際際滷shows by User: gwendal / http://www.slideshare.net/images/logo.gif 際際滷shows by User: gwendal / Sun, 03 Dec 2017 22:56:09 GMT 際際滷Share feed for 際際滷shows by User: gwendal Reproducible research at ACM MMSys /slideshow/reproducible-research-at-acm-mmsys/83273672 reproducibilityacmmmsys1-171203225609
ACM promotes reproducible research. In the SIGMM chapter too! This slideshow presents the efforts toward reproducibility at the Multimedia System (MMSys) conference. In particular the action at MMSys'17 of giving reproducible badges to the papers that have made explicit efforts for sharing artifacts (dataset and code source).]]>
ACM promotes reproducible research. In the SIGMM chapter too! This slideshow presents the efforts toward reproducibility at the Multimedia System (MMSys) conference. In particular the action at MMSys'17 of giving reproducible badges to the papers that have made explicit efforts for sharing artifacts (dataset and code source).]]> Sun, 03 Dec 2017 22:56:09 GMT /slideshow/reproducible-research-at-acm-mmsys/83273672 gwendal@slideshare.net(gwendal) Reproducible research at ACM MMSys gwendal ACM promotes reproducible research. In the SIGMM chapter too! This slideshow presents the efforts toward reproducibility at the Multimedia System (MMSys) conference. In particular the action at MMSys'17 of giving reproducible badges to the papers that have made explicit efforts for sharing artifacts (dataset and code source). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/reproducibilityacmmmsys1-171203225609-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ACM promotes reproducible research. In the SIGMM chapter too! This slideshow presents the efforts toward reproducibility at the Multimedia System (MMSys) conference. In particular the action at MMSys&#39;17 of giving reproducible badges to the papers that have made explicit efforts for sharing artifacts (dataset and code source).
Reproducible research at ACM MMSys from Gwendal Simon
]]> 500 6 https://cdn.slidesharecdn.com/ss_thumbnails/reproducibilityacmmmsys1-171203225609-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Netgames: history and preparing 2018 edition /slideshow/netgames-history-and-preparing-2018-edition/83123440 netgames182-171201114600
Short introduction to the NetGames workshop, the most prestigious workshop on Network and Systems Support for Games. The presentation looks back at the past editions and prepares the 2018 edition. Most of the workshop editions were technically co-sponsored by IEEE and ACM. http://conferences.telecom-bretagne.eu/netgames18/]]>
Short introduction to the NetGames workshop, the most prestigious workshop on Network and Systems Support for Games. The presentation looks back at the past editions and prepares the 2018 edition. Most of the workshop editions were technically co-sponsored by IEEE and ACM. http://conferences.telecom-bretagne.eu/netgames18/]]> Fri, 01 Dec 2017 11:46:00 GMT /slideshow/netgames-history-and-preparing-2018-edition/83123440 gwendal@slideshare.net(gwendal) Netgames: history and preparing 2018 edition gwendal Short introduction to the NetGames workshop, the most prestigious workshop on Network and Systems Support for Games. The presentation looks back at the past editions and prepares the 2018 edition. Most of the workshop editions were technically co-sponsored by IEEE and ACM. http://conferences.telecom-bretagne.eu/netgames18/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/netgames182-171201114600-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Short introduction to the NetGames workshop, the most prestigious workshop on Network and Systems Support for Games. The presentation looks back at the past editions and prepares the 2018 edition. Most of the workshop editions were technically co-sponsored by IEEE and ACM. http://conferences.telecom-bretagne.eu/netgames18/
Netgames: history and preparing 2018 edition from Gwendal Simon
]]> 403 3 https://cdn.slidesharecdn.com/ss_thumbnails/netgames182-171201114600-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Virtual Reality in 5G Networks /slideshow/virtual-reality-in-5g-networks/76859822 gwendal-5g-vr-170612092543
The Virtual Reality (VR) is considered by industrials from content industry as a major technology to develop in the next years. It comes however with a number of challenges, which will require the cooperation between multiple actors in the content delivery chain. Since it combines high quality multimedia delivery and low-latency interactivity, VR matches the requirements of 5G networks and it has the potential to be a key driver for adoption of the next generation network. In this talk, the main requirements of the envisioned next-generation VR applications will be reviewed, especially the need of both bandwidth and latency. Then, the main delivery architectures will be presented, including their main weaknesses in todays networks and the efforts that are currently done in standardization groups to provide the main elements of these architectures in the perspective of 5G. Finally, a selection of the main open challenges will conclude the talk.]]>
