Teleoperation of passivity-based model reference robust control over the internet

This dissertation offers a survey of a known theoretical approach and novel experimental results in establishing a live communication medium through the internet to host a virtual communication environment for use in Passivity-Based Model Reference Robust Control systems with delays. The controller which is used as a carrier to support a robust communication between input-to-state stability is designed as a control strategy that passively compensates for position errors that arise during contact tasks and strives to achieve delay-independent stability for controlling of aircrafts or other mobile objects. Furthermore the controller is used for nonlinear systems, coordination of multiple agents, bilateral teleoperation, and collision avoidance thus maintaining a communication link with an upper bound of constant delay is crucial for robustness and stability of the overall system. For utilizing such framework an elucidation can be formulated by preparing site survey for analyzing not only the geographical distances separating the nodes in which the teleoperation will occur but also the communication parameters that define the virtual topography that the data will travel through. This survey will first define the feasibility of the overall operation since the teleoperation will be used to sustain a delay based controller over the internet thus obtaining a hypothetical upper bound for the delay via site survey is crucial not only for the communication system but also the delay is required for the design of the passivity-based model reference robust control. Following delay calculation and measurement via site survey, bandwidth tests for unidirectional and bidirectional communication is inspected to ensure that the speed is viable to maintain a real-time connection. Furthermore from obtaining the results it becomes crucial to measure the consistency of the delay throughout a sampled period to guarantee that the upper bound is not breached at any point within the communication to jeopardize the robustness of the controller. Following delay analysis a geographical and topological overview of the communication is also briefly examined via a trace-route to understand the underlying nodes and their contribution to the delay and round-trip consistency. To accommodate the communication channel for the controller the input and output data from both nodes need to be encapsulated within a transmission control protocol via a multithreaded design of a robust program within the C language. The program will construct a multithreaded client-server relationship in which the control data is transmitted. For added stability and higher level of security the channel is then encapsulated via an internet protocol security by utilizing a protocol suite for protecting the communication by authentication and encrypting each packet of the session using negotiation of cryptographic keys during each session

Reshaping the African Internet: From scattered islands to a connected continent

There is an increasing awareness amongst developing regions on the importance of localizing Internet traffic in the quest for fast, affordable, and available Internet access. In this paper, we focus on Africa, where 37 IXPs are currently interconnecting local ISPs, but mostly at the country level. An option to enrich connectivity on the continent and incentivize content providers to establish presence in the region is to interconnect ISPs present at isolated IXPs by creating a distributed IXP layout spanning the continent. The goal of this paper is to investigate whether such IXP interconnection would be possible, and if successful, to estimate the best-case benefits that could be realized in terms of traffic localization and performance. Our hope is that quantitatively demonstrating the benefits will provide incentives for ISPs to intensify their peering relationships in the region. However, it is challenging to estimate this best-case scenario, due to numerous economic, political, and geographical factors influencing the region. Towards this end, we begin with a thorough analysis of the environment in Africa. We then investigate a naive approach to IXP interconnection, which shows that a theoretically optimal solution would be infeasible in practice due to the prevailing socio-economic conditions in the region. We therefore provide an innovative, realistic four-step interconnection scheme to achieve the distributed IXP layout that considers and parameterizes external socio-economic factors using publicly available datasets. We demonstrate that our constrained solution doubles the percentage of continental intra-African paths, reduces their lengths, and drastically decreases the median of their RTTs as well as RTTs to ASes hosting the top 10 global and top 10 regional Alexa websites. Our approach highlights how, given real-world constraints, a solution requires careful considerations in order to be practically realizable.Rodérick Fanou was partially supported by IMDEA Networks Institute, US NSF grant CNS-1414177, and the project BRADE (P2013/ICE-2958) from the Directorate General of Universities and Research, Board of Education, Madrid Regional Governement. Francisco Valera was partially funded by the European Commission under FP7 project LEONE (FP7-317647). Amogh Dhamdhere was partially funded by US NSF grants CNS-1414177 and CNS-1513847.Publicad

Machine Learning and Big Data Methodologies for Network Traffic Monitoring

Over the past 20 years, the Internet saw an exponential grown of traffic, users, services and applications. Currently, it is estimated that the Internet is used everyday by more than 3.6 billions users, who generate 20 TB of traffic per second. Such a huge amount of data challenge network managers and analysts to understand how the network is performing, how users are accessing resources, how to properly control and manage the infrastructure, and how to detect possible threats. Along with mathematical, statistical, and set theory methodologies machine learning and big data approaches have emerged to build systems that aim at automatically extracting information from the raw data that the network monitoring infrastructures offer. In this thesis I will address different network monitoring solutions, evaluating several methodologies and scenarios. I will show how following a common workflow, it is possible to exploit mathematical, statistical, set theory, and machine learning methodologies to extract meaningful information from the raw data. Particular attention will be given to machine learning and big data methodologies such as DBSCAN, and the Apache Spark big data framework. The results show that despite being able to take advantage of mathematical, statistical, and set theory tools to characterize a problem, machine learning methodologies are very useful to discover hidden information about the raw data. Using DBSCAN clustering algorithm, I will show how to use YouLighter, an unsupervised methodology to group caches serving YouTube traffic into edge-nodes, and latter by using the notion of Pattern Dissimilarity, how to identify changes in their usage over time. By using YouLighter over 10-month long races, I will pinpoint sudden changes in the YouTube edge-nodes usage, changes that also impair the end users’ Quality of Experience. I will also apply DBSCAN in the deployment of SeLINA, a self-tuning tool implemented in the Apache Spark big data framework to autonomously extract knowledge from network traffic measurements. By using SeLINA, I will show how to automatically detect the changes of the YouTube CDN previously highlighted by YouLighter. Along with these machine learning studies, I will show how to use mathematical and set theory methodologies to investigate the browsing habits of Internauts. By using a two weeks dataset, I will show how over this period, the Internauts continue discovering new websites. Moreover, I will show that by using only DNS information to build a profile, it is hard to build a reliable profiler. Instead, by exploiting mathematical and statistical tools, I will show how to characterize Anycast-enabled CDNs (A-CDNs). I will show that A-CDNs are widely used either for stateless and stateful services. That A-CDNs are quite popular, as, more than 50% of web users contact an A-CDN every day. And that, stateful services, can benefit of A-CDNs, since their paths are very stable over time, as demonstrated by the presence of only a few anomalies in their Round Trip Time. Finally, I will conclude by showing how I used BGPStream an open-source software framework for the analysis of both historical and real-time Border Gateway Protocol (BGP) measurement data. By using BGPStream in real-time mode I will show how I detected a Multiple Origin AS (MOAS) event, and how I studies the black-holing community propagation, showing the effect of this community in the network. Then, by using BGPStream in historical mode, and the Apache Spark big data framework over 16 years of data, I will show different results such as the continuous growth of IPv4 prefixes, and the growth of MOAS events over time. All these studies have the aim of showing how monitoring is a fundamental task in different scenarios. In particular, highlighting the importance of machine learning and of big data methodologies