Towards Autonomous Computer Networks in Support of Critical Systems

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Large-Scale Measurements and Prediction of DC-WAN Traffic

Large cloud service providers have built an increasing number of geo-distributed data centers (DCs) connected by Wide Area Networks (WANs). These DC-WANs carry both high-priority traffic from interactive services and low-priority traffic from bulk transfers. Given that a DC-WAN is an expensive resource, providers often manage it via traffic engineering algorithms that rely on accurate predictions of inter-DC high-priority (delay-sensitive) traffic. In this article, we perform a large-scale measurement study of high-priority inter-DC traffic from Baidu. We measure how inter-DC traffic varies across their global DC-WAN and show that most existing traffic prediction methods either cannot capture the complex traffic dynamics or overlook traffic interrelations among DCs. Building on our measurements, we propose the In terrelated- Te mporal G raph Convolutional Net work (IntegNet) model for inter-DC traffic prediction. In contrast to prior efforts, our model exploits both temporal traffic patterns and inferred co-dependencies between DC pairs. IntegNet forecasts the capacity needed for high-priority traffic demands by accounting for the balance between resource provisioning (i.e., allocating resources exceeding actual demand) and QoS losses (i.e., allocating fewer resources than actual demand). Our experiments show that IntegNet can keep a very limited QoS loss, while also reducing overprovisioning by up to 42.1% compared to the state-of-the-art and up to 66.2% compared to the traditional method used in DC-WAN traffic engineering

Towards a User-Oriented Benchmark for Transport Protocols Comparison in very High Speed Networks

Standard TCP faces some performance limitations in very high speed wide area networks, mainly due to a long end-to-end feedback loop and a conservative behaviour with respect to congestion. Many TCP variants have been proposed to overcome these limitations. However, TCP is a complex protocol with many user-configurable parameters and a range of different implementations. It is then important to define measurement methods so that the transport services and protocols can evolve guided by scientific principles and compared quantitatively. The goal of this report is to present some steps towards a user-oriented benchmark, called ITB, for high speed transport protocols comparison. We first present and analyse some results reported in the literature. From this study we identify classes of representative applications and useful metrics. We then isolate infrastructure parameters and traffic factors which influence the protocol behaviour. This enable us to define scenario capturing and synthesising comprehensive and useful properties. We finally illustrate this proposal by preliminary results obtained on our experimental environment, Grid'5000, we have built and are using for contributing in this benchmark design

Load shedding in network monitoring applications

Monitoring and mining real-time network data streams are crucial operations for managing and operating data networks. The information that network operators desire to extract from the network traffic is of different size, granularity and accuracy depending on the measurement task (e.g., relevant data for capacity planning and intrusion detection are very different). To satisfy these different demands, a new class of monitoring systems is emerging to handle multiple and arbitrary monitoring applications. Such systems must inevitably cope with the effects of continuous overload situations due to the large volumes, high data rates and bursty nature of the network traffic. These overload situations can severely compromise the accuracy and effectiveness of monitoring systems, when their results are most valuable to network operators. In this thesis, we propose a technique called load shedding as an effective and low-cost alternative to over-provisioning in network monitoring systems. It allows these systems to handle efficiently overload situations in the presence of multiple, arbitrary and competing monitoring applications. We present the design and evaluation of a predictive load shedding scheme that can shed excess load in front of extreme traffic conditions and maintain the accuracy of the monitoring applications within bounds defined by end users, while assuring a fair allocation of computing resources to non-cooperative applications. The main novelty of our scheme is that it considers monitoring applications as black boxes, with arbitrary (and highly variable) input traffic and processing cost. Without any explicit knowledge of the application internals, the proposed scheme extracts a set of features from the traffic streams to build an on-line prediction model of the resource requirements of each monitoring application, which is used to anticipate overload situations and control the overall resource usage by sampling the input packet streams. This way, the monitoring system preserves a high degree of flexibility, increasing the range of applications and network scenarios where it can be used. Since not all monitoring applications are robust against sampling, we then extend our load shedding scheme to support custom load shedding methods defined by end users, in order to provide a generic solution for arbitrary monitoring applications. Our scheme allows the monitoring system to safely delegate the task of shedding excess load to the applications and still guarantee fairness of service with non-cooperative users. We implemented our load shedding scheme in an existing network monitoring system and deployed it in a research ISP network. We present experimental evidence of the performance and robustness of our system with several concurrent monitoring applications during long-lived executions and using real-world traffic traces.Postprint (published version