The Virtual Reality (VR) is considered by industrials from content industry as a major technology to develop in the next years. It comes however with a number of challenges, which will require the cooperation between multiple actors in the content delivery chain. Since it combines high quality multimedia delivery and low-latency interactivity, VR matches the requirements of 5G networks and it has the potential to be a key driver for adoption of the next generation network. In this talk, the main requirements of the envisioned next-generation VR applications will be reviewed, especially the need of both bandwidth and latency. Then, the main delivery architectures will be presented, including their main weaknesses in todays networks and the efforts that are currently done in standardization groups to provide the main elements of these architectures in the perspective of 5G. Finally, a selection of the main open challenges will conclude the talk.]]> Mon, 12 Jun 2017 09:25:43 GMT /slideshow/virtual-reality-in-5g-networks/76859822 gwendal@slideshare.net(gwendal) Virtual Reality in 5G Networks gwendal The Virtual Reality (VR) is considered by industrials from content industry as a major technology to develop in the next years. It comes however with a number of challenges, which will require the cooperation between multiple actors in the content delivery chain. Since it combines high quality multimedia delivery and low-latency interactivity, VR matches the requirements of 5G networks and it has the potential to be a key driver for adoption of the next generation network. In this talk, the main requirements of the envisioned next-generation VR applications will be reviewed, especially the need of both bandwidth and latency. Then, the main delivery architectures will be presented, including their main weaknesses in todays networks and the efforts that are currently done in standardization groups to provide the main elements of these architectures in the perspective of 5G. Finally, a selection of the main open challenges will conclude the talk. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gwendal-5g-vr-170612092543-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The Virtual Reality (VR) is considered by industrials from content industry as a major technology to develop in the next years. It comes however with a number of challenges, which will require the cooperation between multiple actors in the content delivery chain. Since it combines high quality multimedia delivery and low-latency interactivity, VR matches the requirements of 5G networks and it has the potential to be a key driver for adoption of the next generation network. In this talk, the main requirements of the envisioned next-generation VR applications will be reviewed, especially the need of both bandwidth and latency. Then, the main delivery architectures will be presented, including their main weaknesses in todays networks and the efforts that are currently done in standardization groups to provide the main elements of these architectures in the perspective of 5G. Finally, a selection of the main open challenges will conclude the talk.
Virtual Reality in 5G Networks from Gwendal Simon
]]> 1555 5 https://cdn.slidesharecdn.com/ss_thumbnails/gwendal-5g-vr-170612092543-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Adaptive Delivery of Live Video Stream: Infrastructure cost vs. QoE /slideshow/live-stream-46092521/46092521 live-stream-150320133750-conversion-gate01
The popularity of OTT platforms for live video streaming is such that Twitch---a service for gamecasters---is today the fourth largest source of US Internet traffic at peak. The challenge for CDN providers is to find a trade-off between the Quality of Experience (QoE) at the user side (which should be maximized) and the footprint of these services on the delivery network infrastructure (which should be minimized). We believe that technologies for dynamic adaptive streaming represent opportunities to revisit this trade-off. We have studied some of these opportunities from an optimization standpoint. We present in this talk two recent contributions: (i) minimizing the footprint by delivering only a subset of the video representations to the CDN edge servers, and (ii) maximizing the QoE by selecting the best video encoding parameters at the origin servers.]]>
