Understanding the characteristics of Internet traffic and designing an efficient RaptorQ-based data transport protocol for modern data centres

This thesis is the amalgamation of research on efficient data transport protocols for data centres and a comprehensive and systematic study of Internet traffic, which came as a result of the need to understand traffic patterns and workloads in modern computer networks. The first part of the thesis is on the development of efficient data transport pro- tocols for data centres. We study modern data transport protocols for data centres through large scale simulations using the OMNeT++ simulator. We developed and experimented with an OMNeT++ model of NDP. This has led to the identification of limitations of the state of the art and the formulation of research questions with respect to data transport protocols for modern data centres. The developed model includes an implementation of a Fat-tree topology and per-packet ECMP load bal- ancing. We discuss how we integrated the model with the INET Framework and validated it by running various experiments that test different model parameters and components. This work revealed limitations of NDP with respect to efficient one-to-many and many-to-one communication in data centres, which led to the de- velopment of SCDP, a novel and general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, natively supports one-to-many and many-to-one data communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, in-network packet trimming and Multi-Level Feed- back Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many- to-one data transport; (2) on top of RaptorQ codes, receiver- driven flow control, in combination with in-network packet trimming, enable efficient usage of network re- sources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluated SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better than NDP. For short and long unicast flows, SCDP performs equally well or better compared to NDP. In the second part of the thesis, we extensively study Internet traffic. Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that the log-normal distribution is a better fit than the Gaussian distribution. We also investigate a second, heavy- tailed distribution and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which are a poor fit for all tested distributions and show that this is often due to traffic outages or links that hit maximum capacity. Stationarity tests showed that the traffic is stationary at some range of aggregation times. We demonstrate the utility of the log-normal distribution in two contexts: predicting the proportion of time traffic will exceed a given level (for link capacity estimation) and predicting 95th percentile pricing. We also show the log-normal distribution is a better predictor than Gaussian orWeibull distributions

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

On Improving Efficiency of Data-Intensive Applications in Geo-Distributed Environments

Distributed systems are pervasively demanded and adopted in nowadays for processing data-intensive workloads since they greatly accelerate large-scale data processing with scalable parallelism and improved data locality. Traditional distributed systems initially targeted computing clusters but have since evolved to data centers with multiple clusters. These systems are mostly built on top of homogeneous, tightly integrated resources connected in high-speed local-area networks (LANs), and typically require data to be ingested to a central data center for processing. Today, with enormous volumes of data continuously generated from geographically distributed locations, direct adoption of such systems is prohibitively inefficient due to the limited system scalability and high cost for centralizing the geo-distributed data over the wide-area networks (WANs). More commonly, it becomes a trend to build geo-distributed systems wherein data processing jobs are performed on top of geo-distributed, heterogeneous resources in proximity to the data at vastly distributed geo-locations. However, critical challenges and mechanisms for efficient execution of data-intensive applications in such geo-distributed environments are unclear by far. The goal of this dissertation is to identify such challenges and mechanisms, by extensively using the research principles and methodology of conventional distributed systems to investigate the geo-distributed environment, and by developing new techniques to tackle these challenges and run data-intensive applications with efficiency at scale. The contributions of this dissertation are threefold. Firstly, the dissertation shows that the high level of resource heterogeneity exhibited in the geo-distributed environment undermines the scalability of geo-distributed systems. Virtualization-based resource abstraction mechanisms have been introduced to abstract the hardware, network, and OS resources throughout the system, to mitigate the underlying resource heterogeneity and enhance the system scalability. Secondly, the dissertation reveals the overwhelming performance and monetary cost incurred by indulgent data sharing over the WANs in geo-distributed systems. Network optimization approaches, including linear- programming-based global optimization, greedy bin-packing heuristics, and TCP enhancement, are developed to optimize the network resource utilization and circumvent unnecessary expenses imposed on data sharing in WANs. Lastly, the dissertation highlights the importance of data locality for data-intensive applications running in the geo-distributed environment. Novel data caching and locality-aware scheduling techniques are devised to improve the data locality.Doctor of Philosoph

