1,236 research outputs found
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QoS - Aware content oriented flow routing in optical computer network
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this thesis, one of the most important issues in the field of networks communication is tackled and addressed. This issue is represented by QoS, where the increasing demand on highquality
applications together with the fast increase in the rates of Internet users have led to
massive traffic being transmitted on the Internet. This thesis proposes new ideas to manage the flow of this huge traffic in a manner that contributes in improving the communication QoS. This can be achieved by replacing the conventional application-insensitive routing schemes by others
which take into account the type of applications when making the routing decision. As a first contribution, the effect on the potential development in the quality of experience on the loading of
Basra optical network has been investigated. Furthermore, the traffic due to each application was dealt with in different ways according to their delay and loss sensitivities. Load rate distributions
over the various links due to the different applications were deployed to investigate the places of possible congestions in the network and the dominant applications that cause such congestions. In addition, OpenFlow and Optica Burst Switching (OBS) techniques were used to provide a wider range of network controllability and management. A centralised routing protocol
that takes into account the available bandwidth, delay, and security as three important QoS parameters, when forwarding traffics of different types, was proposed and implemented using OMNeT++ networks simulator. As a novel idea, security has been incorporated in our QoS requirements by incorporating Oyster Optics Technology (OOT) to secure some of the optical links aiming to supply the network with some secure paths for those applications that have high
privacy requirements. A particular type of traffic is to be routed according to the importance of these three QoS parameters for such a traffic type. The link utilisation, end to end delays and securities due to the different applications were recorded to prove the feasibility of our proposed
system. In order to decrease the amount of traffic overhead, the same QoS constraints were implemented on a distributed Ant colony based routing. The traditional Ant routing protocol was improved by adopting the idea of Red-Green-Blue (RGB) pheromones routing to incorporate these QoS constraints. Improvements of 11% load balancing, and 9% security for private data was achieved compared to the conventional Ant routing techniques. In addition, this Ant based
routing was utilised to propose an improved solution for the routing and wavelength assignment problem in the WDM optical computer networks
Optical Networks and Interconnects
The rapid evolution of communication technologies such as 5G and beyond, rely
on optical networks to support the challenging and ambitious requirements that
include both capacity and reliability. This chapter begins by giving an
overview of the evolution of optical access networks, focusing on Passive
Optical Networks (PONs). The development of the different PON standards and
requirements aiming at longer reach, higher client count and delivered
bandwidth are presented. PON virtualization is also introduced as the
flexibility enabler. Triggered by the increase of bandwidth supported by access
and aggregation network segments, core networks have also evolved, as presented
in the second part of the chapter. Scaling the physical infrastructure requires
high investment and hence, operators are considering alternatives to optimize
the use of the existing capacity. This chapter introduces different planning
problems such as Routing and Spectrum Assignment problems, placement problems
for regenerators and wavelength converters, and how to offer resilience to
different failures. An overview of control and management is also provided.
Moreover, motivated by the increasing importance of data storage and data
processing, this chapter also addresses different aspects of optical data
center interconnects. Data centers have become critical infrastructure to
operate any service. They are also forced to take advantage of optical
technology in order to keep up with the growing capacity demand and power
consumption. This chapter gives an overview of different optical data center
network architectures as well as some expected directions to improve the
resource utilization and increase the network capacity
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Silicon Photonic Subsystems for Inter-Chip Optical Networks
The continuous growth of electronic compute and memory nodes in terms of the number of I/O pins, bandwidth, and areal throughput poses major integration and packaging challenges associated with offloading multi-Tbit/s data rates within the few pJ/bit targets. While integrated photonics are already deployed in long and short distances such as inter and intra data centers communications, the promising characteristics of the silicon photonic platform set it as the future technology for optical interconnects in ultra short inter-chip distances. The high index contrast between the waveguide and the cladding together with strong thermo-optic and carrier effects in silicon allows developing a wide range of micro-scale and low power optical devices compatible with the CMOS fabrication processes. Furthermore, the availability of photonic foundries and new electrical and optical co-packaging techniques further pushes this platform for the next steps of commercial deployment.