The popularity of OTT platforms for live video streaming is such that Twitch---a service for gamecasters---is today the fourth largest source of US Internet traffic at peak. The challenge for CDN providers is to find a trade-off between the Quality of Experience (QoE) at the user side (which should be maximized) and the footprint of these services on the delivery network infrastructure (which should be minimized). We believe that technologies for dynamic adaptive streaming represent opportunities to revisit this trade-off. We have studied some of these opportunities from an optimization standpoint. We present in this talk two recent contributions: (i) minimizing the footprint by delivering only a subset of the video representations to the CDN edge servers, and (ii) maximizing the QoE by selecting the best video encoding parameters at the origin servers.]]> Fri, 20 Mar 2015 13:37:50 GMT /slideshow/live-stream-46092521/46092521 gwendal@slideshare.net(gwendal) Adaptive Delivery of Live Video Stream: Infrastructure cost vs. QoE gwendal The popularity of OTT platforms for live video streaming is such that Twitch---a service for gamecasters---is today the fourth largest source of US Internet traffic at peak. The challenge for CDN providers is to find a trade-off between the Quality of Experience (QoE) at the user side (which should be maximized) and the footprint of these services on the delivery network infrastructure (which should be minimized). We believe that technologies for dynamic adaptive streaming represent opportunities to revisit this trade-off. We have studied some of these opportunities from an optimization standpoint. We present in this talk two recent contributions: (i) minimizing the footprint by delivering only a subset of the video representations to the CDN edge servers, and (ii) maximizing the QoE by selecting the best video encoding parameters at the origin servers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/live-stream-150320133750-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The popularity of OTT platforms for live video streaming is such that Twitch---a service for gamecasters---is today the fourth largest source of US Internet traffic at peak. The challenge for CDN providers is to find a trade-off between the Quality of Experience (QoE) at the user side (which should be maximized) and the footprint of these services on the delivery network infrastructure (which should be minimized). We believe that technologies for dynamic adaptive streaming represent opportunities to revisit this trade-off. We have studied some of these opportunities from an optimization standpoint. We present in this talk two recent contributions: (i) minimizing the footprint by delivering only a subset of the video representations to the CDN edge servers, and (ii) maximizing the QoE by selecting the best video encoding parameters at the origin servers.
Adaptive Delivery of Live Video Stream: Infrastructure cost vs. QoE from Gwendal Simon
]]> 1309 3 https://cdn.slidesharecdn.com/ss_thumbnails/live-stream-150320133750-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Research on cloud gaming: status and perspectives /gwendal/research-on-cloud-gaming-status-and-perspectives cloud-gaming-150312055938-conversion-gate01
Cloud gaming is seen as a major driver for future gaming business. However, cloud gaming is also a big challenge regarding the technical aspects. Researchers have worked on the area in the recent years. This presentation provides a tour on the research activities in the area. We make a focus on network latency aspects. We provide all along the presentation some research challenges.]]>
Cloud gaming is seen as a major driver for future gaming business. However, cloud gaming is also a big challenge regarding the technical aspects. Researchers have worked on the area in the recent years. This presentation provides a tour on the research activities in the area. We make a focus on network latency aspects. We provide all along the presentation some research challenges.]]> Thu, 12 Mar 2015 05:59:38 GMT /gwendal/research-on-cloud-gaming-status-and-perspectives gwendal@slideshare.net(gwendal) Research on cloud gaming: status and perspectives gwendal Cloud gaming is seen as a major driver for future gaming business. However, cloud gaming is also a big challenge regarding the technical aspects. Researchers have worked on the area in the recent years. This presentation provides a tour on the research activities in the area. We make a focus on network latency aspects. We provide all along the presentation some research challenges. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cloud-gaming-150312055938-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Cloud gaming is seen as a major driver for future gaming business. However, cloud gaming is also a big challenge regarding the technical aspects. Researchers have worked on the area in the recent years. This presentation provides a tour on the research activities in the area. We make a focus on network latency aspects. We provide all along the presentation some research challenges.
