Toward incremental FIB aggregation with quick selections (FAQS)

Several approaches to mitigating the Forwarding Information Base (FIB) overflow problem were developed and software solutions using FIB aggregation are of particular interest. One of the greatest concerns to deploy these algorithms to real networks is their high running time and heavy computational overhead to handle thousands of FIB updates every second. In this work, we manage to use a single tree traversal to implement faster aggregation and update handling algorithm with much lower memory footprint than other existing work. We utilize 6-year realistic IPv4 and IPv6 routing tables from 2011 to 2016 to evaluate the performance of our algorithm with various metrics. To the best of our knowledge, it is the first time that IPv6 FIB aggregation has been performed. Our new solution is 2.53 and 1.75 times as fast as the-state-of-the-art FIB aggregation algorithm for IPv4 and IPv6 FIBs, respectively, while achieving a near-optimal FIB aggregation ratio

LAMP: Prompt Layer 7 Attack Mitigation with Programmable Data Planes

While there are various methods to detect application layer attacks or intrusion attempts on an individual end host, it is not efficient to provide all end hosts in the network with heavy-duty defense systems or software firewalls. In this work, we leverage a new concept of programmable data planes, to directly react on alerts raised by a victim and prevent further attacks on the whole network by blocking the attack at the network edge. We call our design LAMP, Layer 7 Attack Mitigation with Programmable data planes. We implemented LAMP using the P4 data plane programming language and evaluated its effectiveness and efficiency in the Behavioral Model (bmv2) environment

Toward a Programmable FIB Caching Architecture

The current Internet routing ecosystem is neither sustainable nor economical. More than 711K IPv4 routes and more than 41K IPv6 routes exist in current global Forwarding Information Base (FIBs) with growth rates increasing. This rapid growth has serious consequences, such as creating the need for costly FIB memory upgrades and increased potential for Internet service outages. And while FIB memories are power-hungry and prohibitively expensive, more than 70\% of the routes in FIBs carry no traffic for long time periods, a wasteful use of these expensive resources. Taking advantage of the emerging concept of programmable data plane, we design a programmable FIB caching architecture to address the existing concerns. Our preliminary evaluation results show that the architecture can significantly mitigate the global routing scalability and poor FIB utilization issues

Leveraging Programmable Data Plane For Compressing Forwarding Tables

The Forwarding Information Base (FIB) resides in the data plane of a routing device and is used to forward packets to a next-hop, based on packets\u27 destination IP addresses. The constant growth of a FIB forces network operators to spend more resources on maintaining memory with line-rate Longest Prefix Match (LPM) lookup in a FIB, namely, expensive and energy-hungry Ternary Content-Addressable Memory (TCAM) chips. In this work, we review two different approaches used to mitigate the FIB overflow problem. First, we investigate FIB aggregation, i.e., merging adjacent or overlapping routes with the same next-hop while preserving the forwarding behavior of a FIB. We propose a near-optimal algorithm, FIB Aggregation with Quick Selections (FAQS), that minimizes the FIB churn and speeds BGP update processing by more than twice. In the meantime, FAQS preserves a high compression ratio (at most 73\%). FAQS handles BGP updates incrementally, without the need of re-aggregating the entire FIB table. Second, we investigate FIB (or route) caching, when TCAM holds only a portion of a FIB that carries most of the traffic. We leverage the emerging concept of the programmable data plane to propose a Programmable FIB Caching Architecture (PFCA), that allows cache-victim selection at the line rate and significantly reduces the FIB churn compared to FIB aggregation. PFCA achieves 99.8% cache-hit ratio with only 3.3\% of the FIB placed in a FIB cache. Finally, we extend PFCA\u27s design with a novel approach of integrating incremental FIB aggregation and FIB caching. Such integration needed to overcome cache hiding challenge when a less specific prefix in a cache hides a more specific prefix in a secondary FIB table, which leads to incorrect LPM matching at the cache. In Combined FIB Caching and Aggregation (CFCA), cache-hit ratio is maximized up to 99.94% with only 2.5\% entries of the FIB, while the total number of route changes in TCAM is reduced by more than 40\% compared to low-churn FIB aggregation techniques

VeriTable: Fast Equivalence Verification of Multiple Large Forwarding Tables

Due to network practices such as traffic engineering and multi-homing, the number of routes---also known as IP prefixes---in the global forwarding tables has been increasing significantly in the last decade and continues growing in a super linear trend. One of the most promising solutions is to use smart Forwarding Information Base (FIB) aggregation algorithms to aggregate the prefixes and convert a large table into a small one. Doing so poses a research question, however, i.e., how can we quickly verify that the original table yields the same forwarding behaviors as the aggregated one? We answer this question in this paper, including addressing the challenges caused by the longest prefix matching (LPM) lookups. In particular, we propose the VeriTable algorithm that can employ a single tree/trie traversal to quickly check if multiple forwarding tables are forwarding equivalent, as well as if they could result in routing loops or black holes. The VeriTable algorithm significantly outperforms the state-of-the-art work for both IPv4 and IPv6 tables in every aspect, including the total running time, memory access times and memory consumption.Comment: INFOCOM 201

Shoreline delineation service: using an earth observation data cube and sentinel 2 images for coastal monitoring

Coastal management has a critical role in estimating the coastal environmental and socio-economic dynamics, providing various vital regional and local services. Remote sensing earth observations are essential for detecting and monitoring shorelines. UAVs combined with satellite remote sensing address the shoreline delineation problems to detect the shoreline and identify the shoreline zones. The paper presents a shoreline delineation service utilizing UAV and Sentinel 2 images within a Data Cube environment for monitoring coastal areas. The BandRatio, McFeeters, MNDWI1, and MNDWI2 algorithms have been implemented in the service to analyze the accuracy of each algorithm by comparing satellite and UAV-derived shorelines. As a case study, the Lake Sevan shoreline delineation, as one of the most incredible freshwater lakes in Eurasia, has been studied using the service. MNDWI2 algorithm showed the best accuracy for Lake Sevan shoreline delineation

Air temperature forecasting using artificial neural network for Ararat valley

The air temperature is a critical factor in many societal challenges to protect human health and the environment. Moreover, a vital climatic parameter, the temperature has a direct impact on evaporation, frost, and snow melting. Temperature predictions are based mainly on numerical and statistical models. Sometimes it is a challenge to improve the weather forecast accuracy. The article aims to implement a weather prediction technique based on machine learning methods and approaches to improve the hourly air temperature prediction for up to 24 hours in the Ararat valley (Armenia). Due to intense heat and low relative humidity, the high temperatures and hot winds occur between 120 and 160 days per year in Ararat valley, as one of the aridest regions of Armenia. The approach utilizes the earth observation data received from several meteorological stations and the large satellite analysis-ready datasets at different frequencies and resolutions. The experiments have been conducted with multiple neural networks to forecast air temperatures for 24 hours that happened over the Ararat valley. The suggested model has 87.31% and 75.57% accuracies to predict the temperature for the next 3 and 24 hours, which is sufficient to be used alongside the current state-of-the-art techniques

Paving the Way towards an Armenian Data Cube

Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia