TRAFFIC PREDICTION AND MANAGEMENT IN CLOUD AND EDGE COMPUTING AND DETECTING DDoS ATTACKS USING MACHINE LEARNING

A smart setup for traffic control mixes machine learning with computing at the edges and in the clouds. Instead of guessing, it uses Isolation Forest to spot odd behaviors early on. This method sees ahead, adjusting how work spreads across local devices and distant servers. Close-up processing cuts down delays, helping apps react quickly when timing matters most. Big jobs go to the cloud where power meets demand smoothly. Balancing loads as things change keeps data moving without piling up. Traffic flows better because predictions guide decisions before problems grow. Efficiency rises since resources shift only where needed, nothing more. Bandwidth gets used wisely under shifting conditions. Performance stays strong even when networks get busy. What results is steadier service, fewer holdups, smarter use of what's available. Picture of how things run comes clear using radar and bar displays, making it easier to see how well systems and connections perform. What stands out is that this setup grows when needed, adjusts on its own, stays steady, works across IoT setups, city tech, factory controls, medical monitoring, even next-gen signal pathways.

  Machine Learning S. R. S. Prabaharan and R. Karthiga, “Machine Learning based Traffic Prediction in Cloud Computing,” IEEE International Conference on Intelligent Computing and Control Systems, pp. 1123–1128, 2023.   Edge Computing W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.   X. Sun and N. Ansari, “EdgeIoT: Mobile Edge Computing for the Internet of Things,” IEEE Communications Magazine, vol. 54, no. 12, pp. 22–29, 2016.   M. Satyanarayanan, “The Emergence of Edge Computing,” Computer, vol. 50, no. 1, pp. 30–39, 2017.   H. Gupta, A. Vahid Dastjerdi, S. K. Ghosh, and R. Buyya, “iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments,” Software: Practice and Experience, vol. 47, no. 9, pp. 1275–1296, 2017.   F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog Computing and Its Role in the Internet of Things,” Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16, 2012.   A. Yousefpour et al., “All One Needs to Know about Fog Computing and Related Edge Computing Paradigms,” Journal of Systems Architecture, vol. 98, pp. 289–330, 2019.   L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.   F. T. Liu, K. M. Ting, and Z. H. Zhou, “Isolation Forest,” IEEE International Conference on Data Mining, pp. 413–422, 2008.   F. T. Liu, K. M. Ting, and Z. H. Zhou, “Isolation-Based Anomaly Detection,” ACM Transactions on Knowledge Discovery from Data, vol. 6, no. 1, pp. 1–39, 2012.