Ethernet Networks for Real-Time Use in the ATLAS Experiment

Ethernet became today's de-facto standard technology for local area networks. Defined by the IEEE 802.3 and 802.1 working groups, the Ethernet standards cover technologies deployed at the first two layers of the OSI protocol stack. The architecture of modern Ethernet networks is based on switches. The switches are devices usually built using a store-and-forward concept. At the highest level, they can be seen as a collection of queues and mathematically modelled by means of queuing theory. However, the traffic profiles on modern Ethernet networks are rather different from those assumed in classical queuing theory. The standard recommendations for evaluating the performance of network devices define the values that should be measured but do not specify a way of reconciling these values with the internal architecture of the switches. The introduction of the 10 Gigabit Ethernet standard provided a direct gateway from the LAN to the WAN by the means of the WAN PHY. Certain aspects related to the actual use of WAN PHY technology were vaguely defined by the standard. The ATLAS experiment at CERN is scheduled to start operation at CERN in 2007. The communication infrastructure of the Trigger and Data Acquisition System will be built using Ethernet networks. The real-time operational needs impose a requirement for predictable performance on the network part. In view of the diversity of the architectures of Ethernet devices, testing and modelling is required in order to make sure the full system will operate predictably. This thesis focuses on the testing part of the problem and addresses issues in determining the performance for both LAN and WAN connections. The problem of reconciling results from measurements to architectural details of the switches will also be tackled. We developed a scalable traffic generator system based on commercial-off-the-shelf Gigabit Ethernet network interface cards. The generator was able to transmit traffic at the nominal Gigabit Ethernet line rate for all frame sizes specified in the Ethernet standard. The calculation of latency was performed with accuracy in the range of +/- 200 ns. We indicate how certain features of switch architectures may be identified through accurate throughput and latency values measured for specific traffic distributions. At this stage, we present a detailed analysis of Ethernet broadcast support in modern switches. We use a similar hands-on approach to address the problem of extending Ethernet networks over long distances. Based on the 1 Gbit/s traffic generator used in the LAN, we develop a methodology to characterise point-to-point connections over long distance networks. At higher speeds, a combination of commercial traffic generators and high-end servers is employed to determine the performance of the connection. We demonstrate that the new 10 Gigabit Ethernet technology can interoperate with the installed base of SONET/SDH equipment through a series of experiments on point-to-point circuits deployed over long-distance network infrastructure in a multi-operator domain. In this process, we provide a holistic view of the end-to-end performance of 10 Gigabit Ethernet WAN PHY connections through a sequence of measurements starting at the physical transmission layer and continuing up to the transport layer of the OSI protocol stack

Optimising data centre operation by removing the transport bottleneck

Data centres lie at the heart of almost every service on the Internet. Data centres are used to provide search results, to power social media, to store and index email, to host “cloud” applications, for online retail and to provide a myriad of other web services. Consequently the more efficient they can be made the better for all of us. The power of modern data centres is in combining commodity off-the-shelf server hardware and network equipment to provide what Google’s Barrosso and Ho ̈lzle describe as “warehouse scale” computers. Data centres rely on TCP, a transport protocol that was originally designed for use in the Internet. Like other such protocols, TCP has been optimised to maximise throughput, usually by filling up queues at the bottleneck. However, for most applications within a data centre network latency is more critical than throughput. Consequently the choice of transport protocol becomes a bottleneck for performance. My thesis is that the solution to this is to move away from the use of one-size-fits-all transport protocols towards ones that have been designed to reduce latency across the data centre and which can dynamically respond to the needs of the applications. This dissertation focuses on optimising the transport layer in data centre networks. In particular I address the question of whether any single transport mechanism can be flexible enough to cater to the needs of all data centre traffic. I show that one leading protocol (DCTCP) has been heavily optimised for certain network conditions. I then explore approaches that seek to minimise latency for applications that care about it while still allowing throughput-intensive applications to receive a good level of service. My key contributions to this are Silo and Trevi. Trevi is a novel transport system for storage traffic that utilises fountain coding to max- imise throughput and minimise latency while being agnostic to drop, thus allowing storage traffic to be pushed out of the way when latency sensitive traffic is present in the network. Silo is an admission control system that is designed to give tenants of a multi-tenant data centre guaranteed low latency network performance. Both of these were developed in collaboration with others

