On the Potential of Flow-Based Routing in Multihomed Environments

The data rates provisioned by broadband Internet access connections continue to fall short of the requirements posed by emerging applications. Yet the potential of statistical multiplexing of the last mile broadband connections remains unexploited even as the average utilization of these connections remains low. Despite recent work in this area [15, 20], two key questions remain unanswered: a) What is the attainable benefit of broadband access sharing? and b) How much of this benefit is realizable given real-world constraints? In this work we quantify the attainable benefit of a multihomed broadband access environment by proposing and evaluating several flow-based access sharing policies using a custom flow-based simulator. We then analyze how much of the performance benefit is lost due to real-world constraints by migrating from simulations to a test-lab environment employing a wireless network. Our results show that in today’s broadband Internet access scenarios, a significant reduction in download times by up to a factor of 3 is achievable

The Digital Earth Observation Librarian: A Data Mining Approach for Large Satellite Images Archives

Throughout the years, various Earth Observation (EO) satellites have generated huge amounts of data. The extraction of latent information in the data repositories is not a trivial task. New methodologies and tools, being capable of handling the size, complexity and variety of data, are required. Data scientists require support for the data manipulation, labeling and information extraction processes. This paper presents our Earth Observation Image Librarian (EOLib), a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data, in general. The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments (PGS). EOLib is composed of several modules which offer functionalities such as data ingestion, feature extraction from SAR (Synthetic Aperture Radar) data, meta-data extraction, semantic definition of the image content through machine learning and data mining methods, advanced querying of the image archives based on content, meta-data and semantic categories, as well as 3-D visualization of the processed images. EOLib is operated by DLR’s (German Aerospace Center’s) Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen, Germany

Flow-basiertes Routing in Community Networks

Die von Internet-Providern Endbenutzern angebotenen Transferraten sind oft zu klein für den Bedarf moderner Applikationen, besonders in Spitzenlastsituationen. Obwohl die durchschnittliche Auslastung dieser Endbenutzer-Anschlüsse meist niedrig ist, bleibt das Potenzial der statistischen Verkehrsbündelung über die letzte Meile von Bandbreitenverbindungen weitgehend unbenutzt. Wir führen in dieser Arbeit eine kollaborative, Flow-basierte Infrastruktur für das gemeinsame Nutzen von Internetverbindungen in privaten Communities vor und untersuchen ihre Auswirkungen. Unsere Ergebnisse zeigen, dass Multihoming und Flow-basiertes Routing in aktuellen Zugangsszenarien mit Bandbreitenverbindungen signifikante Durchsatzverbesserungen sowie die Verkürzung von Downloadzeiten erlauben. Der Umfang des Gewinns ist von der Natur und die Menge des Datenverkehrs abhängig. Die Verbesserungen unter Last und mit vielen parallelen großen Übertragungen sind höher als die unter weniger Last und mit kurzen Übertragungen. Wir beginnen mit einer Beschreibung unserer Architektur und deren Einsatz auf Client-Rechnern und DSL-Routern. Wir stellen unsere Experimentier-Umgebung vor: einen Fluss-basierten Off-line-TCP-Simulator und eine realistische Testumgebung. Wir benutzen mehrere Verkehrsmuster: Flow Traces aus reellen Netzwerken, künstliche Traces, die auf statistischen Distributionen basieren, künstliche Web-Verkehrsmuster und eine Peer-To-Peer Applikation. Als nächstes führen wir eine Methodik für die Bewertung von Flow-Routing ein. Dafür vergleichen wir die erreichten Übertragungszeitungen für Flows unter verschiedenen Routingalgorithmen und Netzwerkkonfigurationen. Wir setzen die Ergebnisse in Beziehung zu verschiedenen Klassen von Flows und zur aktuellen Lastsituation. Wir untersuchen die Kapazität unseres Systems und die mögliche Leistung mit idealisierten, allwissenden Routing-Algorithmen. Als nächstes untersuchen wir mehrere Routing-Algorithmen, die auf Metriken wie Überlastung oder Anzahl von aktiven Flows basieren. Zum Abschluss untersuchen wir die Auswirkung der Benutzung eines Funknetzwerks für die Weiterleitung der Flows zwischen den Breitbandanschlüssen.Data rates provisioned by broadband Internet Service Providers continue to fall short of the requirements posed by emerging applications. However, the potential of statistical multiplexing of the last mile broadband connections remains unexploited even as the average utilization of these connections remains low. In this work we propose and evaluate a collaborative flow-based access sharing infrastructure in community networks. Our results show that with multi-homing and flow-based routing in today's broadband Internet access scenarios, significant performance benefits including a reduction in download times are achievable. The extent of the benefit largely depends on the nature and volume of traffic: under high load and with bulky transfers the achievable improvements are higher than those realizable under low load scenarios with short-lived flows. We start out by introducing the architecture of our system and describing deployments on the client systems and the DSL routers. We introduce our experimental setups: an off-line fluid TCP simulator and a realistic testbed. We use several traffic workloads, including real-world flow traces, artificial flow traces generated according to statistic distributions, artificial Web workloads, and a peer-to-peer application. We then introduce a methodology for evaluating the benefits of flow routing by comparing the flow durations with different routing policies and network setups. This is done for different classes of flows, in relation to the current load in the system. We investigate the capacity of our system and the possible performance with ideal, omniscient routing algorithms. We then evaluate several routing policies based on routing metrics like congestion or the number of active flows in the system. Finally, we study the impact of employing a wireless network for flow redirection amongst the broadband connections