Data and computer center prediction of usage and cost: an interpretable machine learning approach

In recent years, Cloud computing usage has considerably increased and, nowadays, it is the backbone of many emerging applications. However, behind cloud structures, we have physical infrastructures (data centers) for which managing is difficult due to un- predictable utilization patterns. To address the constraints of reactive auto-scaling, data centers are widely adopting predictive cloud resource management mechanisms. How- ever, predictive methods rely on application workloads and are typically pre-optimized for specific patterns, which can cause under/over-provisioning of resources. Accurate workload forecasts are necessary to gain efficiency, save money, and provide clients with better and faster services. Working with real data from a Portuguese bank, we propose Ensemble Adaptive Model with Drift detector (EAMDrift). This novel method combines forecasts from multi- ple individual predictors by giving weights to each individual model prediction according to a performance metric. EAMDrift automatically retrains when needed and identifies the most appropriate models to use at each moment through interpretable mechanisms. We tested our novel methodology in a real data problem, by studying the influence of external signals (mass and social media) on data center workloads. As we are working with real data from a bank, we hypothesize that users can increase or decrease the usage of some applications depending on external factors such as controversies or news about economics. For this study, EAMDrift was projected to allow multiple past covariates. We evaluated EAMDrift in different workloads and compared the results with sev- eral baseline methods models. The experimental evaluation shows that EAMDrift out- performs individual baseline models in 15% to 25%. Compared to the best black-box ensemble model, our model has a comparable error (increased in 1-3%). Thus, this work suggests that interpretable models are a viable solution for data center workload predic- tion.Nos últimos anos, a computação em nuvem tem tido um aumento considerável e, hoje pode ser vista como a espinha dorsal de muitas aplicações que estão a emergir. Contudo, por detrás das conhecidas nuvens, existem estruturas físicas (centro de dados) nas quais, a gestão tem se revelado uma tarefa bastante difícil devido à imprevisibilidade de utilização dos serviços. Para lidar com as restrições do auto-scalling reativo, os mecanismos de gestão dos centros de dados começaram a adotar algoritmos preditivos. No entanto, os algoritmos preditivos são treinados com base nas cargas de utilização das aplicações e geralmente não estão otimizados para todos os padrões, causando sub/sobre provisionamento dos recursos. Através da utilização de dados reais do centro de dados de um banco português, pro- pomos o Ensemble Adaptive Model with Drift detector (EAMDrift). Este novo método combina previsões de vários modelos individuais através de uma métrica de desempe- nho. O EAMDrift possui mecanismos interpretáveis que permitem detetar os melhores modelos em cada previsão, bem como detetar momentos para ser retreinado. A nossa metodologia foi testada num problema com dados reais, e foi estudada a influência de fatores externos (notícias relacionadas com o banco) com a sua utilização. Sendo estes dados de um banco, é possível que os utilizadores aumentem ou diminuam o uso de algumas aplicações com base em fatores externos (polêmicas ou notícias sobre economia). Para isto, o EAMDrift permite o uso de outras variáveis (covariadas). O modelo proposto neste trabalho foi avaliado em diferentes conjuntos de dados e os resultados foram comparados entre vários modelos de base. O EAMDrift superou todos os modelos de base em 15% a 25%. Quando comparado com o melhor modelo que também combina várias previsões mas de forma não interpretável, o nosso modelo obteve um erro comparável (maior em 1 a 3%). Assim, este trabalho sugere que modelos interpretáveis podem ser uma solução viável para a gestão dos centros de dados

The interplay of the physical landscape and social dynamics in shaping movement of African savanna elephants (loxodonta africana)

