
Roux R, Olwal TO, Chowdhury DS. Software defined networking architecture for energy transaction in smart microgrid systems. Energies. 2023;16(14):5275.
Huang G, Ullah I, Huang H, Kim KT. Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approach. J Cloud Comp. 2024;13(1):26.
Diro AA, Reda HT, Chilamkurti N. Differential flow space allocation scheme in SDN based fog computing for IoT applications. J Amb Intel Human Comp. 2018. https://doi.org/10.1007/s12652-017-0677-z.
YLiu, T., Wu, J., Chen, L., Wu, Y. and Li, Y.,. Smart contract-based long-term auction for mobile blockchain computation offloading. IEEE Access. 2020;8:36029–42.
Li J, Zhu M, Liu J, Liu W, Huang B, Liu R. Blockchain-based reliable task offloading framework for edge-cloud cooperative workflows in IoMT. Inf Sci. 2024;668: 120530.
Floodlight. Accessed: Jan. 20, 2024. [Online]. https://github.com/floodlight/floodlight
Arcas GI, Cioara T, Anghel I, Lazea D, Hangan A. Edge offloading in smart grid. Smart Cities. 2024;7(1):680–711.
Zhang S, Liu A, Han C, Liang X, Xu X, Wang G. Multiagent reinforcement learning-based orbital edge offloading in SAGIN supporting Internet of Remote Things. IEEE Internet Things J. 2023;10(23):20472–83.
Fang G, Zhou R, Li X, Huang H, Li Z. A cross-edge offloading framework for green MEC systems. IEEE Trans Cons Elect. 2023. https://doi.org/10.1109/TCE.2023.3319816.
Kar B, Yahya W, Lin YD, Ali A. Offloading using traditional optimization and machine learning in federated cloud–edge–fog systems: A survey. IEEE Com Survey Tutor. 2023;25(2):1199–226.
Wang T, Liang Y, Zhang Y, Zheng X, Arif M, Wang J, Jin Q. An intelligent dynamic offloading from cloud to edge for smart iot systems with big data. IEEE Transact Network Sci Eng. 2020;7(4):2598–607.
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Ms Jayashree Mohanty has developed different architecture, done experimental tests, and resultant output, and Dr. Srichandan Sobhanayak has guided in all respects.
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Mohanty, J., Sobhanayak, S. BESIEC: An Adaptive Optimized Model for Task Scheduling & Offloading. SN COMPUT. SCI. 5, 1099 (2024). https://doi.org/10.1007/s42979-024-03461-5
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DOI: https://doi.org/10.1007/s42979-024-03461-5