Limiting Factors in Widespread Adoption of Active Queue Management in the Philippines’ Consumer Electronics Space

The premise that Active Queue Management (AQM) is effective in both quantitative and qualitative settings in residential and enterprise networks has repeatedly been established in multiple papers from academic journals along with private studies in addressing bufferbloat, characterized as excessive latency because of heavy network utilization. However, the presence and understanding of bufferbloat mitigation is absent and not well-known in the Philippine Internet of Things space except enthusiasts, willing to take the time to examine the concept along with its benefits. Hence, this paper examines possible reasons as to why AQM is not widely adopted by Philippine consumers and industries in increasing productivity considering the COVID-19 Pandemic: a lack of basic understanding of bufferbloat and its implications, the complexity of the concept, the know-how required to execute its implementation being far too high, and the lack of perceived benefit by existing telecommunications players in the country.

Supervised Learning of Neural Networks for Active Queue Management in the Internet

The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router’s queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM PIα mechanism. We create training samples taking into account the self-similarity of network traffic. The model uses fractional Gaussian noise as a source. The quantitative analysis is based on simulation. During the tests, we analyzed the length of the queue, the number of rejected packets and waiting times in the queues. The proposed mechanism shows the usefulness of the Active Queue Management mechanism based on Neural Networks.

Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

The congestion problem at the router buffer leads to serious consequences on network performance. Active Queue Management (AQM) has been developed to react to any possible congestion at the router buffer at an early stage. The limitation of the existing fuzzy-based AQM is the utilization of indicators that do not address all the performance criteria and quality of services required. In this paper, a new method for active queue management is proposed based on using the fuzzy logic and multiple performance indicators that are extracted from the network performance metrics. These indicators are queue length, delta queue and expected loss. The simulation of the proposed method show that in high traffic load, the proposed method preserves packet loss, drop packet only when it is necessary and produce a satisfactory delay that outperformed the state-of-the-art AQM methods.