Change Management Systems for Seamless Evolution in Data Centers

Revenue for data centers today is highly dependent on the satisfaction of their enterprise customers. These customers often require various features to migrate their businesses and operations to the cloud. Thus, clouds today introduce new features at a swift pace to onboard new customers and to meet the needs of existing ones. This pace of innovation continues to grow on super linearly, e.g., Amazon deployed 1400 new features in 2017. However, such a rapid pace of evolution adds complexities both for users and the cloud. Clouds struggle to keep up with the deployment speed, and users struggle to learn which features they need and how to use them. The pace of these evolutions has brought us to a tipping point: we can no longer use rules of thumb to deploy new features, and customers need help to identify which features they need. We have built two systems: Janus and Cherrypick, to address these problems. Janus helps data center operators roll out new changes to the data center network. It automatically adapts to the data center topology, routing, traffic, and failure settings. The system reduces the risk of new deployments for network operators as they can now pick deployment strategies which are less likely to impact users’ performance. Cherrypick finds near-optimal cloud configurations for big data analytics. It adapts allows users to search through the new machine types the clouds are constantly introducing and find ones with a near-optimal performance that meets their budget. Cherrypick can adapt to new big-data frameworks and applications as well as the new machine types the clouds are constantly introducing. As the pace of cloud innovations increases, it is critical to have tools that allow operators to deploy new changes as well as those that would enable users to adapt to achieve good performance at low cost. The tools and algorithms discussed in this thesis help accomplish these goals

Stardust: Divide and conquer in the data center network

Building scalable data centers, and network devices that fit within these data centers, becomes increasingly hard. With modern switches pushing at the boundary of manufacturing feasibility, being able to build simple, and scalable network fabrics is of utmost importance in data centers. We introduce Stardust: a scalable fabric architecture for data center networks, inspired by network-switch system and scaled up to data center scale. Stardust combines packet switches at the edge and disaggregated cell switches at the network fabric, using scheduled traffic. Stardust is a scalable, distributed solution, attending to the limitations of network-switch design, while offering improved performance and power savings compared with traditional solutions. With networking requirements ever increasing, Stardust predicts the elimination of packet switches, replaced by cell switches in the network, and smart network interface cards at the edges.Leverhulme Trust Isaac Newton Trus

DESIGN OF EFFICIENT PACKET MARKING-BASED CONGESTION MANAGEMENT TECHNIQUES FOR CLUSTER INTERCONNECTS

El crecimiento de los computadores paralelos basados en redes de altas prestaciones ha aumentado el interés y esfuerzo de la comunidad investigadora en desarrollar nuevas técnicas que permitan obtener el mejor rendimiento de estas redes. En particular, el desarrollo de nuevas técnicas que permitan un encaminamiento eficiente y que reduzcan la latencia de los paquetes, aumentando así la productividad de la red. Sin embargo, una alta tasa de utilización de la red podría conllevar el que se conoce como "congestión de red", el cual puede causar una degradación del rendimiento. El control de la congestión en redes multietapa es un problema importante que no está completamente resuelto. Con el fin de evitar la degradación del rendimiento de la red cuando aparece congestión, se han propuesto diferentes mecanismos para el control de la congestión. Muchos de estos mecanismos están basados en notificación explícita de la congestión. Para este propósito, los switches detectan congestión y dependiendo de la estrategia aplicada, los paquetes son marcados con la finalidad de advertir a los nodos origenes. Como respuesta, los nodos origenes aplican acciones correctivas para ajustar su tasa de inyección de paquetes. El propósito de esta tesis es analizar las diferentes estratégias de detección y corrección de la congestión en redes multietapa, y proponer nuevos mecanismos de control de la congestión encaminados a este tipo de redes sin descarte de paquetes. Las nuevas propuestas están basadas en una estrategia más refinada de marcaje de paquetes en combinación con un conjunto de acciones correctivas justas que harán al mecanismo capaz de controlar la congestión de manera efectiva con independencia del grado de congestión y de las condiciones de tráfico.Ferrer Pérez, JL. (2012). DESIGN OF EFFICIENT PACKET MARKING-BASED CONGESTION MANAGEMENT TECHNIQUES FOR CLUSTER INTERCONNECTS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18197Palanci

