518,594 research outputs found

    The Beginnings and Prospective Ending of “End-to-End”: An Evolutionary Perspective On the Internet’s Architecture

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    The technology of “the Internet” is not static. Although its “end-to- end” architecture has made this “connection-less” communications system readily “extensible,” and highly encouraging to innovation both in hardware and software applications, there are strong pressures for engineering changes. Some of these are wanted to support novel transport services (e.g. voice telephony, real-time video); others would address drawbacks that appeared with opening of the Internet to public and commercial traffic - e.g., the difficulties of blocking delivery of offensive content, suppressing malicious actions (e.g. “denial of service” attacks), pricing bandwidth usage to reduce congestion. The expected gains from making “improvements” in the core of the network should be weighed against the loss of the social and economic benefits that derive from the “end-to-end” architectural design. Even where technological “fixes” can be placed at the networks’ edges, the option remains to search for alternative, institutional mechanisms of governing conduct in cyberspace.

    ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning

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    Wide Area Networks (WAN) are a key infrastructure in today's society. During the last years, WANs have seen a considerable increase in network's traffic and network applications, imposing new requirements on existing network technologies (e.g., low latency and high throughput). Consequently, Internet Service Providers (ISP) are under pressure to ensure the customer's Quality of Service and fulfill Service Level Agreements. Network operators leverage Traffic Engineering (TE) techniques to efficiently manage network's resources. However, WAN's traffic can drastically change during time and the connectivity can be affected due to external factors (e.g., link failures). Therefore, TE solutions must be able to adapt to dynamic scenarios in real-time. In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process. In the first one, Enero leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. To enable efficient operation over dynamic network scenarios (e.g., when link failures occur), we integrated a Graph Neural Network into the DRL agent. In the second stage, Enero uses a Local Search algorithm to improve DRL's solution without adding computational overhead to the optimization process. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 seconds on average for topologies up to 100 edges.Comment: 12 pages, 9 figure

    An evaluation of the performance of two different global satellite navigation systems, Trimble’s CenterPoint RTX and a conventional network RTK system

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    In the middle of 2011 Trimble introduced the CenterPoint Real time extended (RTX) real-time positioning service providing centimetre accurate positions for real-time applications. This emerging technology means that a GNSS system can be used without reliance on an internet connection, and independent from a conventional base/rover set up, thus overcoming the limitations of the existing systems. A comprehensive literature search reveals a lack of testing in the Sothern Hemisphere to date. This new solution needs to be tested for accuracies and precisions that can be achieved by comparing against a conventional network RTK (NRTK) system and determine if there is any significant decrease in accuracy or precision over time. The tests were conducted on a high accuracy survey permanent mark which has known coordinate values. The testing included the data collection from both systems (RTX and NRTK) on the survey mark individually. Coordinates from each system were compared against the known coordinates to assess accuracy and precision. RTX failed to meet the meet the accuracies or precisions as stated in the manufacturers datasheets but the system’s precisions did increase over time. The benefit of this dissertation is to produce a reliable and current set of results in the Sothern Hemisphere and to assist the survey profession in understanding this new emerging technology

    Distributed Detection of DDoS Attacks During the Intermediate Phase Through Mobile Agents

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    A Distributed Denial of Service attack is a large-scale, coordinated attack on the availability of services of a victim system, launched indirectly through many compromised computers on the Internet. Intrusion detection systems are network security tools that process local audit data or monitor network traffic to search for specific patterns or certain deviations from expected behavior, which indicate malicious activities against the protected network. In this study, we propose distributed intrusion detection methods to detect Distributed Denial of Service attacks in a special dataset and test these methods in a simulated-real time environment, in which the mobile agents are synchronized with the timestamp stated in the dataset. All of our methods use the alarms generated by SNORT, a signature-based network intrusion detection system. We use mobile agents in our methods on the Jade platform in order to reduce network bandwidth usage and to decrease the dependency on the central unit for a higher reliability. The methods are compared based on reliability, network load and mean detection time values

    Knowledge Discovery and Management within Service Centers

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    These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

    Multi-Stage Search Architectures for Streaming Documents

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    The web is becoming more dynamic due to the increasing engagement and contribution of Internet users in the age of social media. A more dynamic web presents new challenges for web search--an important application of Information Retrieval (IR). A stream of new documents constantly flows into the web at a high rate, adding to the old content. In many cases, documents quickly lose their relevance. In these time-sensitive environments, finding relevant content in response to user queries requires a real-time search service; immediate availability of content for search and a fast ranking, which requires an optimized search architecture. These aspects of today's web are at odds with how academic IR researchers have traditionally viewed the web, as a collection of static documents. Moreover, search architectures have received little attention in the IR literature. Therefore, academic IR research, for the most part, does not provide a mechanism to efficiently handle a high-velocity stream of documents, nor does it facilitate real-time ranking. This dissertation addresses the aforementioned shortcomings. We present an efficient mech- anism to index a stream of documents, thereby enabling immediate availability of content. Our indexer works entirely in main memory and provides a mechanism to control inverted list con- tiguity, thereby enabling faster retrieval. Additionally, we consider document ranking with a machine-learned model, dubbed "Learning to Rank" (LTR), and introduce a novel multi-stage search architecture that enables fast retrieval and allows for more design flexibility. The stages of our architecture include candidate generation (top k retrieval), feature extraction, and docu- ment re-ranking. We compare this architecture with a traditional monolithic architecture where candidate generation and feature extraction occur together. As we lay out our architecture, we present optimizations to each stage to facilitate low-latency ranking. These optimizations include a fast approximate top k retrieval algorithm, document vectors for feature extraction, architecture- conscious implementations of tree ensembles for LTR using predication and vectorization, and algorithms to train tree-based LTR models that are fast to evaluate. We also study the efficiency- effectiveness tradeoffs of these techniques, and empirically evaluate our end-to-end architecture on microblog document collections. We show that our techniques improve efficiency without degrading quality

    Flight data monitoring/tracker system for search and rescue mission

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    Traditionally, Kalman Filter is used for the purpose of mixing several input signals and extracting a more reliable output, which greatly benefits aircraft navigation. This paper considers a fusion of four sensor systems: Global Positioning System (GPS), accelerometer, gyroscope and magnetometer. The resultant device, known as Starfish Main Tracking Unit (MTU), is a Flight Data Monitoring (FDM) / Tracking System equipment that uses General Packet Radio Service (GPRS) / Iridium / ICS (Internet Communications Services), which provides low cost telemetry as well as multiple solutions for global flight following and flight data transfer between aircraft and ground. Users from ground are able to monitor their fleet, configure their systems and also generate various flight reports from a single web-based interface, named the Starfish Fleet Management system. This developed system complements the Black Box by downloading limited aircraft data to the ground, provides real time tracking and assist in Search and Rescue (SAR) mission

    Smart Parking System

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    Master of ScienceDepartment of Computing and Information SciencesDaniel A. AndresenLocating a parking spot during peak hours in most populated areas like shopping malls, universities, exhibitions or convention centers is difficult for the drivers. The difficulty rises from not knowing where the available spots may be at that required time. Smart parking is a solution to metropolitan cities to reduce congestion, cut vehicle emission totals and save persons' time by helping them in finding a spot to park. Smart Parking is a parking system, usually a new one that is equipped with special structured devices (things) to detect the available parking slots at any parking area. This is an application based on Internet of Things (IoT) that in Real-Time environment have sensors and devices embedded into parking spaces, transmitting data on the occupancy status; and the vehicle drivers can search for parking availability using their mobile phones or any infotainment system that is attached to the vehicle. Hence the driver would know where there is an available spot to park his vehicle in less time, reducing the energy consumption and air pollution. The Client or the sensor posts the parking slot occupancy status to a web service URL. The Java based web service is built using Spring and Hibernate to connect to the backend system. The web service (.war) file is deployed on Apache Tomcat Server and the backend used is MySQL database
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