End-to-end informed VM selection in compute clouds

The selection of resources, particularly VMs, in current public IaaS clouds is usually done in a blind fashion, as cloud users do not have much information about resource consumption by co-tenant third-party tasks. In particular, communication patterns can play a significant part in cloud application performance and responsiveness, specially in the case of novel latencysensitive applications, increasingly common in today’s clouds. Thus, herein we propose an end-to-end approach to the VM allocation problem using policies based uniquely on round-trip time measurements between VMs. Those become part of a userlevel ‘Recommender Service’ that receives VM allocation requests with certain network-related demands and matches them to a suitable subset of VMs available to the user within the cloud. We propose and implement end-to-end algorithms for VM selection that cover desirable profiles of communications between VMs in distributed applications in a cloud setting, such as profiles with prevailing pair-wise, hub-and-spokes, or clustered communication patterns between constituent VMs. We quantify the expected benefits from deploying our Recommender Service by comparing our informed VM allocation approaches to conventional, random allocation methods, based on real measurements of latencies between Amazon EC2 instances. We also show that our approach is completely independent from cloud architecture details, is adaptable to different types of applications and workloads, and is lightweight and transparent to cloud providers.This work is supported in part by the National Science Foundation under grant CNS-0963974

Multi-capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP) problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem , and we evaluate its efficiency using simulations on various application workloads, and network models.This work was done while author was at Boston University. It was partially supported by NSF CISE awards #1430145, #1414119, #1239021 and #1012798. (1430145 - NSF CISE; 1414119 - NSF CISE; 1239021 - NSF CISE; 1012798 - NSF CISE

Network-constrained packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources.With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models.This work is supported by NSF CISE CNS Award #1347522, # 1239021, # 1012798

Pathfinder: Application-Aware Distributed Path Computation in Clouds

Path computation in a network is dependent on the network’s processes and resource usage pattern. While distributed traffic control methods improve the scalability of a system, their topology and link state conditions may influence the sub-optimal path computation. Herein, we present Pathfinder, an application-aware distributed path computation model. The proposed model framework can improve path computation functions through software-defined network controls. In the paper, we first analyse the key issues in distributed path computation functions and then present Pathfinder’s system architecture, followed by its design principles and orchestration environment. Furthermore, we evaluate our system’s performance by comparing it with FreeFlow and Prune-Dijk techniques. Our results demonstrate that Pathfinder outperforms these two techniques and delivers significant improvement in the system’s resource utilisation behaviour

ClouDiA: a deployment advisor for public clouds

An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and oper-ating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances non-contiguously, i.e., instances of a given application may end up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in signif-icant performance degradation, unless care is taken in how applica-tion components are mapped to instances. In this paper, we propose ClouDiA, a general deployment ad-visor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs mixed-integer programming and constraint programming techniques to ef-ficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applica-tions in Amazon EC2, we show that our techniques yield a 15 % to 55 % reduction in time-to-solution or service response time, without any need for modifying application code. 1

Improving the Performance of Cloud-based Scientific Services

Cloud computing provides access to a large scale set of readily available computing resources at the click of a button. The cloud paradigm has commoditised computing capacity and is often touted as a low-cost model for executing and scaling applications. However, there are significant technical challenges associated with selecting, acquiring, configuring, and managing cloud resources which can restrict the efficient utilisation of cloud capabilities. Scientific computing is increasingly hosted on cloud infrastructure—in which scientific capabilities are delivered to the broad scientific community via Internet-accessible services. This migration from on-premise to on-demand cloud infrastructure is motivated by the sporadic usage patterns of scientific workloads and the associated potential cost savings without the need to purchase, operate, and manage compute infrastructure—a task that few scientific users are trained to perform. However, cloud platforms are not an automatic solution. Their flexibility is derived from an enormous number of services and configuration options, which in turn result in significant complexity for the user. In fact, naïve cloud usage can result in poor performance and excessive costs, which are then directly passed on to researchers. This thesis presents methods for developing efficient cloud-based scientific services. Three real-world scientific services are analysed and a set of common requirements are derived. To address these requirements, this thesis explores automated and scalable methods for inferring network performance, considers various trade-offs (e.g., cost and performance) when provisioning instances, and profiles application performance, all in heterogeneous and dynamic cloud environments. Specifically, network tomography provides the mechanisms to infer network performance in dynamic and opaque cloud networks; cost-aware automated provisioning approaches enable services to consider, in real-time, various trade-offs such as cost, performance, and reliability; and automated application profiling allows a huge search space of applications, instance types, and configurations to be analysed to determine resource requirements and application performance. Finally, these contributions are integrated into an extensible and modular cloud provisioning and resource management service called SCRIMP. Cloud-based scientific applications and services can subscribe to SCRIMP to outsource their provisioning, usage, and management of cloud infrastructures. Collectively, the approaches presented in this thesis are shown to provide order of magnitude cost savings and significant performance improvement when employed by production scientific services