Cooperative scheduling and load balancing techniques in fog and edge computing

Fog and Edge Computing are two models that reached maturity in the last decade. Today, they are two solid concepts and plenty of literature tried to develop them. Also corroborated by the development of technologies, like for example 5G, they can now be considered de facto standards when building low and ultra-low latency applications, privacy-oriented solutions, industry 4.0 and smart city infrastructures. The common trait of Fog and Edge computing environments regards their inherent distributed and heterogeneous nature where the multiple (Fog or Edge) nodes are able to interact with each other with the essential purpose of pre-processing data gathered by the uncountable number of sensors to which they are connected to, even by running significant ML models and relying upon specific processors (TPU). However, nodes are often placed in a geographic domain, like a smart city, and the dynamic of the traffic during the day may cause some nodes to be overwhelmed by requests while others instead may become completely idle. To achieve the optimal usage of the system and also to guarantee the best possible QoS across all the users connected to the Fog or Edge nodes, the need to design load balancing and scheduling algorithms arises. In particular, a reasonable solution is to enable nodes to cooperate. This capability represents the main objective of this thesis, which is the design of fully distributed algorithms and solutions whose purpose is the one of balancing the load across all the nodes, also by following, if possible, QoS requirements in terms of latency or imposing constraints in terms of power consumption when the nodes are powered by green energy sources. Unfortunately, when a central orchestrator is missing, a crucial element which makes the design of such algorithms difficult is that nodes need to know the state of the others in order to make the best possible scheduling decision. However, it is not possible to retrieve the state without introducing further latency during the service of the request. Furthermore, the retrieved information about the state is always old, and as a consequence, the decision is always relying on imprecise data. In this thesis, the problem is circumvented in two main ways. The first one considers randomised algorithms which avoid probing all of the neighbour nodes in favour of at maximum two nodes picked at random. This is proven to bring an exponential improvement in performance with respect to the probe of a single node. The second approach, instead, considers Reinforcement Learning as a technique for inferring the state of the other nodes thanks to the reward received by the agents when requests are forwarded. Moreover, the thesis will also focus on the energy aspect of the Edge devices. In particular, will be analysed a scenario of Green Edge Computing, where devices are powered only by Photovoltaic Panels and a scenario of mobile offloading targeting ML image inference applications. Lastly, a final glance will be given at a series of infrastructural studies, which will give the foundations for implementing the proposed algorithms on real devices, in particular, Single Board Computers (SBCs). There will be presented a structural scheme of a testbed of Raspberry Pi boards, and a fully-fledged framework called ``P2PFaaS'' which allows the implementation of load balancing and scheduling algorithms based on the Function-as-a-Service (FaaS) paradigm

Evaluation of data centre networks and future directions

Traffic forecasts predict a more than threefold increase in the global datacentre workload in coming years, caused by the increasing adoption of cloud and data-intensive applications. Consequently, there has been an unprecedented need for ultra-high throughput and minimal latency. Currently deployed hierarchical architectures using electronic packet switching technologies are costly and energy-inefficient. Very high capacity switches are required to satisfy the enormous bandwidth requirements of cloud datacentres and this limits the overall network scalability. With the maturity of photonic components, turning to optical switching in data centres is a viable option to accommodate greater bandwidth and network flexibility while potentially minimising the latency, cost and power consumption. Various DCN architectures have been proposed to date and this thesis includes a comparative analysis of such electronic and optical topologies to judge their suitability based on network performance parameters and cost/energy effectiveness, while identifying the challenges faced by recent DCN infrastructures. An analytical Layer 2 switching model is introduced that can alleviate the simulation scalability problem and evaluate the performance of the underlying DCN architecture. This model is also used to judge the variation in traffic arrival/offloading at the intermediate queueing stages and the findings are used to derive closed form expressions for traffic arrival rates and delay. The results from the simulated network demonstrate the impact of buffering and versubscription and reveal the potential bottlenecks and network design tradeoffs. TCP traffic forms the bulk of current DCN workload and so the designed network is further modified to include TCP flows generated from a realistic traffic generator for assessing the impact of Layer 4 congestion control on the DCN performance with standard TCP and datacentre specific TCP protocols (DCTCP). Optical DCN architectures mostly concentrate on core-tier switching. However, substantial energy saving is possible by introducing optics in the edge tiers. Hence, a new approach to optical switching is introduced using Optical ToR switches which can offer better delay performance than commodity switches of similiar size, while having far less power dissipation. An all-optical topology has been further outlined for the efficient implementation of the optical switch meeting the future scalability demands