Architecture Concept for an Information Mining System for Earth Observation Data

EOLib, the Earth Observation Image Librarian is an upcoming Image Information Mining (IIM) system for earth observation (EO) products. As integral part of a payload ground segment (PGS) it operates on the original EO products, metadata, and on computed higher level abstractions including basic features and semantic annotations of product tiles. The core goal of EOLib is to introduce mature information mining functions in existing EO payload ground segments. EOLib is integrated with the multi-mission PGS existing at DLR’s premises. Operations including product tiling, feature extraction and automatic annotation are performed within the PGS services infrastructure. Intermediate data including features and quick look images are forwarded to the EOLib core to perform additional data mining operations as: semantic learning, content-based information retrieval and visual data mining. The PGS user services are augmented with query engines for semantic annotations and metadata and with a semantic catalogue browser. We present in this paper the architecture concept of the EOLib system. It starts with a set of functional requirements, the constraints imposed by the existing PGS and the experience gathered from prototype standalone IIM systems. System components are subsequently identified based on logical functionality blocks. Data flows and interfaces are built in order to allow simple, clear system integration and to maximize performance. We conclude with the implementation of a few use cases

Reporting in a Payload Data Ground Segment

This paper describes the concept and implementation of the DIMS Reporting Control (RC) for the multi-mission PDGS environment. The tool compiles reports based on data collected from multiple components. It generates ad-hoc and scheduled reports and employs statistical analyses to depict the current system state or its evolution over time. This paper introduces DIMS reporting requirements, followed by a discussion on how these drive design decisions. Then involved interfaces and tools are presented. Next the proposed system is compared to existing solutions. The publication concludes with an evaluation of the DIMS Reporting Control implementation from the perspectives of usability and performance

Automated Urban Mapping in a Satellite Ground Segment

We demonstarte how a satellite ground segmant can be designed to allow automated urban mapping based on satellite images

EOLib: An Image Information Mining Project

The abundance of available satellite images calls for their automated analysis and interpretation, including the semantic annotation of discovered objects as well as the monitoring of changes within image time series. A common approach is to cut large satellite image into contiguous patches and to classify each patch separately by attaching a semantic patch content label to it. In this context, the selected patch size is a critical parameter, as patches being too large may contain multiple objects and patches being too small may not be understandable due to missing contextual information. This approach has been embedded into an interactive active learning and exploitation environment within the ESA-funded EOLib project. The software of EOLib allows automated image data ingestion, feature extraction, and semantic image content annotation supported by interactive visualization tools. We report about our experiences with medium and high resolution Synthetic Aperture Radar (SAR) and optical multispectral image classification when using such an active learning approach. The most important phenomenon is the impact of image resolution. The higher the resolution, the higher the number of discernible land cover categories, in particular for built-up areas and industrial sites where we can see and interpret the impact of distinct human-made activities. Here, the discernible land cover categories depend on the actual image resolution. This becomes apparent when we compare the same target areas acquired by different space-borne SAR sensors (e.g., Sentinel-1A versus TerraSAR-X). In addition, it turns out that several country-specific regional surface cover categories can be trained and retrieved with SAR images that often appear differently in optical satellite images; however, any increase in classification accuracy has to be paid for by higher computational effort. Thus, EOLib represents an approach for future ground segments whose functionality will no longer be limited to the mere generation of level 1,2, or 3 products, but will include automated and user-friendly image content analysis and annotation

Data Mining and Knowledge Discovery for the TerraSAR-X Payload Ground Segment

Earth Observation (EO) imaging satellites continuously acquire huge volumes of high resolution scenes thus increasing the size of image archives and the variety and complexity of EO image content. Therefore, new methodologies and tools that allow the end-user to access large image repositories, to dynamically find and retrieve collections of desired images, and to extract and infer knowledge about the patterns hidden in the image archives are required. In this context, this paper presents the Earth Observation Image Librarian (EOLib), which is a modular system offering data mining and knowledge discovery functionality for the TerraSAR-X Payload Ground Segment and is serving to setup the next generation of Image Information Mining (IIM) systems. It implements novel techniques for image content exploration and exploitation. The main goal of EOLib is to create a communication channel between Payload Ground Segments and the end-user who receives the image content enriched with annotations and metadata as well as coded data in an understandable format associated with semantic categories being ready for immediate exploitation. EOLib is composed of several components offering new functionality such as ingestion and feature extraction from SAR images, metadata extraction, semantic definition of the image content based on machine learning and data mining methods, advanced querying of the image archives utilizing data content, metadata and semantic categories, as well as 3D visualization of the huge and complex image archives. EOLib will be interfaced to and operated in DLR’s Multi-Mission Payload Ground Segment (PGS) of the Remote Sensing Data Center at Oberpfaffenhofen, representing at the same time a general new concept for the operations of Ground Segment infrastructures