Research on cloud gaming: status and perspectives from Gwendal Simon
]]> 2073 5 https://cdn.slidesharecdn.com/ss_thumbnails/cloud-gaming-150312055938-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 DASH in Twitch: Adaptive Bitrate Streaming in Live Game Streaming Platforms /slideshow/main-42407430/42407430 main-141205154152-conversion-gate02
Live game streaming platforms such as Twitch allow gamers to broadcast their gameplay over the Internet. The popularity of these platforms boosts the market of eSport but poses new delivery problems. In this paper, we focus on the implementation of adaptive bitrate streaming in massive live game streaming platforms. Based on three months of real data traces from Twitch, we motivate the need for an adoption of adaptive bitrate streaming in this platform to reduce the delivery bandwidth cost and to increase QoE of view- ers. We show however that a naive implementation requires the reservation of a large amount of computing resources for transcoding purposes. To address the trade-off between benefits and costs, we formulate a management problem and we design two strategies for deciding which online channels should be delivered by adaptive bitrate streaming. Our evaluations based on real traces show that these strategies can reduce the overall infrastructure cost by 40% in comparison to an implementation without adaptive streaming.]]>
Live game streaming platforms such as Twitch allow gamers to broadcast their gameplay over the Internet. The popularity of these platforms boosts the market of eSport but poses new delivery problems. In this paper, we focus on the implementation of adaptive bitrate streaming in massive live game streaming platforms. Based on three months of real data traces from Twitch, we motivate the need for an adoption of adaptive bitrate streaming in this platform to reduce the delivery bandwidth cost and to increase QoE of view- ers. We show however that a naive implementation requires the reservation of a large amount of computing resources for transcoding purposes. To address the trade-off between benefits and costs, we formulate a management problem and we design two strategies for deciding which online channels should be delivered by adaptive bitrate streaming. Our evaluations based on real traces show that these strategies can reduce the overall infrastructure cost by 40% in comparison to an implementation without adaptive streaming.]]> Fri, 05 Dec 2014 15:41:52 GMT /slideshow/main-42407430/42407430 gwendal@slideshare.net(gwendal) DASH in Twitch: Adaptive Bitrate Streaming in Live Game Streaming Platforms gwendal Live game streaming platforms such as Twitch allow gamers to broadcast their gameplay over the Internet. The popularity of these platforms boosts the market of eSport but poses new delivery problems. In this paper, we focus on the implementation of adaptive bitrate streaming in massive live game streaming platforms. Based on three months of real data traces from Twitch, we motivate the need for an adoption of adaptive bitrate streaming in this platform to reduce the delivery bandwidth cost and to increase QoE of view- ers. We show however that a naive implementation requires the reservation of a large amount of computing resources for transcoding purposes. To address the trade-off between benefits and costs, we formulate a management problem and we design two strategies for deciding which online channels should be delivered by adaptive bitrate streaming. Our evaluations based on real traces show that these strategies can reduce the overall infrastructure cost by 40% in comparison to an implementation without adaptive streaming. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/main-141205154152-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Live game streaming platforms such as Twitch allow gamers to broadcast their gameplay over the Internet. The popularity of these platforms boosts the market of eSport but poses new delivery problems. In this paper, we focus on the implementation of adaptive bitrate streaming in massive live game streaming platforms. Based on three months of real data traces from Twitch, we motivate the need for an adoption of adaptive bitrate streaming in this platform to reduce the delivery bandwidth cost and to increase QoE of view- ers. We show however that a naive implementation requires the reservation of a large amount of computing resources for transcoding purposes. To address the trade-off between benefits and costs, we formulate a management problem and we design two strategies for deciding which online channels should be delivered by adaptive bitrate streaming. Our evaluations based on real traces show that these strategies can reduce the overall infrastructure cost by 40% in comparison to an implementation without adaptive streaming.
DASH in Twitch: Adaptive Bitrate Streaming in Live Game Streaming Platforms from Gwendal Simon
]]> 1789 3 https://cdn.slidesharecdn.com/ss_thumbnails/main-141205154152-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fast Near-Optimal Delivery of Live Streams in CDN /slideshow/slides-24612226/24612226 slides-130725072917-phpapp02
CDNs are confronted with a sharp increase in traffic related to live video (channel) streaming. Previous theoretical models that deal with streaming capacity problems do not capture the emerging reality faced by todays CDNs, in particular rate-adaptive streaming. In this presentation, we identify a new, discretized streaming model for live video delivery in CDNs. For this model we formulate a general optimization problem. Then we study a practical scenario that occurs in real CDNs. We present a fast, easy to implement, and near-optimal algorithm with performance approximation ratios that are negligible for large network. More details in: http://enstb.org/~gsimon/Resources/algotel13.pdf http://enstb.org/~gsimon/Resources/icccn13.pdf]]>
