Conventional WebRTC media flows rely on Transport-Wide Congestion Control (TWCC) to handle delay- and loss-based signals for video bit rate adaptation. Emerging Low Latency Low Loss, and Scalable Throughput (L4S) technology enables applications to exploit Explicit Congestion Notification (ECN) signals and scalable congestion control to achieve sub-millisecond queuing latency. Specifically, for WebRTC media flows, this requires client-side support for IETF RFC 8888 ECN feedback and L4S-compliant congestion control at the server.

Current networking solutions are unable to satisfy the low-latency requirements of real-time volumetric video conferencing when faced with heavy congestion scenarios. Traditional congestion controllers use packet loss or the change in round-trip time (RTT) to estimate the bandwidth. Commonly, this method is too slow as congestion has already occurred and the receiving user has already experienced a latency spike. Low latency, low loss and scalable throughput (L4S), recently published as RFC 9330, wants to alleviate this problem by aiming for sub 1 ms queuing delay for low-latency traffic by using accurate explicit congestion notification (AccECN) packet marking to notify applications of early congestion. We propose an L4S-based pipeline for volumetric video delivery, which achieves a more consistent latency under congestion compared to web real-time communication (WebRTC). In addition, L4S bandwidth estimation achieves a 45% faster convergence compared to Google congestion control (GCC) estimation, commonly used in WebRTC. Furthermore, in our detailed evaluation setup the L4S application experiences no packet loss, while the WebRTC-based version suffers from irrecoverable packet loss, resulting in 3% of frames being undecodable.

Nowadays, many services are offered via the cloud, i.e., they rely on interacting software components that can run on a set of connected Commercial Off-The-Shelf (COTS) servers sitting in data centers. As the demand for any particular service evolves over time, the computational resources associated with the service must be scaled accordingly while keeping the Key Performance Indicators (KPIs) associated with the service under control. Consequently, scaling always involves a delicate trade-off between using the cloud resources and complying with the KPIs. In this paper, we show that a (workload-dependent) Pareto front embodies this trade-off’s limits. We identify this Pareto front for various workloads and assess the ability of several scaling algorithms to approach that Pareto front.

The excess buffering of packets in network elements, also referred to as bufferbloat, results in high latency. Considering the requirements of traffic generated by video conferencing systems like Zoom, cloud rendered gaming platforms like Google Stadia, or even video streaming services such as Netflix, Amazon Prime and YouTube, timeliness of such traffic is important. Ensuring low latency to IP flows with a high throughput calls for the application of Active Queue Management (AQM) schemes. This introduces yet another problem as the co-existence of scalable and classic congestion controls leads to the starvation of classic TCP flows. Technologies such as Low Latency Low Loss Scalable Throughput (L4S) and the corresponding dual queue coupled AQM, DualPI2, provide a robust solution to these problems. However, their deployment on hardware targets such as programmable switches is quite challenging due to the complexity of algorithms and architectural constraints of switching ASICs. In this study, we provide proof of concept implementations of two AQMs that enable the co-existence of scalable and traditional TCP traffic, namely DualPI2 and the preceding single-queue PI2 AQM, on an Intel Tofino switching ASIC. Given the fixed operation of the switch’s traffic manager, we investigate to what extent it is possible to implement a fully RFC-compliant version of the two AQMs on the Tofino ASIC. The study shows that an appropriate split between control and data plane operations is required while we also exploit fixed functionality of the traffic manager to support such solutions.

Network slicing plays a key role in the 5G ecosystem for vertical industries to introduce new services. However, one widely-recognized challenge of network slicing is to provide traffic isolation and concurrently satisfy diverse performance requirements, e.g., bandwidth and latency. In this work, we showcase the capability to retain these two goals at the same time, via extending the 5Growth baseline architecture and designing a new data-plane pipeline, i.e., virtual queue, over the P4 switch. To demonstrate the effectiveness of our approach, a proof-of-concept is presented serving different service requests over a mixed data path, including P4 switches and Open vSwitches (OvSs).

The advent of programmable network switch ASICs and recent developments on other programmable data planes (NPUs, FPGAs) drive the renewed interest in network data plane programmability. The P4 language has emerged as a strong candidate to describe a protocol independent datapath pipeline. With its supported architectures, the P4 language provides an excellent way to define the packet processing and forwarding behavior, while leaving other networking components such as the traffic management engine, to non-programmable fixed function elements, based on the capabilities of most programmable devices. However, network flexibility is essential to meet the Quality of Service (QoS) requirements of traffic flows. Thus, enabling programmable control for fixed-function elements like traffic management is crucial. Towards that end we propose the use of virtual queues in the P4 pipeline, investigate the application of virtual queue-based traffic management, and portability of the approach using different P4 programmable targets. Specifically, we focus on virtual queue based Active Queue Management (AQM) for congestion policing and meeting the latency targets of distinct network slices. The solution is compared to P4 built-in functionality for bandwidth management using meters, proving also that the additional dimensions of control are achieved without compromising the processing complexity of the solution.

The current local area networks (LANs) are occupied by a large variety of heterogeneous consumer devices, equipped with the ability to connect to the Internet using a variety of different network technologies (e.g., Ethernet, 2.4 and 5GHz Wi-Fi). Nevertheless, devices generally opt to statically connect using a single technology, based on predefined priorities. This static behaviour does not allow the network to unlock its full potential, which becomes increasingly more important as the requirements of services, in terms of latency and throughput, grow. In this paper we present a real-life SDN-based implementation of our previously proposed algorithm that addresses this problem.

Video streaming services have continuously gained popularity over the last decades, accounting for about 70% of all consumer Internet traffic in 2016. All of these video streaming sessions have strict delivery deadlines in order to avoid playout interruptions, detrimentally impacting the Quality of Experience (QoE). However, the vast majority of this traffic uses TCP at the transport layer, which is known to be far from minimizing the number of deadline-missing streams. By introducing deadline-awareness at the transport layer, video delivery can be optimized by prioritizing specific flows. This paper proposes a deadline-aware congestion control mechanism, based on a parametrization of the traditional TCP New Reno congestion control strategy. By taking into account the available deadline information, the modulation of the congestion window is dynamically adapted to steer the aggressiveness of a considered stream. The proposed approach has been thoroughly evaluated in both a video-on-demand (VoD)-only scenario and a scenario where VoD streams co-exist with live streaming sessions and non-deadline-aware traffic. It was shown that in a video streaming scenario the minimal bottleneck bandwidth can be reduced by 16% on average when using deadline-aware congestion control. In coexistence with other TCP traffic, a bottleneck reduction of 11% could be achieved.

This paper concerns the use of Active Queue Management (AQM) to reduce queuing delay. It offers insight into why it has proved hard for a Proportional Integral (PI) controller to remain both responsive and stable while controlling `Classic' TCP flows, such as TCP Reno and Cubic. Due to their non-linearity, the controller's adjustments have to be smaller when the target drop probability is lower. The PI Enhanced (PIE) algorithm attempts to solve this problem by scaling down the adjustments of the controller using a look-up table. Instead, we control an internal variable that is by definition linearly proportional to the load, then post-process it into the required Classic drop probability---in fact we show that the output simply needs to be squared. This allows tighter control, giving responsiveness and stability better or no worse than PIE achieves, but without all its corrective heuristics.

This demo dramatically illustrates how replacing 'Classic' TCP congestion control (Reno, Cubic, etc.) with a 'Scalable' alternative like Data Centre TCP (DCTCP) keeps queuing delay ultra-low; not just for a select few light applications like voice or gaming, but even when a variety of interactive applications all heavily load the same (emulated) Internet access. DCTCP has so far been confined to data centres because it is too aggressive---it starves Classic TCP flows. To allow DCTCP to be exploited on the public Internet, we developed DualQ Coupled Active Queue Management (AQM), which allows the two TCP types to safely co-exist. Visitors can test all these claims. As well as running Web-based apps, they can pan and zoom a panoramic video of a football stadium on a touch-screen, and experience how their personalized HD scene seems to stick to their finger, even though it is encoded on the fly on servers accessed via an emulated delay, representing 'the cloud'. A pair of VR goggles can be used at the same time, making a similar point. The demo provides a dashboard so that visitors can not only experience the interactivity of each application live, but they can also quantify it via a wide range of performance stats, updated live. It also includes controls so visitors can configure different TCP variants, AQMs, network parameters and background loads and immediately test the effect.

In this paper, we investigate the proxy video caching problem and propose a specific caching replacement strategy for segmented video streaming. The proposed caching algorithm makes use of the natural linear time structure of video streaming, which fits the philosophy of the optimal MIN algorithm. By observing the watching positions of active viewers at a certain point of time, the replacement approach computes the exact reuse time or, if not available, the predicted reuse time for stored video segments, and discards the segment that is supposedly to be used the furthest in time. Evaluations performed on a trace of a real Video-On-Demand (VOD) service show that the proposed caching algorithm substantially improves the hit ratio, especially for small caches.

