Hardware Acceleration of the Robust Header Compression (RoHC) Algorithm

With the proliferation of Long Term Evolution (LTE) networks, many cellular carriers are embracing the emerging eld of mobile Voice over Internet Protocol (VoIP). The robust header compression (RoHC) framework was introduced as a part of the LTE Layer 2 stack to compress the large headers of the VoIP packets before transmitted over LTE IP-based architectures. The headers, which are encapsulated Real-time Transport Protocol (RTP)/User Datagram Protocol (UDP)/Internet Protocol (IP) stack, are large compared to the small payload. This header-compression scheme is especially useful for ecient utilization of the radio bandwidth and network resources. In an LTE base-station implementation, RoHC is a processing-intensive algorithm that may be the bottleneck of the system, and thus, may be the limiting factor when it comes to number of users served. In this thesis, a hardware-software and a full-hardware solution are proposed, targeting LTE base-stations to accelerate this computationally intensive algorithm and enhance the throughput and the capacity of the system. The results of both solutions are discussed and compared with respect to design metrics like throughput, capacity, power consumption, chip area and exibility. This comparison is instrumental in taking architectural level trade-o decisions in-order to meet the present day requirements and also be ready to support future evolution. In terms of throughput, a gain of 20% (6250 packets/sec can be processed at a frequency of 150 MHz) is achieved in the HW-SW solution compared to the SW-Only solution by implementing the Cyclic Redundancy Check (CRC) and the Least Signicant Bit(LSB) encoding blocks as hardware accelerators . Whereas, a Full-HW implementation leads to a throughput of 45 times (244000 packets/sec can be processed at a frequency of 100 MHz) the throughput of the SW-Only solution. However, the full-HW solution consumes more Lookup Tables (LUTs) when it is synthesized on an Field-Programmable Gate Array (FPGA) platform compared to the HW-SW solution. In Arria II GX, the HW-SW and the full-HW solutions use 2578 and 7477 LUTs and consume 1.5 and 0.9 Watts, respectively. Finally, both solutions are synthesized and veried on Altera's Arria II GX FPGA

Reducing the acknowledgement frequency in IETF QUIC

Research Funding European Space Agency University of AberdeenPeer reviewedPublisher PD

A novel network architecture for train-to-wayside communication with quality of service over heterogeneous wireless networks

In the railway industry, there are nowadays different actors who would like to send or receive data from the wayside to an onboard device or vice versa. These actors are e.g., the Train Operation Company, the Train Constructing Company, a Content Provider, etc. This requires a communication module on each train and at the wayside. These modules interact with each other over heterogeneous wireless links. This system is referred to as the Train-to-Wayside Communication System (TWCS). While there are already a lot of deployments using a TWCS, the implementation of quality of service, performance enhancing proxies (PEP) and the network mobility functions have not yet been fully integrated in TWCS systems. Therefore, we propose a novel and modular IPv6-enabled TWCS architecture in this article. It jointly tackles these functions and considers their mutual dependencies and relationships. DiffServ is used to differentiate between service classes and priorities. Virtual local area networks are used to differentiate between different service level agreements. In the PEP, we propose to use a distributed TCP accelerator to optimize bandwidth usage. Concerning network mobility, we propose to use the SCTP protocol (with Dynamic Address Reconfiguration and PR-SCTP extensions) to create a tunnel per wireless link, in order to support the reliable transmission of data between the accelerators. We have analyzed different design choices, pinpointed the main implementation challenges and identified candidate solutions for the different modules in the TWCS system. As such, we present an elaborated framework that can be used for prototyping a fully featured TWCS

Network congestion control at the application layer

Application-layer protocols play a special role in network programming. Typical programmers are more familiar with them and more likely to implement them. Well-designed application-layer proto-cols follow many patterns that improve the performance of applications using these protocols. We present a subset of these patterns that focuses on the congestion control at the application layer

