155 research outputs found
Quality aspects of Internet telephony
Internet telephony has had a tremendous impact on how people communicate.
Many now maintain contact using some form of Internet telephony.
Therefore the motivation for this work has been to address the quality aspects
of real-world Internet telephony for both fixed and wireless telecommunication.
The focus has been on the quality aspects of voice communication,
since poor quality leads often to user dissatisfaction. The scope of the work
has been broad in order to address the main factors within IP-based voice
communication.
The first four chapters of this dissertation constitute the background
material. The first chapter outlines where Internet telephony is deployed
today. It also motivates the topics and techniques used in this research.
The second chapter provides the background on Internet telephony including
signalling, speech coding and voice Internetworking. The third chapter
focuses solely on quality measures for packetised voice systems and finally
the fourth chapter is devoted to the history of voice research.
The appendix of this dissertation constitutes the research contributions.
It includes an examination of the access network, focusing on how calls are
multiplexed in wired and wireless systems. Subsequently in the wireless
case, we consider how to handover calls from 802.11 networks to the cellular
infrastructure. We then consider the Internet backbone where most of our
work is devoted to measurements specifically for Internet telephony. The
applications of these measurements have been estimating telephony arrival
processes, measuring call quality, and quantifying the trend in Internet telephony
quality over several years. We also consider the end systems, since
they are responsible for reconstructing a voice stream given loss and delay
constraints. Finally we estimate voice quality using the ITU proposal PESQ
and the packet loss process.
The main contribution of this work is a systematic examination of Internet
telephony. We describe several methods to enable adaptable solutions
for maintaining consistent voice quality. We have also found that relatively
small technical changes can lead to substantial user quality improvements.
A second contribution of this work is a suite of software tools designed to
ascertain voice quality in IP networks. Some of these tools are in use within
commercial systems today
Securing media streams in an Asterisk-based environment and evaluating the resulting performance cost
When adding Confidentiality, Integrity and Availability (CIA) to a multi-user VoIP (Voice over IP) system, performance and quality are at risk. The aim of this study is twofold. Firstly, it describes current methods suitable to secure voice streams within a VoIP system and make them available in an Asterisk-based VoIP environment. (Asterisk is a well established, open-source, TDM/VoIP PBX.) Secondly, this study evaluates the performance cost incurred after implementing each security method within the Asterisk-based system, using a special testbed suite, named DRAPA, which was developed expressly for this study. The three security methods implemented and studied were IPSec (Internet Protocol Security), SRTP (Secure Real-time Transport Protocol), and SIAX2 (Secure Inter-Asterisk eXchange 2 protocol). From the experiments, it was found that bandwidth and CPU usage were significantly affected by the addition of CIA. In ranking the three security methods in terms of these two resources, it was found that SRTP incurs the least bandwidth overhead, followed by SIAX2 and then IPSec. Where CPU utilisation is concerned, it was found that SIAX2 incurs the least overhead, followed by IPSec, and then SRTP
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High Performance Silicon Photonic Interconnected Systems
Advances in data-driven applications, particularly artificial intelligence and deep learning, are driving the explosive growth of computation and communication in today’s data centers and high-performance computing (HPC) systems. Increasingly, system performance is not constrained by the compute speed at individual nodes, but by the data movement between them. This calls for innovative architectures, smart connectivity, and extreme bandwidth densities in interconnect designs. Silicon photonics technology leverages mature complementary metal-oxide-semiconductor (CMOS) manufacturing infrastructure and is promising for low cost, high-bandwidth, and reconfigurable interconnects. Flexible and high-performance photonic switched architectures are capable of improving the system performance. The work in this dissertation explores various photonic interconnected systems and the associated optical switching functionalities, hardware platforms, and novel architectures. It demonstrates the capabilities of silicon photonics to enable efficient deep learning training.
We first present field programmable gate array (FPGA) based open-loop and closed-loop control for optical spectral-and-spatial switching of silicon photonic cascaded micro-ring resonator (MRR) switches. Our control achieves wavelength locking at the user-defined resonance of the MRR for optical unicast, multicast, and multiwavelength-select functionalities. Digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) are necessary for the control of the switch. We experimentally demonstrate the optical switching functionalities using an FPGA-based switch controller through both traditional multi-bit DAC/ADC and novel single-wired DAC/ADC circuits. For system-level integration, interfaces to the switch controller in a network control plane are developed. The successful control and the switching functionalitiesachieved are essential for system-level architectural innovations as presented in the following sections.
Next, this thesis presents two novel photonic switched architectures using the MRR-based switches. First, a photonic switched memory system architecture was designed to address memory challenges in deep learning. The reconfigurable photonic interconnects provide scalable solutions and enable efficient use of disaggregated memory resources for deep learning training. An experimental testbed was built with a processing system and two remote memory nodes using silicon photonic switch fabrics and system performance improvements were demonstrated. The collective results and existing high-bandwidth optical I/Os show the potential of integrating the photonic switched memory to state-of-the-art processing systems. Second, the scaling trends of deep learning models and distributed training workloads are challenging network capacities in today’s data centers and HPCs. A system architecture that leverages SiP switch-enabled server regrouping is proposed to tackle the challenges and accelerate distributed deep learning training. An experimental testbed with a SiP switch-enabled reconfigurable fat tree topology was built to evaluate the network performance of distributed ring all-reduce and parameter server workloads. We also present system-scale simulations. Server regrouping and bandwidth steering were performed on a large-scale tapered fat tree with 1024 compute nodes to show the benefits of using photonic switched architectures in systems at scale.
Finally, this dissertation explores high-bandwidth photonic interconnect designs for disaggregated systems. We first introduce and discuss two disaggregated architectures leveraging extreme high bandwidth interconnects with optically interconnected computing resources. We present the concept of rack-scale graphics processing unit (GPU) disaggregation with optical circuit switches and electrical aggregator switches. The architecture can leverage the flexibility of high bandwidth optical switches to increase hardware utilization and reduce application runtimes. A testbed was built to demonstrate resource disaggregation and defragmentation. In addition, we also present an extreme high-bandwidth optical interconnect accelerated low-latency communication architecture for deep learning training. The disaggregated architecture utilizes comb laser sources and MRR-based cross-bar switching fabrics to enable an all-to-all high bandwidth communication with a constant latency cost for distributed deep learning training. We discuss emerging technologies in the silicon photonics platform, including light source, transceivers, and switch architectures, to accommodate extreme high bandwidth requirements in HPC and data center environments. A prototype hardware innovation - Optical Network Interface Cards (comprised of FPGA, photonic integrated circuits (PIC), electronic integrated circuits (EIC), interposer, and high-speed printed circuit board (PCB)) is presented to show the path toward fast lanes for expedited execution at 10 terabits.
Taken together, the work in this dissertation demonstrates the capabilities of high-bandwidth silicon photonic interconnects and innovative architectural designs to accelerate deep learning training in optically connected data center and HPC systems
Efficient service discovery in wide area networks
Living in an increasingly networked world, with an abundant number
of services available to consumers, the consumer electronics market
is enjoying a boom. The average consumer in the developed world may
own several networked devices such as games consoles, mobile phones,
PDAs, laptops and desktops, wireless picture frames and printers to
name but a few. With this growing number of networked devices comes
a growing demand for services, defined here as functions requested
by a client and provided by a networked node. For example, a client
may wish to download and share music or pictures, find and use
printer services, or lookup information (e.g. train times, cinema
bookings).
It is notable that a significant proportion of networked devices are
now mobile. Mobile devices introduce a new dynamic to the service
discovery problem, such as lower battery and processing power and
more expensive bandwidth. Device owners expect to access services
not only in their immediate proximity, but further afield (e.g. in
their homes and offices). Solving these problems is the focus of
this research.
This Thesis offers two alternative approaches to service discovery
in Wide Area Networks (WANs). Firstly, a unique combination of the
Session Initiation Protocol (SIP) and the OSGi middleware technology
is presented to provide both mobility and service discovery
capability in WANs. Through experimentation, this technique is shown
to be successful where the number of operating domains is small, but
it does not scale well.
To address the issue of scalability, this Thesis proposes the use of
Peer-to-Peer (P2P) service overlays as a medium for service
discovery in WANs. To confirm that P2P overlays can in fact support
service discovery, a technique to utilise the Distributed Hash Table
(DHT) functionality of distributed systems is used to store and
retrieve service advertisements. Through simulation, this is shown
to be both a scalable and a flexible service discovery technique.
However, the problems associated with P2P networks with respect to
efficiency are well documented.
In a novel approach to reduce messaging costs in P2P networks,
multi-destination multicast is used. Two well known P2P overlays are
extended using the Explicit Multi-Unicast (XCAST) protocol. The
resulting analysis of this extension provides a strong argument for
multiple P2P maintenance algorithms co-existing in a single P2P
overlay to provide adaptable performance. A novel multi-tier P2P
overlay system is presented, which is tailored for service rich
mobile devices and which provides an efficient platform for service
discovery
Addressing Insider Threats from Smart Devices
Smart devices have unique security challenges and are becoming increasingly common. They have been used in the past to launch cyber attacks such as the Mirai attack. This work is focused on solving the threats posed to and by smart devices inside a network. The size of the problem is quantified; the initial compromise is prevented where possible, and compromised devices are identified.
To gain insight into the size of the problem, campus Domain Name System (DNS) measurements were taken that allow for wireless traffic to be separated from wired traffic. Two-thirds of the DNS traffic measured came from wireless hosts, implying that mobile devices are playing a bigger role in networks. Also, port scans and service discovery protocols were used to identify Internet of Things (IoT) devices on the campus network and follow-up work was done to assess the state of the IoT devices.
Motivated by these findings, three solutions were developed. To handle the scenario when compromised mobile devices are connected to the network, a new strategy for steppingstone detection was developed with both an application layer and a transport layer solution. The proposed solution is effective even when the mobile device cellular connection is used. Also, malicious or vulnerable applications make it through the mobile app store vetting process. A user space tool was developed that identifies apps contacting malicious domains in real time and collects data for research purposes. Malicious app behavior can then be identified on the user’s device, catching malicious apps that were overlooked by software vetting. Last, the variety of IoT device types and manufacturers makes the job of keeping them secure difficult. A generic framework was developed to lighten the management burden of securing IoT devices, serve as a middle box to secure legacy devices, and also use DNS queries as a way to identify misbehaving devices
Dynamic Quality-of-Service Management Under Software-Defined Networking Architectures
The Internet is facing new challenges emerging from new trends in Information and Communication Technologies (ICT) for example, cloud services, Big Data, increased mobile usage etc. Traditional IP networks rely in two design principles that, despite serving as an effective solution in the last decades, have become deprecated and not well fit for the new challenges. First, the control and data plane are tightly embedded in the networking devices and second, the structure is highly decentralized with no centralized point of management. This static and rigid architecture leaves no space for innovation with a consequence lack of scalability. Also, it leads to high management and operation costs. The SDN paradigm provides a more dynamic, manageable, cost-effective and adaptable architecture that is ready for the dynamic nature of today's applications. The goal of this thesis is a novel SDN-enabled solution that provides dynamic Quality of Service management for real-time and multimedia applications. This solution will be tested and implemented over a real, not-simulated testbed, composed by OpenFlow-enabled devices, the ONOS SDN controller and client terminals that produced/consume data streams. Furthermore, it is also expected to characterize and evaluate the benefits of the SDN-based solution against a traditional usage of the network (non-SDN)
Building the Future Internet through FIRE
The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate
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