Free ranging African savanna elephants (Loxodonta africana) are increasingly impacted by human-induced habitat loss and poaching for ivory. Because elephants live in tightly knit groups, this combination of threats not only reduces the size of their populations but also degrades their social interactions. Long-term relationships with socially competent individuals, such as experienced seniors, benefit the ability of other group members to access limiting resources and avoid danger. Understanding how anthropogenic pressure may affect persistence of elephant populations is important, because elephants are an economically important keystone species. This doctoral thesis characterizes how individual elephants influence the movement of their social partners, and how the social network properties of elephant groups related to information sharing may change when socially competent members are killed by poachers. To that end, two techniques commonly used to study movement of individuals in their habitat, and one used to study the consequences of repeated social interactions, are modified and extended to incorporate a number of the social processes typically found in groups of elephants. First, an established, choice-based statistical framework for movement analysis is modified and validated using synthetic and empirical data. It allows for simultaneous modeling of the effects of the habitat quality and social interactions on individual movement choices. Next, this new model is applied to a unique set of remotely sensed tracks from five male elephants navigating across the same habitat in southern Africa. A key result is that known dominance relationships observed at water points and other gathering places are determined to persist even when elephants are ranging more widely across the landscape. Lastly, an existing \u27social network and poaching\u27 simulation model is parameterized with data from wild elephants. It reveals debilitating effects of poaching on various network metrics thought to correlate with group communication efficiency. The modeling and simulation tools developed over the course of this doctoral research may be generalized to include the influence of \u27dynamic points\u27 other than social conspecifics, such as predators or poachers, on long-term movement patterns, and thus may provide a tool to both understand and mitigate human-wildlife conflict. In addition, they may aid hypothesis testing about disturbance of social dynamics in animal systems subject to exploitation by humans or lethal management

Taking “Fun and Games” Seriously: Proposing the Hedonic-Motivation System Adoption Model (HMSAM)

Hedonic-motivation systems (HMS)—systems used primarily to fulfill users’ intrinsic motivations—are the elephant in the room for IS research. Growth in HMS sales has outperformed utilitarian-motivation systems (UMS) sales for more than a decade, generating billions in revenue annually; yet IS research focuses mostly on UMS. In this study, we explain the role of intrinsic motivations in systems use and propose the hedonic-motivation system adoption model (HMSAM) to improve the understanding of HMS adoption. Instead of a minor, general TAM extension, HMSAM is an HMS-specific system acceptance model based on an alternative theoretical perspective, which is in turn grounded in flow-based cognitive absorption (CA). The HMSAM extends van der Heijden’s (2004) model of hedonic system adoption by including CA as a key mediator of perceived ease of use (PEOU) and of behavioral intentions to use (BIU) hedonic-motivation systems. Results from experiments involving 665 participants confirm that, in a hedonic context, CA is a more powerful and appropriate predictor of BIU than PEOU or joy, and that the effect of PEOU on BIU is fully mediated by CA sub-constructs. This study lays a foundation, provides guidance, and opens up avenues for future HMS, UMS, and mixed-motivation system research

Understanding User Engagement in the Open Collaboration Model of Crowdsourcing

Crowdsourcing refers to the use of technologies to gather the collective effort and wisdom from an undefined group of online users for organizational innovation and/or problem solving. Further, open collaboration model refers to the crowdsourcing type wherein the crowd members discuss the submitted contributions among themselves to provide the final outcomes to problem owners. Regardless of crowdsourcing forms, a critical challenge for crowdsourcing service providers is to engage online participants in making sustained contributions. Inspired by Flow Theory (Csikszentmihalyi & Csikszentmihayi, 1988), the purpose of this dissertation is to examine whether the conditions of challenge-skill balance and clear and immediate feedback invoke the flow state, specifically an absorbed and enjoyable experience, and consequently make Internet users more engaged in the open collaboration events. The proposed relationships were tested through lab experiment, with the flow state being measured through both self-report survey and eye-tracking. As for the results, I found that perceived challenge-skill balance and perceived feedback were associated with the invocation of fun, but not the holistic flow experience in the brainstorming task. Moreover, fun was also found to positively associate with the indicators of the intensity and sustainability of user engagement. I also identified some exploratory ocular patterns of participants when they enjoyed the task at hand