Facilitating dynamic network control with software-defined networking

This dissertation starts by realizing that network management is a very complex and error-prone task. The major causes are identified through interviews and systematic analysis of network config- uration data on two large campus networks. This dissertation finds that network events and dynamic reactions to them should be programmatically encoded in the network control program by opera- tors, and some events should be automatically handled for them if the desired reaction is general. This dissertation presents two new solutions for managing and configuring networks using Software- Defined Networking (SDN) paradigm: Kinetic and Coronet. Kinetic is a programming language and central control platform that allows operators to implement traffic control application that reacts to various kinds of network events in a concise, intuitive way. The event-reaction logic is checked for correction before deployment to prevent misconfigurations. Coronet is a data-plane failure recovery service for arbitrary SDN control applications. Coronet pre-plans primary and backup routing paths for any given topology. Such pre-planning guarantees that Coronet can perform fast recovery when there is failure. Multiple techniques are used to ensure that the solution scales to large networks with more than 100 switches. Performance and usability evaluations show that both solutions are feasible and are great alternative solutions to current mechanisms to reduce misconfigurations.Ph.D

Causal impacts of transport interventions on air quality

The transport sector is one of the main sources of air pollution emissions, particularly for carbon monoxide, nitrogen oxides, and particulate matter. Evaluating the effectiveness of transport interventions on improving air quality is essential to informing future policy. However, a comparison of air quality observations before and after an intervention can be biased by various factors, such as weather conditions and seasonality effects. Causal inference methods generally have advantages in intervention evaluation in terms of data requirements, model building, and the interpretation of effect estimates. Causality goes beyond statistical association in the sense that it seeks to measure the net effect of an intervention on an outcome through all possible pathways directing from the intervention to the outcome. Causal inference methods have been applied to address the same question, however, the important confounders (such as weather conditions) are commonly controlled for by including variables in the causal inference model and assuming a parametric relationship. The thesis focuses on understanding the causal impacts of transport interventions on air quality. A novel ex-post policy evaluation framework, combining meteorological normalisation, change point detection, and causal inferencing, is proposed to overcome the limitations of previous approaches, and it is applied to three distinct transport interventions: improving public transport supply (Jubilee Line Extension), tightening road traffic emission standards (London Ultra Low Emission Zone), and restricting both transport activities and supply (COVID-19 lockdown). The Jubilee Line extension led to only small (< 1%) or insignificant changes in air pollution on average in London. The Ultra Low Emission Zone showed an average reduction of less than 3% for NO2 concentrations and insignificant effects on O3 and PM2.5 concentrations. The lockdown reduced the NO2 concentrations in London by less than 12% on average, and it had an insignificant effect on O3, PM10, and PM2.5. Therefore, the empirical results of the thesis consistently highlight the necessity of a multi-faceted set of policies that aim to reduce emissions across sectors with coordination among local, regional, and national government in order to achieve long-term improvements in air quality in cities.Open Acces

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Enabling Human Centric Smart Campuses via Edge Computing and Connected Objects

Early definitions of Smart Building focused almost entirely on the technology aspect and did not suggest user interaction at all. Indeed, today we would attribute it more to the concept of the automated building. In this sense, control of comfort conditions inside buildings is a problem that is being well investigated, since it has a direct effect on users’ productivity and an indirect effect on energy saving. Therefore, from the users’ perspective, a typical environment can be considered comfortable, if it’s capable of providing adequate thermal comfort, visual comfort and indoor air quality conditions and acoustic comfort. In the last years, the scientific community has dealt with many challenges, especially from a technological point of view. For instance, smart sensing devices, the internet, and communication technologies have enabled a new paradigm called Edge computing that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. This has allowed us to improve services, sustainability and decision making. Many solutions have been implemented such as smart classrooms, controlling the thermal condition of the building, monitoring HVAC data for energy-efficient of the campus and so forth. Though these projects provide to the realization of smart campus, a framework for smart campus is yet to be determined. These new technologies have also introduced new research challenges: within this thesis work, some of the principal open challenges will be faced, proposing a new conceptual framework, technologies and tools to move forward the actual implementation of smart campuses. Keeping in mind, several problems known in the literature have been investigated: the occupancy detection, noise monitoring for acoustic comfort, context awareness inside the building, wayfinding indoor, strategic deployment for air quality and books preserving

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others