HTTP Adaptive Streaming (HAS) is rapidly becoming a key video delivery technology for fixed and mobile networks. However, today there is no solution that allows network operators or CDN providers to perform network-based QoE monitoring for HAS sessions. We present a HAS QoE monitoring system, based on data collected in the network, without monitoring information from the client. To retrieve the major QoE parameters such as average quality, quality variation, rebuffering events and interactivity delay, we propose a technique called session reconstruction. We define a number of iterative steps and developed algorithms that can be used to perform HAS session reconstruction. Finally, we present the results of a working prototype for the reconstruction and monitoring of Microsoft Smooth Streaming HAS sessions that is capable of dealing with intermediate caching and user interactivity. We describe the main observations when using the platform to analyze more than a hundred HAS sessions.

The consumption of multimedia services over HTTP-based delivery mechanisms has recently gained popularity due to their increased flexibility and reliability. Traditional broadcast TV channels are now offered over the Internet, in order to support Live TV for a broad range of consumer devices. Moreover, service providers can greatly benefit from offering external live content (e.g., YouTube, Hulu) in a managed way. Recently, HTTP Adaptive Streaming (HAS) techniques have been proposed in which video clients dynamically adapt their requested video quality level based on the current network and device state. Unlike linear TV, traditional HTTP- and HAS-based video streaming services depend on unicast sessions, leading to a network traffic load proportional to the number of multimedia consumers. In this paper we propose a novel HAS-based video delivery architecture, which features intelligent multicasting and caching in order to decrease the required bandwidth considerably in a Live TV scenario. Furthermore we discuss the autonomic selection of multicasted content to support Video on Demand (VoD) sessions. Experiments were conducted on a large scale and realistic emulation environment and compared with a traditional HAS-based media delivery setup using only unicast connections.

In recent years, the networking community has put a significant research effort in identifying new ways to distribute content to multiple users in a better-than-unicast manner. Scalable delivery is more important now video is the dominant traffic type and further growth is expected. To make content distribution scalable, in-network optimization functions are needed such as caches. The established transport layer protocols are end-to-end and do not allow optimizing transport below the application layer, hence the popularity of overlay application layer solutions located in the network. In this paper, we introduce a novel transport protocol, the Shared Content Addressing Protocol (SCAP) that allows in-network intermediate elements to participate in optimizing the delivery process, using only the transport layer. SCAP runs on top of standard IP networks, and SCAP optimization functions can be plugged-in the network transparently as needed. As such, only transport protocol based intermediate functions need to be deployed in the network, and the applications can stay at the topological end points. We define and evaluate a prototype version of the SCAP protocol using both simulation and a prototype implementation of a transparent SCAP-only intermediate optimization function.

The popularity of streaming multimedia services has greatly increased in recent years. Telco- and cable-providers have started offering a plethora of multimedia services in the access and aggregation network, including video on demand, interactive digital television, and time-shifted TV. However, these services introduce additional challenges, such as stringent time constraints, and high bandwidth requirements. To overcome these problems, we explore the advantages of delivering such multimedia content using deadline-aware scheduling and caching algorithms. These algorithms decide when to send and store which content. This enables the network to optimize bandwidth consumption and satisfy deadline constraints. The designed algorithm was evaluated and compared to classical deadline-unaware delivery protocols. This allows us to study the efficiency of the new algorithm, and identify the scenarios in which deadline-aware scheduling improves delivery of multimedia content.

DSL aggregation networks are evolving to the standard platform for the delivery of multimedia services such as television and network based personal video recording. These multimedia services introduce large challenges for network operators as they are sensitive to packet loss. Therefore, admission control mechanisms are required to avoid congestion caused by allowing too many sessions. However, as multimedia services are often bursty it is not possible to reserve a fixed amount of bandwidth in the network since this policy will lead to either over-admittance or under-admittance. Recently, the IETF Pre-Congestion Notification (PCN) Working Group, proposed a measurement based admission control mechanism, where the network load is measured at each node and sessions are allowed or blocked at the edge of the network. In this paper, we extend and evaluate the PCN mechanism: we propose a new measurement algorithm for PCN, based on bandwidth metering, and determine the configuration guidelines for the parameters of both the original token bucket based approach and the novel algorithm for different network conditions and traffic types. More specifically, we study PCN's applicability on protecting VBR video services, which is currently not studied in the PCN Working Group. Furthermore, we characterise the gain of PCN in comparison to a centralised admission control mechanism.