CDNs are confronted with a sharp increase in traffic related to live video (channel) streaming. Previous theoretical models that deal with streaming capacity problems do not capture the emerging reality faced by todays CDNs, in particular rate-adaptive streaming. In this presentation, we identify a new, discretized streaming model for live video delivery in CDNs. For this model we formulate a general optimization problem. Then we study a practical scenario that occurs in real CDNs. We present a fast, easy to implement, and near-optimal algorithm with performance approximation ratios that are negligible for large network. More details in: http://enstb.org/~gsimon/Resources/algotel13.pdf http://enstb.org/~gsimon/Resources/icccn13.pdf]]> Thu, 25 Jul 2013 07:29:17 GMT /slideshow/slides-24612226/24612226 gwendal@slideshare.net(gwendal) Fast Near-Optimal Delivery of Live Streams in CDN gwendal CDNs are confronted with a sharp increase in traffic related to live video (channel) streaming. Previous theoretical models that deal with streaming capacity problems do not capture the emerging reality faced by todays CDNs, in particular rate-adaptive streaming. In this presentation, we identify a new, discretized streaming model for live video delivery in CDNs. For this model we formulate a general optimization problem. Then we study a practical scenario that occurs in real CDNs. We present a fast, easy to implement, and near-optimal algorithm with performance approximation ratios that are negligible for large network. More details in: http://enstb.org/~gsimon/Resources/algotel13.pdf http://enstb.org/~gsimon/Resources/icccn13.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-130725072917-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> CDNs are confronted with a sharp increase in traffic related to live video (channel) streaming. Previous theoretical models that deal with streaming capacity problems do not capture the emerging reality faced by todays CDNs, in particular rate-adaptive streaming. In this presentation, we identify a new, discretized streaming model for live video delivery in CDNs. For this model we formulate a general optimization problem. Then we study a practical scenario that occurs in real CDNs. We present a fast, easy to implement, and near-optimal algorithm with performance approximation ratios that are negligible for large network. More details in: http://enstb.org/~gsimon/Resources/algotel13.pdf http://enstb.org/~gsimon/Resources/icccn13.pdf
Fast Near-Optimal Delivery of Live Streams in CDN from Gwendal Simon
]]> 1439 3 https://cdn.slidesharecdn.com/ss_thumbnails/slides-130725072917-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Scadoosh: Scaling Down the Footprint of Rate-Adaptive Live Streaming on CDN Infrastructure /slideshow/scadoosh-scale-down-footprint-of-dash/14205455 scadoosh-gwendal-120907105126-phpapp01
Akamai recently announced that its infrastructure will have to expand by a factor of 100 times in the next five years just to keep up with the demand for real-time video. One of the reasons comes from the rate-adaptive streaming technologies. Our mission is to reduce the footprint of live rate-adaptive streaming applications on the CDN infrastructure. We show in this presentation that a smart system can reduce the infrastructure needs by a factor of five with negligible losses of Quality of Experience (QoE) for end users.]]>
Akamai recently announced that its infrastructure will have to expand by a factor of 100 times in the next five years just to keep up with the demand for real-time video. One of the reasons comes from the rate-adaptive streaming technologies. Our mission is to reduce the footprint of live rate-adaptive streaming applications on the CDN infrastructure. We show in this presentation that a smart system can reduce the infrastructure needs by a factor of five with negligible losses of Quality of Experience (QoE) for end users.]]> Fri, 07 Sep 2012 10:51:24 GMT /slideshow/scadoosh-scale-down-footprint-of-dash/14205455 gwendal@slideshare.net(gwendal) Scadoosh: Scaling Down the Footprint of Rate-Adaptive Live Streaming on CDN Infrastructure gwendal Akamai recently announced that its infrastructure will have to expand by a factor of 100 times in the next five years just to keep up with the demand for real-time video. One of the reasons comes from the rate-adaptive streaming technologies. Our mission is to reduce the footprint of live rate-adaptive streaming applications on the CDN infrastructure. We show in this presentation that a smart system can reduce the infrastructure needs by a factor of five with negligible losses of Quality of Experience (QoE) for end users. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scadoosh-gwendal-120907105126-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Akamai recently announced that its infrastructure will have to expand by a factor of 100 times in the next five years just to keep up with the demand for real-time video. One of the reasons comes from the rate-adaptive streaming technologies. Our mission is to reduce the footprint of live rate-adaptive streaming applications on the CDN infrastructure. We show in this presentation that a smart system can reduce the infrastructure needs by a factor of five with negligible losses of Quality of Experience (QoE) for end users.
