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  1. A Blockchain-Based Decentralized Data Storage and Access Framework for PingER

    The blockchain is an innovative technology which opened doors to new applications for solving numerous problems in distributed environments. In this study, we design a blockchain-based data storage and access framework for PingER (worldwide end-to-end Internet performance measurement project) to remove its total dependence on a centralized repository. We use the permissioned blockchain and Distributed Hash Tables (DHT) for this purpose. In the proposed framework, metadata of the files are stored on the blockchain whereas the actual files are stored off-chain through DHT at multiple locations using a peer-to-peer network of PingER Monitoring Agents. Lastly, this will provide decentralized storage,more » distributed processing, and efficient lookup capabilities to the PingER framework.« less
  2. Internet Performance Analysis of South Asian Countries Using End-to-End Internet Performance Measurements

    Internet performance is highly correlated with key economic development metrics of a region. According to World Bank, the economic growth of a country increases 1.3% with a 10% increase in the speed of the Internet. Therefore, it is necessary to monitor and understand the performance of the Internet links in the region. It helps to figure out the infrastructural inefficiencies, poor resource allocation, and routing issues in the region. Moreover, it provides healthy suggestions for future upgrades. Therefore, the objective of this paper is to understand the Internet performance and routing infrastructure of South Asian countries in comparison to themore » developed world and neighboring countries using end-to-end Internet performance measurements. The South Asian countries comprise nearly 32% of the Internet users in Asia and nearly 16% of the world. The Internet performance metrics in the region are collected through the PingER framework. The framework is developed by the SLAC National Accelerator Laboratory, USA and is running for the last 20 years. PingER has 16 monitoring nodes in the region, and in the last year PingER monitors about 40 sites in South Asia using the ubiquitous ping facility. The collected data is used to estimate the key Internet performance metrics of South Asian countries. The performance metrics are compared with the neighboring countries and the developed world. Particularly, the TCP throughput of the countries is also correlated with different development indices. Further, worldwide Internet connectivity and routing patterns of the countries are investigated to figure out the inconsistencies in the region. Furthermore, the performance analysis revealed that the South Asia region is 7-10 years behind the developed regions of North America (USA and Canada), Europe, and East Asia.« less
  3. Detecting Anomalies from End-to-End Internet Performance Measurements (PingER) Using Cluster Based Local Outlier Factor

    PingER (Ping End-to-End Reporting) is a worldwide end-to-end Internet performance measurement framework. It was developed by the SLAC National Accelerator Laboratory, Stanford, USA and running from the last 20 years. It has more than 700 monitoring agents and remote sites which monitor the performance of Internet links around 170 countries of the world. At present, the size of the compressed PingER data set is about 60 GB comprising of 100,000 flat files. The data is publicly available for valuable Internet performance analyses. However, the data sets suffer from missing values and anomalies due to congestion, bottleneck links, queuing overflow, networkmore » software misconfiguration, hardware failure, cable cuts, and social upheavals. Therefore, the objective of this paper is to detect such performance drops or spikes labeled as anomalies or outliers for the PingER data set. In the proposed approach, the raw text files of the data set are transformed into a PingER dimensional model. The missing values are imputed using the k-NN algorithm. The data is partitioned into similar instances using the k-means clustering algorithm. Afterward, clustering is integrated with the Local Outlier Factor (LOF) using the Cluster Based Local Outlier Factor (CBLOF) algorithm to detect the anomalies or outliers from the PingER data. Lastly, anomalies are further analyzed to identify the time frame and location of the hosts generating the major percentage of the anomalies in the PingER data set ranging from 1998 to 2016.« less

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"Ali, Saqib"

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