Nowadays internet has become more popular to each and every one. It is very sensitive to nodes or links failure due to many known or unknown issues in the network connectivity. Routing is the important concept in wired and wireless network for packet transmission. During the packet transmission many times some of the problems occur, due to this packets are being lost or nodes not able to transmit the packets to the specific destination. This paper discusses various issues and approaches related to the routing mechanism. In this paper, we present a review and comparison of different routing algorithms and protocols proposed recently in order to address various issues. The main purpose of this study is to address issues for packet forwarding like network control management, load balancing, congestion control, convergence time and instability. We also focus on the impact of these issues on packet forwarding.
This paper proposes a novel test case prioritization technique, namely Multi- Objective Crow Search and Fruitfly Optimization (MOCSFO) for test case prioritization. The proposed MOCSFO is designed by integrating Crow search algorithm (CSA) and Chaotic Fruitfly optimization algorithm (CFOA). The optimal test cases are selected based on newly modelled fitness function, which consist of two parameters, namely average percentage of combinatorial coverage (APCC) and Normalized average of the percentage of faults detected (NAPFD). The test case to be selected is decided using a searching criterion or fitness based on sequential weighed coverage size. Accordingly, the effective searching criterion is formulated to determine the optimal test cases based on the constraints. The experimentation of the proposed MOCSFO method is performed by considering the performance metrics, like NAPFD, and APCC. The proposed MOCSFO outperformed the existing methods with enhanced NAPFD of 0.7, and APCC of 0.837.
The combinatorial strategy is useful in the reduction of the number of input parameters into a compact set of a system based on the combinations of the parameters. This strategy can be used in testing the behaviour that takes place when the events are allowed to be executed in an appropriate order. Basically, in the software systems, for the highly configurable system, the input configurations are based on the constraints, and the construction of this idea undergoes various kinds of difficulties. The proposed Jaya-Bat optimization algorithm is developed with the combinatorial interaction test cases in an effective manner in the presence of the constraints. The proposed Jaya-Bat based optimization algorithm is the integration of the Jaya optimization algorithm (JOA) and the Bat optimization algorithm (BA). The experimentation is carried out in terms of average size and the average time to prove the effectiveness of the proposed algorithm. From the results, it is clear that the proposed algorithm is capable of selecting the test cases optimally with better performance.
Border Gateway Protocol (BGP), a path vector routing protocol, is a widespread exterior gateway protocol (EGP) in the internet. Extensive deployment of the new technologies in internet, protocols need to have continuous improvements in its behavior and operations. New routing technologies conserve a top level of service availability. Hence, due to topological changes, BGP needs to achieve a fast network convergence. Now a days size of the network growing very rapidly. To maintain the high scalability in the network BGP needs to avoid instability. The instability and failures may cause the network into an unstable state, which significantly increases the network convergence time. This paper summarizes the various approaches like BGP policies, instability, and fault detection etc. to improve the convergence time of BGP.
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