Scadoosh: Scaling Down the Footprint of Rate-Adaptive Live Streaming on CDN Infrastructure from Gwendal Simon
]]> 794 3 https://cdn.slidesharecdn.com/ss_thumbnails/scadoosh-gwendal-120907105126-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Minimizing Server Throughput for Low-Delay Live Streaming in Content Delivery Networks /slideshow/minimizing-server-throughput-for-lowdelay-live-streaming-in-content-delivery-networks/13461081 nossdavgwendalsimon-120626133051-phpapp01
Large-scale live streaming systems can experience bottle- necks within the infrastructure of the underlying Content Delivery Network. In particular, the equipment bottleneck occurs when the fan-out of a machine does not enable the concurrent transmission of a stream to multiple other equipments. In this paper, we aim to deliver a live stream to a set of destination nodes with minimum throughput at the source and limited increase of the streaming delay. We leverage on rateless codes and cooperation among destination nodes. With rateless codes, a node is able to decode a video block of k information symbols after receiving slightly more than k encoded symbols. To deliver the encoded symbols, we use multiple trees where inner nodes forward all received symbols. Our goal is to build a diffusion forest that minimizes the transmission rate at the source while guaranteeing on-time delivery and reliability at the nodes. When the network is assumed to be lossless and the constraint on delivery delay is relaxed, we give an algorithm that computes a diffusion forest resulting in the minimum source transmission rate. We also propose an effective heuristic algorithm for the general case where packet loss occurs and the delivery delay is bounded. Simulation results for realistic settings show that with our solution the source requires only slightly more than the video bit rate to reliably feed all nodes.]]>
Large-scale live streaming systems can experience bottle- necks within the infrastructure of the underlying Content Delivery Network. In particular, the equipment bottleneck occurs when the fan-out of a machine does not enable the concurrent transmission of a stream to multiple other equipments. In this paper, we aim to deliver a live stream to a set of destination nodes with minimum throughput at the source and limited increase of the streaming delay. We leverage on rateless codes and cooperation among destination nodes. With rateless codes, a node is able to decode a video block of k information symbols after receiving slightly more than k encoded symbols. To deliver the encoded symbols, we use multiple trees where inner nodes forward all received symbols. Our goal is to build a diffusion forest that minimizes the transmission rate at the source while guaranteeing on-time delivery and reliability at the nodes. When the network is assumed to be lossless and the constraint on delivery delay is relaxed, we give an algorithm that computes a diffusion forest resulting in the minimum source transmission rate. We also propose an effective heuristic algorithm for the general case where packet loss occurs and the delivery delay is bounded. Simulation results for realistic settings show that with our solution the source requires only slightly more than the video bit rate to reliably feed all nodes.]]> Tue, 26 Jun 2012 13:30:49 GMT /slideshow/minimizing-server-throughput-for-lowdelay-live-streaming-in-content-delivery-networks/13461081 gwendal@slideshare.net(gwendal) Minimizing Server Throughput for Low-Delay Live Streaming in Content Delivery Networks gwendal Large-scale live streaming systems can experience bottle- necks within the infrastructure of the underlying Content Delivery Network. In particular, the equipment bottleneck occurs when the fan-out of a machine does not enable the concurrent transmission of a stream to multiple other equipments. In this paper, we aim to deliver a live stream to a set of destination nodes with minimum throughput at the source and limited increase of the streaming delay. We leverage on rateless codes and cooperation among destination nodes. With rateless codes, a node is able to decode a video block of k information symbols after receiving slightly more than k encoded symbols. To deliver the encoded symbols, we use multiple trees where inner nodes forward all received symbols. Our goal is to build a diffusion forest that minimizes the transmission rate at the source while guaranteeing on-time delivery and reliability at the nodes. When the network is assumed to be lossless and the constraint on delivery delay is relaxed, we give an algorithm that computes a diffusion forest resulting in the minimum source transmission rate. We also propose an effective heuristic algorithm for the general case where packet loss occurs and the delivery delay is bounded. Simulation results for realistic settings show that with our solution the source requires only slightly more than the video bit rate to reliably feed all nodes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nossdavgwendalsimon-120626133051-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Large-scale live streaming systems can experience bottle- necks within the infrastructure of the underlying Content Delivery Network. In particular, the equipment bottleneck occurs when the fan-out of a machine does not enable the concurrent transmission of a stream to multiple other equipments. In this paper, we aim to deliver a live stream to a set of destination nodes with minimum throughput at the source and limited increase of the streaming delay. We leverage on rateless codes and cooperation among destination nodes. With rateless codes, a node is able to decode a video block of k information symbols after receiving slightly more than k encoded symbols. To deliver the encoded symbols, we use multiple trees where inner nodes forward all received symbols. Our goal is to build a diffusion forest that minimizes the transmission rate at the source while guaranteeing on-time delivery and reliability at the nodes. When the network is assumed to be lossless and the constraint on delivery delay is relaxed, we give an algorithm that computes a diffusion forest resulting in the minimum source transmission rate. We also propose an effective heuristic algorithm for the general case where packet loss occurs and the delivery delay is bounded. Simulation results for realistic settings show that with our solution the source requires only slightly more than the video bit rate to reliably feed all nodes.