Profiling Large-scale Live Video Streaming and Distributed Applications

PhDToday, distributed applications run at data centre and Internet scales, from intensive data analysis, such as MapReduce; to the dynamic demands of a worldwide audience, such as YouTube. The network is essential to these applications at both scales. To provide adequate support, we must understand the full requirements of the applications, which are revealed by the workloads. In this thesis, we study distributed system applications at different scales to enrich this understanding. Large-scale Internet applications have been studied for years, such as social networking service (SNS), video on demand (VoD), and content delivery networks (CDN). An emerging type of video broadcasting on the Internet featuring crowdsourced live video streaming has garnered attention allowing platforms such as Twitch to attract over 1 million concurrent users globally. To better understand Twitch, we collected real-time popularity data combined with metadata about the contents and found the broadcasters rather than the content drives its popularity. Unlike YouTube and Netflix where content can be cached, video streaming on Twitch is generated instantly and needs to be delivered to users immediately to enable real-time interaction. Thus, we performed a large-scale measurement of Twitchs content location revealing the global footprint of its infrastructure as well as discovering the dynamic stream hosting and client redirection strategies that helped Twitch serve millions of users at scale. We next consider applications that run inside the data centre. Distributed computing applications heavily rely on the network due to data transmission needs and the scheduling of resources and tasks. One successful application, called Hadoop, has been widely deployed for Big Data processing. However, little work has been devoted to understanding its network. We found the Hadoop behaviour is limited by hardware resources and processing jobs presented. Thus, after characterising the Hadoop traffic on our testbed with a set of benchmark jobs, we built a simulator to reproduce Hadoops job traffic With the simulator, users can investigate the connections between Hadoop traffic and network performance without additional hardware cost. Different network components can be added to investigate the performance, such as network topologies, queue policies, and transport layer protocols. In this thesis, we extended the knowledge of networking by investigated two widelyused applications in the data centre and at Internet scale. We (i)studied the most popular live video streaming platform Twitch as a new type of Internet-scale distributed application revealing that broadcaster factors drive the popularity of such platform, and we (ii)discovered the footprint of Twitch streaming infrastructure and the dynamic stream hosting and client redirection strategies to provide an in-depth example of video streaming delivery occurring at the Internet scale, also we (iii)investigated the traffic generated by a distributed application by characterising the traffic of Hadoop under various parameters, (iv)with such knowledge, we built a simulation tool so users can efficiently investigate the performance of different network components under distributed applicationQueen Mary University of Londo

Scheduling for Large Scale Distributed Computing Systems: Approaches and Performance Evaluation Issues

Although our everyday life and society now depends heavily oncommunication infrastructures and computation infrastructures,scientists and engineers have always been among the main consumers ofcomputing power. This document provides a coherent overview of theresearch I have conducted in the last 15 years and which targets themanagement and performance evaluation of large scale distributedcomputing infrastructures such as clusters, grids, desktop grids,volunteer computing platforms, ... when used for scientific computing.In the first part of this document, I present how I have addressedscheduling problems arising on distributed platforms (like computinggrids) with a particular emphasis on heterogeneity and multi-userissues, hence in connection with game theory. Most of these problemsare relaxed from a classical combinatorial optimization formulationinto a continuous form, which allows to easily account for keyplatform characteristics such as heterogeneity or complex topologywhile providing efficient practical and distributed solutions.The second part presents my main contributions to the SimGrid project,which is a simulation toolkit for building simulators of distributedapplications (originally designed for scheduling algorithm evaluationpurposes). It comprises a unified presentation of how the questions ofvalidation and scalability have been addressed in SimGrid as well asthoughts on specific challenges related to methodological aspects andto the application of SimGrid to the HPC context

Network-Wide Monitoring And Debugging

Modern networks can encompass over 100,000 servers. Managing such an extensive network with a diverse set of network policies has become more complicated with the introduction of programmable hardwares and distributed network functions. Furthermore, service level agreements (SLAs) require operators to maintain high performance and availability with low latencies. Therefore, it is crucial for operators to resolve any issues in networks quickly. The problems can occur at any layer of stack: network (load imbalance), data-plane (incorrect packet processing), control-plane (bugs in configuration) and the coordination among them. Unfortunately, existing debugging tools are not sufficient to monitor, analyze, or debug modern networks; either they lack visibility in the network, require manual analysis, or cannot check for some properties. These limitations arise from the outdated view of the networks, i.e., that we can look at a single component in isolation. In this thesis, we describe a new approach that looks at measuring, understanding, and debugging the network across devices and time. We also target modern stateful packet processing devices: programmable data-planes and distributed network functions as these becoming increasingly common part of the network. Our key insight is to leverage both in-network packet processing (to collect precise measurements) and out-of-network processing (to coordinate measurements and scale analytics). The resulting systems we design based on this approach can support testing and monitoring at the data center scale, and can handle stateful data in the network. We automate the collection and analysis of measurement data to save operator time and take a step towards self driving networks

Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms