The Perspective of Multipath Routing | Chapter 6 | Theory and Practice of Mathematics and Computer Science Vol. 11

 Multipath routing refers to the use of multiple paths rather than a single path to forward traffic through the network from source node to destination node. Multipath routing improves overall network performance by making the best use of available network resources. If multiple paths are used for traffic transmission, traffic will be redirected to the backup path if the active path fails or there is congestion. Furthermore, by distributing traffic, multiple paths can be used concurrently.  Consequently, Multipath routing can be more efficient than single path routing in terms of network utilization and load balancing. It can significantly reduce congestion and increase network throughput, thereby increasing network reliability. The calculation of multiple paths and traffic distribution among multiple paths are the two main concerns when implementing a multipath routing scheme. Various algorithms for effectively calculating the multiple paths are presented in the literature. In this chapter, we discussed some multipath routing approaches that take path construction and path selection into account when distributing the flow.

Author (s) Details

Dr. Gaytri Devi
GVM Institute of Technology and Management, DCRUST, Murthal, India.

Dr. Shuchita Upadhyaya
Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, India.

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Current Research on Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review | Chapter 3 | Recent Studies in Mathematics and Computer Science Vol. 2

Realization of the tremendous features and facilities provided by Cloud Computing by the geniuses in the world of digital marketing increases its demand. As customer satisfaction is the manifest of this ever shining field, balancing its load becomes a major issue. Various heuristic and meta-heuristic algorithms were applied to get optimum solutions. The current era is much attracted with the provisioning of self-manageable, self-learnable, self-healable, and self-configurable smart systems. To get self-manageable Smart Cloud, various Artificial Intelligence and Machine Learning (AI-ML) techniques and algorithms are revived. In this review, recent trend in the utilization of AI-ML techniques, their applied areas, purpose, their merits and demerits are highlighted. These techniques are further categorized as instance-based machine learning algorithms and reinforcement learning techniques based on their ability of learning. Reinforcement learning is preferred when there is no training data set.  It leads the system to learn by its own experience itself even in dynamic environment.

Author(s)  Details

V. Radhamani 
Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, Tamilnadu, India.

G. Dalin
Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, Tamilnadu, India.

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