A Review of the In-Network Computing and Its Role in the Edge-Cloud Continuum

Future networks are anticipated to enable exciting applications and industrial services ranging from Multisensory Extended Reality to Holographic and Haptic communication. These services are accompanied by high bandwidth requirements and/or require low latency and low reliability, which leads to the need for scarce and expensive resources. Cloud and edge computing offer different functionalities to these applications that require communication, computing, and caching (3C) resources working collectively. Hence, a paradigm shift is necessary to enable the joint management of the 3Cs in the edge-cloud continuum. We argue that In-Network Computing (INC) is the missing element that completes the edge-cloud continuum. This paper provides a detailed analysis of the driving use-cases, explores the synergy between INC and 3C, and emphasizes the crucial role of INC. A discussion on the opportunities and challenges posed by INC is held from various perspectives, including hardware implementation, architectural design, and regulatory and commercial aspects

Reducing Internet Latency : A Survey of Techniques and their Merit

Bob Briscoe, Anna Brunstrom, Andreas Petlund, David Hayes, David Ros, Ing-Jyh Tsang, Stein Gjessing, Gorry Fairhurst, Carsten Griwodz, Michael WelzlPeer reviewedPreprin

Accelerating Audio Data Analysis with In-Network Computing

Digital transformation will experience massive connections and massive data handling. This will imply a growing demand for computing in communication networks due to network softwarization. Moreover, digital transformation will host very sensitive verticals, requiring high end-to-end reliability and low latency. Accordingly, the emerging concept “in-network computing” has been arising. This means integrating the network communications with computing and also performing computations on the transport path of the network. This can be used to deliver actionable information directly to end users instead of raw data. However, this change of paradigm to in-network computing raises disruptive challenges to the current communication networks. In-network computing (i) expects the network to host general-purpose softwarized network functions and (ii) encourages the packet payload to be modified. Yet, today’s networks are designed to focus on packet forwarding functions, and packet payloads should not be touched in the forwarding path, under the current end-to-end transport mechanisms. This dissertation presents fullstack in-network computing solutions, jointly designed from network and computing perspectives to accelerate data analysis applications, specifically for acoustic data analysis. In the computing domain, two design paradigms of computational logic, namely progressive computing and traffic filtering, are proposed in this dissertation for data reconstruction and feature extraction tasks. Two widely used practical use cases, Blind Source Separation (BSS) and anomaly detection, are selected to demonstrate the design of computing modules for data reconstruction and feature extraction tasks in the in-network computing scheme, respectively. Following these two design paradigms of progressive computing and traffic filtering, this dissertation designs two computing modules: progressive ICA (pICA) and You only hear once (Yoho) for BSS and anomaly detection, respectively. These lightweight computing modules can cooperatively perform computational tasks along the forwarding path. In this way, computational virtual functions can be introduced into the network, addressing the first challenge mentioned above, namely that the network should be able to host general-purpose softwarized network functions. In this dissertation, quantitative simulations have shown that the computing time of pICA and Yoho in in-network computing scenarios is significantly reduced, since pICA and Yoho are performed, simultaneously with the data forwarding. At the same time, pICA guarantees the same computing accuracy, and Yoho’s computing accuracy is improved. Furthermore, this dissertation proposes a stateful transport module in the network domain to support in-network computing under the end-to-end transport architecture. The stateful transport module extends the IP packet header, so that network packets carry message-related metadata (message-based packaging). Additionally, the forwarding layer of the network device is optimized to be able to process the packet payload based on the computational state (state-based transport component). The second challenge posed by in-network computing has been tackled by supporting the modification of packet payloads. The two computational modules mentioned above and the stateful transport module form the designed in-network computing solutions. By merging pICA and Yoho with the stateful transport module, respectively, two emulation systems, i.e., in-network pICA and in-network Yoho, have been implemented in the Communication Networks Emulator (ComNetsEmu). Through quantitative emulations, the experimental results showed that in-network pICA accelerates the overall service time of BSS by up to 32.18%. On the other hand, using in-network Yoho accelerates the overall service time of anomaly detection by a maximum of 30.51%. These are promising results for the design and actual realization of future communication networks