Network Traffic Control Design and Evaluation

Recently, the term bufferbloat has been coined to indicate the uncontrolled growth of the network queueing time. A number of network traffic control strategies have been proposed to control network queueing delay. Active Queue Management (AQM) algorithms such as RED, CoDel and PIE have been proposed to drop packets before the network queues become full and to notify upper layers, e.g., transport protocols, about possible congestion status. Innovative packet schedulers such as FQ-CoDel, have been introduced to prioritize flows which do not build queues. Strategies to reduce device buffering, e.g., BQL, have been proposed to increase the effectiveness of packet schedulers. Network experimentation through simulators such as ns-3, one of the most used network simulators, allows the study of bufferbloat and to evaluate solutions in a controlled environment. In this work, we aligned the ns-3 queueing system to the Linux one, one of the most used networking stacks. We introduced in ns-3 a traffic control module modelled after the Linux one. Our design allowed the introduction in ns-3 of schedulers such as FQ-CoDel and of algorithms to dynamically size the buffers such as BQL. Also, we devised a new emulation methodology to overcome some limitations and increase the emulation fidelity. Then, by using the new emulation methodology, we validated the traffic control module with its AQM algorithms (RED, CoDel, FQ-CoDel and PIE). Our experiments prove the high fidelity of network emulation and the high accuracy of the traffic control module and AQM algorithms. Then, we show two proposals of design and evaluation of traffic control strategies by using ns-3. Firstly, we designed and evaluated a traffic control layer for the backlog management in 3GPP stacks. The approach improves significantly the flows performance in LTE networks. Secondly, we highlighted possible design flaws in rate based AQM algorithms and proposed an alternative flow control approach. The approach allows the improvement of the effectiveness of AQM algorithms. Our work will allow researchers to design and evaluate in a more accurate manner traffic control strategies through ns-3 based simulation and emulation and to evaluate the accuracy of other modules implemented in ns-3

Reducing User Perceived Latency in Smart Phones Exploiting IP Network Diversity

The Fifth Generation (5G) wireless networks set its standard to provide very high data rates, Ultra-Reliable Low Latency Communications (URLLC), and significantly improved Quality of Service (QoS). 5G networks and beyond will power up billions of connected devices as it expands wireless services to edge computing and the Internet of Things (IoT). The Internet protocol suite continues its evolution from IPv4 addresses to IPv6 addresses by increasing the adoption rate and prioritizing IPv6. Hence, Internet Service Providers (ISP's) are using the address transition method called dual-stack to prioritize the IPv6 while supporting the existing IPv4. But this causes more connectivity overhead in dual-stack as compared to the single-stack network due to its preference schema towards the IPv6. The dual-stack network increases the Domain Name System (DNS) resolution and Transmission Control Protocol (TCP) connection time that results in higher page loading time, thereby significantly impacting the user experience. Hence, we propose a novel connectivity mechanism, called NexGen Connectivity Optimizer (NexGenCO), which redesigns the DNS resolution and TCP connection phases to reduce the user-perceived latency in the dual-stack network for mobile devices. Our solution utilizes the IP network diversity to improve connectivity through concurrency and intelligent caching. NexGenCO is successfully implemented in Samsung flagship devices with Android Pie and further evaluated using both simulated and live-air networks. It significantly reduces connectivity overhead and improves page loading time up to 18%

Traffic differentiation and multiqueue networking in ns-3

The Linux networking subsystem provides fundamental abstraction to send and receive packets or to perform other operations. At socket layer, the user can set the socket priority used by the networking stack to prioritise the packets. The kernel sends a high priority packet before a low priority packet but the exact behaviour depends on the traffic control layer. A priority based queueing discipline uses that value of priority to enqueue the packets while a multiqueue aware queueing discipline uses a priority mapping defined by the device to enqueue the packets in its queues. The enqueue event triggers a number of consecutive dequeues based on the implemented device flow control mechanism. In case of a WiFi device, an additional layer, named the Soft MAC layer, sits in between the networking API and the hard device MAC. This layer defines the priority mapping and the device driver uses the API provided by that layer to notify the kernel about the status of their queues. In this paper, we present the introduction of the socket priority and of the multiqueue networking infrastructure in ns-3 and the design of the new flow control infrastructure. Finally, we report a preliminary evaluation of our work, consisting of a number of tests that highlight the new behaviour introduced by our models

Design and Implementation of the Traffic Control Module in ns-3

The Linux networking subsystem relies on the Traffic Control infrastructure to process both the incoming and the outgoing packets. One of the most important components of the Traffic Control is the queueing discipline, whose role is to store packets waiting for transmission and select the next packet to pass to the network interface. The Linux Traffic Control enables to perform scheduling, shaping of the egress traffic, policing of the ingress traffic, and dropping of both ingress and egress traffic. In this paper, we present the design and implementation of the Traffic Control layer as an additional module in ns-3. This layer sits in between the netdevices and the network layer. We also present the design and implementation of the base class introduced to model a queueing discipline. Finally, we report a preliminary validation of our work, consisting in a number of tests that properly compare the new stack to the previous one

An analysis of the impact of network device buffers on packet schedulers through experiments and simulations

Keeping the delay experienced by packets while travelling from a source to a destination below certain thresholds is essential to successfully deliver a number of Internet services nowadays. Most of the packet delay can be usually ascribed to the time spent in the many queues encountered by the packet. In this context, the term bufferbloat has been recently coined to denote the uncontrolled growth of the queuing time due, among others, to the excessive size of the buffers and the attitude of TCP to increase the sending rate until a packet is dropped. In this paper, we focus on the queues employed by the traffic control infrastructure and by the network device drivers. Reducing the queuing time due to the former is the objective of a plethora of scheduling algorithms developed in the past years and referred to as Active Queue Management (AQM) algorithms. Conversely, the impact of the additional queuing in the buffer of the network device driver on performance and on the effectiveness of AQM algorithms has instead received much less attention. In this paper, we report the results of an experimental analysis we conducted to gain a better insight into the impact that network device buffers (and their size) have on performance. We also give an in-depth presentation of Dynamic Queue Limits (DQL), an algorithm recently introduced in the Linux kernel to dynamically adapt the size of the buffers held by network device drivers. The experiments we conducted show that DQL not only enables to reduce the queuing time in the network device buffers, which is essential to ensure the effectiveness of AQM algorithms, but also enables to keep latency stable, which is important to reduce the jitter. In order to faithfully reproduce through simulations the dynamics revealed by the experimental study we conducted, we implemented DQL for the popular ns-3 network simulator. In this paper, we describe the design of such implementation and report the results of a simulation study we conducted to show the ability of the simulator to accurately reproduce the experimental results