iRED: A disaggregated P4-AQM fully implemented in programmable data plane hardware

Routers employ queues to temporarily hold packets when the scheduler cannot immediately process them. Congestion occurs when the arrival rate of packets exceeds the processing capacity, leading to increased queueing delay. Over time, Active Queue Management (AQM) strategies have focused on directly draining packets from queues to alleviate congestion and reduce queuing delay. On Programmable Data Plane (PDP) hardware, AQMs traditionally reside in the Egress pipeline due to the availability of queue delay information there. We argue that this approach wastes the router's resources because the dropped packet has already consumed the entire pipeline of the device. In this work, we propose ingress Random Early Detection (iRED), a more efficient approach that addresses the Egress drop problem. iRED is a disaggregated P4-AQM fully implemented in programmable data plane hardware and also supports Low Latency, Low Loss, and Scalable Throughput (L4S) framework, saving device pipeline resources by dropping packets in the Ingress block. To evaluate iRED, we conducted three experiments using a Tofino2 programmable switch: i) An in-depth analysis of state-of-the-art AQMs on PDP hardware, using 12 different network configurations varying in bandwidth, Round-Trip Time (RTT), and Maximum Transmission Unit (MTU). The results demonstrate that iRED can significantly reduce router resource consumption, with up to a 10x reduction in memory usage, 12x fewer processing cycles, and 8x less power consumption for the same traffic load; ii) A performance evaluation regarding the L4S framework. The results prove that iRED achieves fairness in bandwidth usage for different types of traffic (classic and scalable); iii) A comprehensive analysis of the QoS in a real setup of a DASH) technology. iRED demonstrated up to a 2.34x improvement in FPS and a 4.77x increase in the video player buffer fill.Comment: Preprint (TNSM under review

Women Filmmakers in the United Arab Emirates

This entry provides an introduction to the work of and the challenges faced by women filmmakers in the United Arab Emirates while at the same time providing an overview of the emerging film industry in the country. The fact that these women claim the power of representation and start telling their stories against the backdrop of a conservative, patriarchal society, obviously opens up space for gender redefinition. Overall, it seems that their films reveal a preference for strong, interesting women that fight for a meaningful life against the backdrop of a globalized society that has left women weaker and more fragile, with less obvious choices and possibilities for fulfillment. As their female characters claim center stage, both traditional and contemporary constructions of gender are called into question and popular stereotypes about Emirati women are challenged

DESiRED -- Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry

Active Queue Management (AQM) is a mechanism employed to alleviate transient congestion in network device buffers, such as routers and switches. Traditional AQM algorithms use fixed thresholds, like target delay or queue occupancy, to compute random packet drop probabilities. A very small target delay can increase packet losses and reduce link utilization, while a large target delay may increase queueing delays while lowering drop probability. Due to dynamic network traffic characteristics, where traffic fluctuations can lead to significant queue variations, maintaining a fixed threshold AQM may not suit all applications. Consequently, we explore the question: \textit{What is the ideal threshold (target delay) for AQMs?} In this work, we introduce DESiRED (Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precise network feedback from In-band Network Telemetry (INT) to feed a Deep Reinforcement Learning (DRL) model. This model dynamically adjusts the target delay based on rewards that maximize application Quality of Service (QoS). We evaluate DESiRED in a realistic P4-based test environment running an MPEG-DASH service. Our findings demonstrate up to a 90x reduction in video stall and a 42x increase in high-resolution video playback quality when the target delay is adjusted dynamically by DESiRED.Comment: Preprint (Computer Networks under review

牛糞堆肥を施用した畑地における大腸菌の流出抑制に関する研究

2013東京農業大