MMPTCP: a novel transport protocol for data centre networks

Modern data centres provide large aggregate capacity in the backbone of networks so that servers can theoretically communicate with each other at their maximum rates. However, the Transport Control Protocol (TCP) cannot efficiently use this large capacity even if Equal-Cost Multi-Path (ECMP) routing is enabled to exploit the existence of parallel paths. MultiPath TCP (MPTCP) can effectively use the network resources of such topologies by performing fast distributed load balancing. MPTCP is an appealing technique for data centres that are very dynamic in nature. However, it is ill-suited for handling short flows since it increases their flow completion time. To mitigate these problems, we propose Maximum MultiPath TCP (MMPTCP), a novel transport protocol for modern data centres. Unlike MPTCP, it provides high performance for all network flows. It also decreases the bursty nature of data centres, which is essentially rooted in traffic patterns of short flows. MMPTCP achieves these nice features by randomising a flow’s packets via all parallel paths to a destination during the initial phase of data transmission until a certain amount of data is delivered. It then switches to MPTCP with several subflows in which data transmission is governed by MPTCP congestion control. In this way, short flows are delivered very fast via the initial phase only, and long flows are delivered by MPTCP with several subflows. We evaluate MMPTCP in a FatTree topology under various network conditions. We found that MMPTCP decreases the loss rate of all the links throughout the network and helps competing flows to achieve a better performance. Unlike MPTCP with a fixed number of subflows, MMPTCP offers high burst tolerance and low-latency for short flows while it maintains high overall network utilisation. MMPTCP is incrementally deployable in existing data centres because it does not require any modification to the network and application layers

Performance controls for distributed telecommunication services

As the Internet and Telecommunications domains merge, open telecommunication service architectures such as TINA, PARLAY and PINT are becoming prevalent. Distributed Computing is a common engineering component in these technologies and promises to bring improvements to the scalability, reliability and flexibility of telecommunications service delivery systems. This distributed approach to service delivery introduces new performance concerns. As service logic is decomposed into software components and distnbuted across network resources, significant additional resource loading is incurred due to inter-node communications. This fact makes the choice of distribution of components in the network and the distribution of load between these components critical design and operational issues which must be resolved to guarantee a high level of service for the customer and a profitable network for the service operator. Previous research in the computer science domain has addressed optimal placement of components from the perspectives of minimising run time, minimising communications costs or balancing of load between network resources. This thesis proposes a more extensive optimisation model, which we argue, is more useful for addressing concerns pertinent to the telecommunications domain. The model focuses on providing optimal throughput and profitability of network resources and on overload protection whilst allowing flexibility in terms of the cost of installation of component copies and differentiation in the treatment of service types, in terms of fairness to the customer and profitability to the operator. Both static (design-time) component distribution and dynamic (run-time) load distribution algorithms are developed using Linear and Mixed Integer Programming techniques. An efficient, but sub-optimal, run-time solution, employing Market-based control, is also proposed. The performance of these algorithms is investigated using a simulation model of a distributed service platform, which is based on TINA service components interacting with the Intelligent Network through gateways. Simulation results are verified using Layered Queuing Network analytic modelling Results show significant performance gains over simpler methods of performance control and demonstrate how trade-offs in network profitability, fairness and network cost are possible

An efficient multichannel wireless sensor networks MAC protocol based on IEEE 802.11 distributed co-ordinated function.

This research aimed to create new knowledge and pioneer a path in the area relating to future trends in the WSN, by resolving some of the issues at the MAC layer in Wireless Sensor Networks. This work introduced a Multi-channel Distributed Coordinated Function (MC-DCF) which takes advantage of multi-channel assignment. The backoff algorithm of the IEEE 802.11 distributed coordination function (DCF) was modified to invoke channel switching, based on threshold criteria in order to improve the overall throughput for wireless sensor networks. This work commenced by surveying different protocols: contention-based MAC protocols, transport layer protocols, cross-layered design and multichannel multi-radio assignments. A number of existing protocols were analysed, each attempting to resolve one or more problems faced by the current layers. The 802.15.4 performed very poorly at high data rate and at long range. Therefore 802.15.4 is not suitable for sensor multimedia or surveillance system with streaming data for future multichannel multi-radio systems. A survey on 802.11 DCF - which was designed mainly for wireless networks –supports and confirm that it has a power saving mechanism which is used to synchronise nodes. However it uses a random back-off mechanism that cannot provide deterministic upper bounds on channel access delay and as such cannot support real-time traffic. The weaknesses identified by surveying this protocol form the backbone of this thesis The overall aim for this thesis was to introduce multichannel with single radio as a new paradigm for IEEE 802.11 Distributed Coordinated Function (DCF) in wireless sensor networks (WSNs) that is used in a wide range of applications, from military application, environmental monitoring, medical care, smart buildings and other industry and to extend WSNs with multimedia capability which sense for instance sounds or motion, video sensor which capture video events of interest. Traditionally WSNs do not need high data rate and throughput, since events are normally captured periodically. With the paradigm shift in technology, multimedia streaming has become more demanding than data sensing applications as such the need for high data rate protocol for WSN which is an emerging technology in this area. The IEEE 802.11 can support data rates up to 54Mbps and 802.11 DCF was designed specifically for use in wireless networks. This thesis focused on designing an algorithm that applied multichannel to IEEE 802.11 DCF back-off algorithm to reduce the waiting time of a node and increase throughput when attempting to access the medium. Data collection in WSN tends to suffer from heavy congestion especially nodes nearer to the sink node. Therefore, this thesis proposes a contention based MAC protocol to address this problem from the inspiration of the 802.11 DCF backoff algorithm resulting from a comparison of IEEE 802.11 and IEEE 802.15.4 for Future Green Multichannel Multi-radio Wireless Sensor Networks

Security related self-protected networks: Autonomous threat detection and response (ATDR)

>Magister Scientiae - MScCybersecurity defense tools, techniques and methodologies are constantly faced with increasing challenges including the evolution of highly intelligent and powerful new-generation threats. The main challenges posed by these modern digital multi-vector attacks is their ability to adapt with machine learning. Research shows that many existing defense systems fail to provide adequate protection against these latest threats. Hence, there is an ever-growing need for self-learning technologies that can autonomously adjust according to the behaviour and patterns of the offensive actors and systems. The accuracy and effectiveness of existing methods are dependent on decision making and manual input by human experts. This dependence causes 1) administration overhead, 2) variable and potentially limited accuracy and 3) delayed response time

Improving video streaming experience through network measurements and analysis

Multimedia traffic dominates today’s Internet. In particular, the most prevalent traffic carried over wired and wireless networks is video. Most popular streaming providers (e.g. Netflix, Youtube) utilise HTTP adaptive streaming (HAS) for video content delivery to end-users. The power of HAS lies in the ability to change video quality in real time depending on the current state of the network (i.e. available network resources). The main goal of HAS algorithms is to maximise video quality while minimising re-buffering events and switching between different qualities. However, these requirements are opposite in nature, so striking a perfect blend is challenging, as there is no single widely accepted metric that captures user experience based on the aforementioned requirements. In recent years, researchers have put a lot of effort into designing subjectively validated metrics that can be used to map quality, re-buffering and switching behaviour of HAS players to the overall user experience (i.e. video QoE). This thesis demonstrates how data analysis can contribute in improving video QoE. One of the main characteristics of mobile networks is frequent throughput fluctuations. There are various underlying factors that contribute to this behaviour, including rapid changes in the radio channel conditions, system load and interaction between feedback loops at the different time scales. These fluctuations highlight the challenge to achieve a high video user experience. In this thesis, we tackle this issue by exploring the possibility of throughput prediction in cellular networks. The need for better throughput prediction comes from data-based evidence that standard throughput estimation techniques (e.g. exponential moving average) exhibit low prediction accuracy. Cellular networks deploy opportunistic exponential scheduling algorithms (i.e. proportional-fair) for resource allocation among mobile users/devices. These algorithms take into account a user’s physical layer information together with throughput demand. While the algorithm itself is proprietary to the manufacturer, physical layer and throughput information are exchanged between devices and base stations. Availability of this information allows for a data-driven approach for throughput prediction. This thesis utilises a machine-learning approach to predict available throughput based on measurements in the near past. As a result, a prediction accuracy with an error less than 15% in 90% of samples is achieved. Adding information from other devices served by the same base station (network-based information) further improves accuracy while lessening the need for a large history (i.e. how far to look into the past). Finally, the throughput prediction technique is incorporated to state-of-the-art HAS algorithms. The approach is validated in a commercial cellular network and on a stock mobile device. As a result, better throughput prediction helps in improving user experience up to 33%, while minimising re-buffering events by up to 85%. In contrast to wireless networks, channel characteristics of the wired medium are more stable, resulting in less prominent throughput variations. However, all traffic traverses through network queues (i.e. a router or switch), unlike in cellular networks where each user gets a dedicated queue at the base station. Furthermore, network operators usually deploy a simple first-in-first-out queuing discipline at queues. As a result, traffic can experience excessive delays due to the large queue sizes, usually deployed in order to minimise packet loss and maximise throughput. This effect, also known as bufferbloat, negatively impacts delay-sensitive applications, such as web browsing and voice. While there exist guidelines for modelling queue size, there is no work analysing its impact on video streaming traffic generated by multiple users. To answer this question, the performance of multiple videos clients sharing a bottleneck link is analysed. Moreover, the analysis is extended to a realistic case including heterogeneous round-trip-time (RTT) and traffic (i.e. web browsing). Based on experimental results, a simple two queue discipline is proposed for scheduling heterogeneous traffic by taking into account application characteristics. As a result, compared to the state-of-the-art Active Queue Management (AQM) discipline, Controlled Delay Management (CoDel), the proposed discipline decreases median Page Loading Time (PLT) of web traffic by up to 80% compared to CoDel, with no significant negative impact on video QoE