Detection of Power Line Supporting Towers via Interpretable Semantic Segmentation of 3D Point Clouds

The inspection and maintenance of energy transmission networks are demanding and crucial tasks for any transmission system operator. They rely on a combination of on-theground staff and costly low-flying helicopters to visually inspect the power grid structure. Recently, LiDAR-based inspections have shown the potential to accelerate and increase inspection precision. These high-resolution sensors allow one to scan an environment and store it in a 3D point cloud format for further processing and analysis by maintenance specialists to prevent fires and damage to the electrical system. However, this task is especially demanding to handle on time when we consider the extensive area that the transmission network covers. Nonetheless, the transition to point cloud data allows us to take advantage of Deep Learning to automate these inspections, by detecting collisions between the grid and the revolving scene. Deep Learning is a recent and powerful tool that has been successfully applied to a myriad of real-life problems, such as image recognition and speech generation. With the introduction of affordable LiDAR sensors, the application of Deep Learning on 3D data emerged, with numerous methods being proposed every day to address difficult problems, from 3D object detection to 3D point cloud segmentation. Alas, state-of-the-art methods are remarkably complex, composed of millions of trainable parameters, and take several weeks, if not months, to train on specific hardware, which makes it difficult for traditional companies, like utilities, to employ them. Therefore, we explore a novel mathematical framework that allows us to define tailored operators that incorporate prior knowledge regarding our problem. These operators are then integrated into a learning agent, called SCENE-Net, that detects power line supporting towers in 3D point clouds. SCENE-Net allows for the interpretability of its results, which is not possible in conventional models, it shows an efficient training and inference time of 85 mn and 20 ms on a regular laptop. Our model is composed of 11 trainable geometrical parameters, like the height of a cylinder, and has a Precision gain of 24% against a comparable CNN with 2190 parameters.A inspeção e manutenção de redes de transmissão de energia são tarefas cruciais para operadores de rede. Recentemente, foram adotadas inspeções utilizando sensores LiDAR de forma a acelerar este processo e aumentar a sua precisão. Estes sensores são objetos de alta precisão que conseguem inspecionar ambientes e guarda-los no formato de nuvens de pontos 3D, para serem posteriormente analisadas por specialistas que procuram prevenir fogos florestais e danos à estruta eléctrica. No entanto, esta tarefa torna-se bastante difícil de concluir em tempo útil pois a rede de transmissão é bastasnte vasta. Por isso, podemos tirar partido da transição para dados LiDAR e utilizar aprendizagem profunda para automatizar as inspeções à rede. Aprendizagem profunda é um campo recente e em grande desenvolvimento, sendo aplicado a vários problemas do nosso quotidiano e facilmente atinge um desempenho superior ao do ser humano, como em reconhecimento de imagens, geração de voz, entre outros. Com o desenvolvimento de sensores LiDAR acessíveis, o uso de aprendizagem profunda em dados 3D rapidamente se desenvolveu, apresentando várias metodologias novas todos os dias que respondem a problemas complexos, como deteção de objetos 3D. No entanto, modelos do estado da arte são incrivelmente complexos e compostos por milhões de parâmetros e demoram várias semanas, senão meses, a treinar em GPU potentes, o que dificulta a sua utilização em empresas tradicionais, como a EDP. Portanto, nós exploramos uma nova teoria matemática que nos permite definir operadores específicos que incorporaram conhecimento sobre o nosso problema. Estes operadores são integrados num modelo de aprendizagem prounda, designado SCENE-Net, que deteta torres de suporte de linhas de transmissão em nuvens de pontos. SCENE-Net permite a interpretação dos seus resultados, aspeto que não é possível com modelos convencionais, demonstra um treino eficiente de 85 minutos e tempo de inferência de 20 milissegundos num computador tradicional. O nosso modelo contém apenas 11 parâmetros geométricos, como a altura de um cilindro, e demonstra um ganho de Precisão de 24% quando comparado com uma CNN com 2190 parâmetros

Revealing and Characterizing MPLS Networks

The Internet is a wide network of computers in constant evolution. Each year, more and more organizations are connected to this worldwide network. Each of them has its own structure and administration that are not publicly revealed for economical, political, and security reasons. Consequently, our perception of the Internet structure, and more specifically, its topology, is incomplete. In order to balance this lack of knowledge, the research community relies on network measurements. Most of the time, they are performed based on the well-known tool traceroute. However, in practice, an operator may privilege other technologies than IP to forward packets inside its network. MultiProtocol Label Switching (MPLS) is one them. Even if it is heavily deployed by operators, it has not been really investigated by researchers. Prior to this thesis, only two studies focused on the identification of MPLS tunnels in traceroute data. Moreover, while one of them does not take all possible scenarios into account, the other lack of precision in some of its models. In addition, MPLS tunnels may hide their content to traceroute. Topologies inferred from such data may thus contain false links or nodes with an artificially high degree, leading so to biases in standard graph metrics used to model the network. Even if some researchers already tried to tackle this issue, the revelation of hidden MPLS devices in traceroute data is still an open question. This thesis aims at characterizing MPLS in two different ways. On the one hand, at an architectural level, we will analyze in detail its deployment and use in both IPv4 and IPv6 networks in order to improve its state-of-the-art view. We will show that, in practice, more than one IPv4 trace out of two crosses at least one MPLS tunnel. We will also see that, even if this protocol can simplify the internal architecture of transit networks, it also allows some operators to perform traffic engineering in their domain. On the other hand, MPLS will be studied from a measurement point of view. We will see that routers from different manufacturers may have distinct default behaviors regarding to MPLS, and that these specific behaviors can be exploited to identify MPLS tunnels during traceroute measurements. More precisely, we will focus on new methods able to infer the presence of tunnels that are invisible in traceroute outputs, as well as on mechanisms to reveal their content. We will also show that they can be used in order to improve the inference of Internet graph properties, such as path lengths and node degrees. Finally, these techniques will be integrated into Trace the Naughty Tunnels (TNT), a traceroute extension able to identify all types of MPLS tunnels along a path towards a destination. We will prove that this tool can be used in order to get a detailed quantification of MPLS tunnels in the worldwide network. TNT is publicly available, and can therefore be part of many future studies conducted by the research community.Internet est un immense réseau informatique en constante évolution. Chaque année, de plus en plus d’organisations s’y connectent. Chacune d’elles est gérée et administrée indépendamment des autres. En pratique, l’architecture interne de leur réseau n’est pas rendue publique pour des raisons politiques, économiques, ou de sécurité. Par conséquent, notre perception de la structure d’Internet, et plus particulièrement de sa topologie, est incomplète. Afin de pallier ce manque de connaissance, la communauté de la recherche s’appuie sur des mesures de réseau. La plupart du temps, elles sont réalisées avec l’outil traceroute. Cependant, des technologies autres que IP peuvent être privilégiées pour transférer les paquets dans un réseau. MultiProtocol Label Switching (MPLS) est l’une d’entre elles. Même si cette technologie est largement déployée dans Internet, elle n’est pas bien étudiée par les chercheurs. Avant cette thèse, seulement deux travaux se sont intéressés à l’identification d’MPLS dans les données collectées avec traceroute. Alors que le premier ne prend pas en compte tous les scénarios possibles, le second propose des modèles qui manquent de précision. De plus, les tunnels MPLS peuvent dissimuler leur contenu à traceroute. Les topologies inférées sur base de ces données peuvent donc contenir de faux liens, ou des noeuds avec un degré anormalement élevé. Les différentes modélisations d’Internet qui en résultent peuvent alors être biaisées. Aujourd’hui, la question de la révélation des routeurs MPLS qui sont invisibles dans les données de mesure n’est toujours pas résolue, même si certains chercheurs ont déjà proposé quelques méthodes pour y parvenir. Cette thèse a pour but de caractériser MPLS de deux manières différentes. Dans un premier temps, au niveau architectural, nous analyserons en détail son déploiement et son utilisation dans les réseaux IPv4 et IPv6 afin d’améliorer l’état de l’art. Nous montrerons qu’en pratique, plus d’une trace IPv4 sur deux traverse au moins un tunnel MPLS. Nous découvrirons également que bien que ce protocole peut être utilisé pour simplifier l’architecture interne des réseaux de transit, il peut aussi être déployé pour la mise en place de solutions d’ingénierie de trafic. Dans un second temps, MPLS sera étudié d’un point de vue mesure. Nous verrons que les comportements par défaut liés au protocole varient d’un fabricant de routeur à l’autre, et qu’ils peuvent être exploités afin d’identifier les tunnels MPLS dans les données traceroute. Plus précisément, nous découvrirons de nouvelles méthodes capables d’inférer la présence de tunnels invisibles avec traceroute, ainsi que de nouvelles techniques pour révéler leur contenu. Nous montrerons également qu’elles peuvent être utilisées afin d’améliorer la modélisation d’Internet. Pour terminer, ces techniques seront intégrées à Trace the Naughty Tunnels (TNT), une extension de traceroute qui permet d’identifier tous les types de tunnels MPLS le long du chemin vers une destination. Nous prouverons que cet outil peut être utilisé pour obtenir des statistiques détaillées sur le déploiement d’MPLS sur Internet. TNT est disponible publiquement, et peut donc être librement exploité par la communauté de la recherche pour de multiples futures études