The work in this dissertation presents the current trends in high-performance memory and processor nodes and gives motivation for disaggregated and reconfigurable inter-chip network enabled with the silicon photonic layer. A dense WDM transceiver and broadband switch architectures are discussed to support a bi-directional network of ten hybrid-memory cubes (HMC) interconnected to ten processor nodes with an overall aggregated bandwidth of 9.6Tbit/s. Latency and energy consumption are key performance parameters in a processor to primary memory nodes connectivity. The transceiver design is based on energy-efficient micro-ring resonators, and the broadband switch is constructed with 2x2 Mach-Zehnder elements for nano-second reconfiguration. Each transceiver is based on hundreds of micro-rings to convert the native HMC electrical protocol to the optical domain and the switch is based on tens of hundreds of 2x2 elements to achieve non-blocking all-to-all connectivity.
The next chapters focus on developing methods for controlling and monitoring such complex and highly integrated silicon photonic subsystems. The thermo-optic effect is characterized and we show experimentally that the phase of the optical carrier can be reliably controlled with pulse-width modulation (PWM) signal, ultimately relaxing the need for hundreds of digital to analog converters (DACs). We further show that doped waveguide heaters can be utilized as \textit{in-line} optical power monitors by measuring photo-conductance current, which is an alternative for the conventional tapping and integration of photo-diodes.
The next part concerned with a common cascaded micro-ring resonator in a WDM transceiver design. We develop on an FPGA control algorithm that abstracts the physical layer and takes user-defined inputs to set the resonances to the desired wavelength in a unicast and multicast transmission modes. The associated sensitivities of these silicon ring resonators are presented and addressed with three closed-loop solutions. We first show a closed-loop operation based on tapping the error signal from the drop port of the micro-ring. The second solution presents a resonance wavelength locking with a single digital I/O for control and feedback signals. Lastly, we leverage the photo-conductance effect and demonstrate the locking procedure using only the doped heater for both control and feedback purposes.
To achieve the inter-chip reconfigurability we discuss recent advances of high-port-count SiP broadband switches for reconfigurable inter-chip networks. To ensure optimal operation in terms of low insertion loss, low cross-talk and high signal integrity per routing path, hundreds of 2x2 Mach-Zehnder elements need to be biased precisely for the cross and bar states. We address this challenge with a tapless and a design agnostic calibration approach based on the photo-conductance effect. The automated algorithm returns a look-up table for all for each 2x2 element and the associated calibrated biases. Each routing scenario is then tested for insertion loss, crosstalk and bit-error rate of 25Gbit/s 4-level pulse amplitude modulation signals. The last part utilizes the Mach-Zehnder interferometers in WDM transceiver applications. We demonstrate a polarization insensitive four-channel WDM receiver with 40Gbit/s per channel and a transmitter design generating 8-level pulse amplitude modulation signals at 30Gbit/s
Optical Switching for Scalable Data Centre Networks
This thesis explores the use of wavelength tuneable transmitters and control systems within the context of scalable, optically switched data centre networks. Modern data centres require innovative networking solutions to meet their growing power, bandwidth, and scalability requirements. Wavelength routed optical burst switching (WROBS) can meet these demands by applying agile wavelength tuneable transmitters at the edge of a passive network fabric. Through experimental investigation of an example WROBS network, the transmitter is shown to determine system performance, and must support ultra-fast switching as well as power efficient transmission. This thesis describes an intelligent optical transmitter capable of wideband sub-nanosecond wavelength switching and low-loss modulation. A regression optimiser is introduced that applies frequency-domain feedback to automatically enable fast tuneable laser reconfiguration. Through simulation and experiment, the optimised laser is shown to support 122×50 GHz channels, switching in less than 10 ns. The laser is deployed as a component within a new wavelength tuneable source (WTS) composed of two time-interleaved tuneable lasers and two semiconductor optical amplifiers. Switching over 6.05 THz is demonstrated, with stable switch times of 547 ps, a record result. The WTS scales well in terms of chip-space and bandwidth, constituting the first demonstration of scalable, sub-nanosecond optical switching. The power efficiency of the intelligent optical transmitter is further improved by introduction of a novel low-loss split-carrier modulator. The design is evaluated using 112 Gb/s/λ intensity modulated, direct-detection signals and a single-ended photodiode receiver. The split-carrier transmitter is shown to achieve hard decision forward error correction ready performance after 2 km of transmission using a laser output power of just 0 dBm; a 5.2 dB improvement over the conventional transmitter. The results achieved in the course of this research allow for ultra-fast, wideband, intelligent optical transmitters that can be applied in the design of all-optical data centres for power efficient, scalable networking
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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
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