Minimizing Server Throughput for Low-Delay Live Streaming in Content Delivery Networks from Gwendal Simon
]]> 1001 3 https://cdn.slidesharecdn.com/ss_thumbnails/nossdavgwendalsimon-120626133051-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Time-Shifted TV in Content Centric Networks: the Case for Cooperative In-Network Caching /slideshow/timeshifted-tv-in-content-centric-networks-the-case-for-cooperative-innetwork-caching/8240703 slidesimoncooperativeccn-110607202826-phpapp01
Recent works on Content Centric Networking (CCN) enable the exploitation of the caching resources of the new generation of routers (Content Routers or CR). So far, only a basic Least Recently Used (LRU) strategy implemented on every CRs has been proposed. We introduce here a cooperative caching strategy that has been designed for the treatment of large video streams with on-demand access. This caching strategy addresses the need of Internet Service Provider by halving the cross-domain traffic. ]]>
Recent works on Content Centric Networking (CCN) enable the exploitation of the caching resources of the new generation of routers (Content Routers or CR). So far, only a basic Least Recently Used (LRU) strategy implemented on every CRs has been proposed. We introduce here a cooperative caching strategy that has been designed for the treatment of large video streams with on-demand access. This caching strategy addresses the need of Internet Service Provider by halving the cross-domain traffic. ]]> Tue, 07 Jun 2011 20:28:22 GMT /slideshow/timeshifted-tv-in-content-centric-networks-the-case-for-cooperative-innetwork-caching/8240703 gwendal@slideshare.net(gwendal) Time-Shifted TV in Content Centric Networks: the Case for Cooperative In-Network Caching gwendal Recent works on Content Centric Networking (CCN) enable the exploitation of the caching resources of the new generation of routers (Content Routers or CR). So far, only a basic Least Recently Used (LRU) strategy implemented on every CRs has been proposed. We introduce here a cooperative caching strategy that has been designed for the treatment of large video streams with on-demand access. This caching strategy addresses the need of Internet Service Provider by halving the cross-domain traffic. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesimoncooperativeccn-110607202826-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Recent works on Content Centric Networking (CCN) enable the exploitation of the caching resources of the new generation of routers (Content Routers or CR). So far, only a basic Least Recently Used (LRU) strategy implemented on every CRs has been proposed. We introduce here a cooperative caching strategy that has been designed for the treatment of large video streams with on-demand access. This caching strategy addresses the need of Internet Service Provider by halving the cross-domain traffic.
Time-Shifted TV in Content Centric Networks: the Case for Cooperative In-Network Caching from Gwendal Simon
]]> 791 3 https://cdn.slidesharecdn.com/ss_thumbnails/slidesimoncooperativeccn-110607202826-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Internet : pourquoi 巽a marche https://fr.slideshare.net/slideshow/internet-pourquoi-a-marche/6842518 fetescience-110207144834-phpapp01
Une br竪ve description d'Internet pour un public non-averti]]>
Une br竪ve description d'Internet pour un public non-averti]]> Mon, 07 Feb 2011 14:48:32 GMT https://fr.slideshare.net/slideshow/internet-pourquoi-a-marche/6842518 gwendal@slideshare.net(gwendal) Internet : pourquoi 巽a marche gwendal Une br竪ve description d'Internet pour un public non-averti <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fetescience-110207144834-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Une br竪ve description d&#39;Internet pour un public non-averti
from Gwendal Simon
]]> 1190 11 https://cdn.slidesharecdn.com/ss_thumbnails/fetescience-110207144834-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Optimal Network Locality in Distributed Services /slideshow/locating-data-pieces-in-internet-edges/2657613 euronf-gwendal-091205182838-phpapp01
In age of cloud computing, any equipment can become server, e.g. set-top-boxes or access routers. For service providers, a challenge consists in accurately making use of these servers. We address the problem of locating a large service (or content) into these Internet edges so that the delivery to clients is efficient from a networking point of view.]]>
In age of cloud computing, any equipment can become server, e.g. set-top-boxes or access routers. For service providers, a challenge consists in accurately making use of these servers. We address the problem of locating a large service (or content) into these Internet edges so that the delivery to clients is efficient from a networking point of view.]]> Sat, 05 Dec 2009 18:27:48 GMT /slideshow/locating-data-pieces-in-internet-edges/2657613 gwendal@slideshare.net(gwendal) Optimal Network Locality in Distributed Services gwendal In age of cloud computing, any equipment can become server, e.g. set-top-boxes or access routers. For service providers, a challenge consists in accurately making use of these servers. We address the problem of locating a large service (or content) into these Internet edges so that the delivery to clients is efficient from a networking point of view. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/euronf-gwendal-091205182838-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In age of cloud computing, any equipment can become server, e.g. set-top-boxes or access routers. For service providers, a challenge consists in accurately making use of these servers. We address the problem of locating a large service (or content) into these Internet edges so that the delivery to clients is efficient from a networking point of view.
