The Experts below are selected from a list of 18399 Experts worldwide ranked by ideXlab platform
Saha Sabyasachi - One of the best experts on this subject based on the ideXlab platform.
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YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
2015Co-Authors: Giordano Danilo, Traverso Stefano, Grimaudo Luigi, Mellia Marco, Tongaonkar Alok, Baralis Elena, Saha SabyasachiAbstract:YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions. This paper presents YouLighter, an Unsupervised Technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Constellation Distance, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allow comparison between the clustering results from the same dataset, no Technique allows to measure the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Constellation Distance, to solve this problem. By running YouLighter over 10-month long traces obtained from two ISPs in different countries, we pinpoint both sudden changes in edge-node allocation, and small alterations to the cache allocation policies which actually impair the QoE that the end-users perceive
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YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
2015Co-Authors: Giordano Danilo, Traverso Stefano, Grimaudo Luigi, Mellia Marco, Baralis, Elena Maria, Tongaonkar Alok, Saha SabyasachiAbstract:YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions.This paper presents YouLighter, an Unsupervised Technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Pattern Dissimilarity, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allows us to compare the clustering results from the same dataset, no Technique measures the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Pattern Dissimilarity, to solve this problem.By running YouLighter over 10-month long traces obtained from ISPs, we pinpoint both sudden changes in edge-node allocation, and modifications to the cache allocation policy which actually impair the QoE that the end-users perceive
Giordano Danilo - One of the best experts on this subject based on the ideXlab platform.
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YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
2015Co-Authors: Giordano Danilo, Traverso Stefano, Grimaudo Luigi, Mellia Marco, Tongaonkar Alok, Baralis Elena, Saha SabyasachiAbstract:YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions. This paper presents YouLighter, an Unsupervised Technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Constellation Distance, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allow comparison between the clustering results from the same dataset, no Technique allows to measure the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Constellation Distance, to solve this problem. By running YouLighter over 10-month long traces obtained from two ISPs in different countries, we pinpoint both sudden changes in edge-node allocation, and small alterations to the cache allocation policies which actually impair the QoE that the end-users perceive
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YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
2015Co-Authors: Giordano Danilo, Traverso Stefano, Grimaudo Luigi, Mellia Marco, Baralis, Elena Maria, Tongaonkar Alok, Saha SabyasachiAbstract:YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions.This paper presents YouLighter, an Unsupervised Technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Pattern Dissimilarity, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allows us to compare the clustering results from the same dataset, no Technique measures the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Pattern Dissimilarity, to solve this problem.By running YouLighter over 10-month long traces obtained from ISPs, we pinpoint both sudden changes in edge-node allocation, and modifications to the cache allocation policy which actually impair the QoE that the end-users perceive
Nasser M. Abdel-salam - One of the best experts on this subject based on the ideXlab platform.
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Classification of Drinking Water Quality Index and Identification of Significant Factors
Water Resources Management, 2016Co-Authors: Hafiza Mamona Nazir, Zulifqar Ali, Mazhar Iqbal Zafar, Ijaz Hussain, Nasser M. Abdel-salamAbstract:Water pipes are considered to be one of responsible sources for the water pollution. Among these sources of water supply, the water pipes are the only source of carrying out fresh or processed water into lakes, ponds and streams etc. In Pakistan, knowledge on the condition of water pipes is scarce as deterioration of water pipes are hardly inspected due to high cost. The aim of the current research was to examine the quality of water pipelines of eight districts of South-Punjab, namely, Mianwali, Khushab, Layyah, Bhakkar, Dera Ghazi Khan, Muzaffargarh, Rajanpur and Rahim Yar Khan. Selected sampling stations were analyzed for physio-chemical parameters such as pH, Total Dissolve Solids (TDS), Sulfate (SO_4), Chlorine (Cl), Calcium (Ca), Magnesium (Mg), Hardness, Nitrate (NO_3), Fluoride (F) and Iron (Fe). The data pertaining water monitoring contain different parameters and seem difficult work for the interpretation of water quality by managing different parameters separately. For this purpose, National Sanitation Foundation Water Quality Index (NSF-WQI) was determined to communicate the quality of water in a simple form. Besides this, groups comprising of similar sampling sites based on water quality characteristics were identified using Unsupervised Technique. Factor Analysis (FA) has been performed for extracting the latent pollution sources that may cause the more variance in large and complex data. The calculated values of WQI from 1600 sampling stations ranging from 20.73 to 223.74 are divided into five groups; Excellent to Unsuitable class of waters with the average value 62.09 described as good limit for drinking water. Further sampling stations are divided into five optimal clusters selected with suitable k value obtained from Silhouette coefficient. Results of k-means clustering are also verified with natural groups made by WQI. Analysis of multivariate Techniques showed several factors to be responsible for the water quality deterioration. It is found out from the FA that three latent factors such as organic pollution, agriculture run-off and urban land use caused 83.30 % of the total variation. Hence, water quality management and control of these latent factors are strongly recommended.
Grimaudo Luigi - One of the best experts on this subject based on the ideXlab platform.
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YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
2015Co-Authors: Giordano Danilo, Traverso Stefano, Grimaudo Luigi, Mellia Marco, Tongaonkar Alok, Baralis Elena, Saha SabyasachiAbstract:YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions. This paper presents YouLighter, an Unsupervised Technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Constellation Distance, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allow comparison between the clustering results from the same dataset, no Technique allows to measure the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Constellation Distance, to solve this problem. By running YouLighter over 10-month long traces obtained from two ISPs in different countries, we pinpoint both sudden changes in edge-node allocation, and small alterations to the cache allocation policies which actually impair the QoE that the end-users perceive
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YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
2015Co-Authors: Giordano Danilo, Traverso Stefano, Grimaudo Luigi, Mellia Marco, Baralis, Elena Maria, Tongaonkar Alok, Saha SabyasachiAbstract:YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions.This paper presents YouLighter, an Unsupervised Technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Pattern Dissimilarity, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allows us to compare the clustering results from the same dataset, no Technique measures the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Pattern Dissimilarity, to solve this problem.By running YouLighter over 10-month long traces obtained from ISPs, we pinpoint both sudden changes in edge-node allocation, and modifications to the cache allocation policy which actually impair the QoE that the end-users perceive
Tongaonkar Alok - One of the best experts on this subject based on the ideXlab platform.
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YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
2015Co-Authors: Giordano Danilo, Traverso Stefano, Grimaudo Luigi, Mellia Marco, Tongaonkar Alok, Baralis Elena, Saha SabyasachiAbstract:YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions. This paper presents YouLighter, an Unsupervised Technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Constellation Distance, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allow comparison between the clustering results from the same dataset, no Technique allows to measure the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Constellation Distance, to solve this problem. By running YouLighter over 10-month long traces obtained from two ISPs in different countries, we pinpoint both sudden changes in edge-node allocation, and small alterations to the cache allocation policies which actually impair the QoE that the end-users perceive
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YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
2015Co-Authors: Giordano Danilo, Traverso Stefano, Grimaudo Luigi, Mellia Marco, Baralis, Elena Maria, Tongaonkar Alok, Saha SabyasachiAbstract:YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions.This paper presents YouLighter, an Unsupervised Technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Pattern Dissimilarity, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allows us to compare the clustering results from the same dataset, no Technique measures the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Pattern Dissimilarity, to solve this problem.By running YouLighter over 10-month long traces obtained from ISPs, we pinpoint both sudden changes in edge-node allocation, and modifications to the cache allocation policy which actually impair the QoE that the end-users perceive
