Reliability Aware Multiple Path Installation in Software-Defined Networking

Link failures frequently occur in communication networks, which negatively impacts network services delivery. Compared to traditional distributed networks, Software-Defined Networking (SDN) provides numerous benefits for link robustness to avoid services unavailability. To cope with link failures, the existing SDN approaches compute multiple paths and install corresponding flow rules at network switches without considering the reliability value of the primary computed path. This increases computation time, traffic overhead and end-to-end packets delay. This paper proposes a new approach called Reliability Aware Multiple Path Flow Rule (RAF) that calculates links reliability and installs minimum flow rules for multiple paths based on the reliability value of the primary path. RAF has been simulated, evaluated and compared with the existing approaches. The simulation results show that RAF performs better than the existing approaches in terms of computation overhead at the controller and reduces end-to-end packet delay and traffic overhead for flow rules installation.

Assessment of Gradient Descent Trained Rule-Fact Network Expert System Multi-Path Training Technique Performance

The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.

Multiple-to-multiple path analysis model

One-to-multiple path analysis model describes the regulation mechanism of multiple independent variables to one dependent variable by dividing the correlation coefficient and the determination coefficient. How to analyse more complex regulation mechanisms of multiple independent variables to multiple dependent variables? Similarly, according to multiple-to-multiple linear regression analysis, multiple-to-multiple path analysis model was proposed in this paper and it demonstrated more complex regulation mechanisms among multiple independent variables and multiple dependent variables by dividing the generalized determination coefficient. Differently, three other types of paths were generated in multiple-to-multiple path analysis model in that the correlation among multiple dependent variables was considered. Then, the decision coefficient of each independent variable was constructed for dependent variables system, and its hypothesis testing statistics were given. Finally, the research example of the wheat breeding rules in arid area demonstrated that the multiple-to-multiple path analysis considering more correlation information can get better results.