Optimal Network Locality in Distributed Services from Gwendal Simon
]]> 552 3 https://cdn.slidesharecdn.com/ss_thumbnails/euronf-gwendal-091205182838-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Cloud Engineering /gwendal/cloud-engineering courscloud2009-091116101416-phpapp02
An introduction to datacenter and cloud computing for master students in engineering]]>
An introduction to datacenter and cloud computing for master students in engineering]]> Mon, 16 Nov 2009 10:14:12 GMT /gwendal/cloud-engineering gwendal@slideshare.net(gwendal) Cloud Engineering gwendal An introduction to datacenter and cloud computing for master students in engineering <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courscloud2009-091116101416-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An introduction to datacenter and cloud computing for master students in engineering
Cloud Engineering from Gwendal Simon
]]> 1382 6 https://cdn.slidesharecdn.com/ss_thumbnails/courscloud2009-091116101416-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 peer-to-peer oppotunities /slideshow/peertopeer-oppotunities/2364443 p2p-ietr-091028050329-phpapp02
A short tour about peer-to-peer applications, and their opportunities, in Jan 2008. Attendees were members of a "research and development cluster" on multimedia and networking]]>
A short tour about peer-to-peer applications, and their opportunities, in Jan 2008. Attendees were members of a "research and development cluster" on multimedia and networking]]> Wed, 28 Oct 2009 05:03:08 GMT /slideshow/peertopeer-oppotunities/2364443 gwendal@slideshare.net(gwendal) peer-to-peer oppotunities gwendal A short tour about peer-to-peer applications, and their opportunities, in Jan 2008. Attendees were members of a "research and development cluster" on multimedia and networking <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/p2p-ietr-091028050329-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A short tour about peer-to-peer applications, and their opportunities, in Jan 2008. Attendees were members of a &quot;research and development cluster&quot; on multimedia and networking
peer-to-peer oppotunities from Gwendal Simon
]]> 833 3 https://cdn.slidesharecdn.com/ss_thumbnails/p2p-ietr-091028050329-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Infrastructureless Wireless networks /slideshow/infrastructureless-wireless-networks/2364333 p2p-m2m-091028044329-phpapp02
An overview of the basic algorithmic knowledge about ad-hoc and sensor networks for engineers.]]>
An overview of the basic algorithmic knowledge about ad-hoc and sensor networks for engineers.]]> Wed, 28 Oct 2009 04:43:21 GMT /slideshow/infrastructureless-wireless-networks/2364333 gwendal@slideshare.net(gwendal) Infrastructureless Wireless networks gwendal An overview of the basic algorithmic knowledge about ad-hoc and sensor networks for engineers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/p2p-m2m-091028044329-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An overview of the basic algorithmic knowledge about ad-hoc and sensor networks for engineers.
Infrastructureless Wireless networks from Gwendal Simon
]]> 8099 8 https://cdn.slidesharecdn.com/ss_thumbnails/p2p-m2m-091028044329-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-gwendal-48x48.jpg?cb=1531904590 I am Professor in a French grande 辿cole in France: perso.telecom-bretagne.eu/gwendalsimon/ https://cdn.slidesharecdn.com/ss_thumbnails/reproducibilityacmmmsys1-171203225609-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/reproducible-research-at-acm-mmsys/83273672 Reproducible research ... https://cdn.slidesharecdn.com/ss_thumbnails/netgames182-171201114600-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/netgames-history-and-preparing-2018-edition/83123440 Netgames: history and ... https://cdn.slidesharecdn.com/ss_thumbnails/gwendal-5g-vr-170612092543-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/virtual-reality-in-5g-networks/76859822 Virtual Reality in 5G ...