Shuffling with a Croupier: Nat-Aware Peer-Sampling

Despite much recent research on peer-to-peer (P2P) protocols for the Internet, there have been relatively few practical protocols designed to explicitly account for Network Address Translation gateways (NATs). Those P2P protocols that do handle NATs circumvent them using relaying and hole-punching techniques to route packets to nodes residing behind NATs. In this paper, we present Croupier, a peer sampling service (PSS) that provides uniform random samples of nodes in the presence of NATs in the network. It is the first NAT-aware PSS that works without the use of relaying or hole-punching. By removing the need for relaying and hole-punching, we decrease the complexity and overhead of our protocol as well as increase its robustness to churn and failure. We evaluated Croupier in simulation, and, in comparison with existing NAT-aware PSS’, our results show similar randomness properties, but improved robustness in the presence of both high percentages of nodes behind NATs and massive node failures. Croupier also has substantially lower protocol overhead

Live Streaming in P2P and Hybrid P2P-Cloud Environments for the Open Internet

Peer-to-Peer (P2P) live media streaming is an emerging technology that reduces the barrier to stream live events over the Internet. However, providing a high quality media stream using P2P overlay networks is challenging and gives raise to a number of issues: (i) how to guarantee quality of the service (QoS) in the presence of dynamism, (ii) how to incentivize nodes to participate in media distribution, (iii) how to avoid bottlenecks in the overlay, and (iv) how to deal with nodes that reside behind Network Address Translators gateways (NATs). In this thesis, we answer the above research questions in form of new algorithms and systems. First of all, we address problems (i) and (ii) by presenting our P2P live media streaming solutions: Sepidar, which is a multiple-tree overlay, and GLive, which is a mesh overlay. In both models, nodes with higher upload bandwidth are positioned closer to the media source. This structure reduces the playback latency and increases the playback continuity at nodes, and also incentivizes the nodes to provide more upload bandwidth. We use a reputation model to improve participating nodes in media distribution in Sepidar and GLive. In both systems, nodes audit the behaviour of their directly connected nodes by getting feedback from other nodes. Nodes who upload more of the stream get a relatively higher reputation, and proportionally higher quality streams. To construct our streaming overlay, we present a distributed market model inspired by Bertsekas auction algorithm, although our model does not rely on a central server with global knowledge. In our model, each node has only partial information about the system. Nodes acquire knowledge of the system by sampling nodes using the Gradient overlay, where it facilitates the discovery of nodes with similar upload bandwidth. We address the bottlenecks problem, problem (iii), by presenting CLive that satisfies real-time constraints on delay between the generation of the stream and its actual delivery to users. We resolve this problem by borrowing some resources (helpers) from the cloud, upon need. In our approach, helpers are added on demand to the overlay, to increase the amount of total available bandwidth, thus increasing the probability of receiving the video on time. As the use of cloud resources costs money, we model the problem as the minimization of the economical cost, provided that a set of constraints on QoS is satisfied. Finally, we solve the NAT problem, problem (iv), by presenting two NAT-aware peer sampling services (PSS): Gozar and Croupier. Traditional gossip-based PSS breaks down, where a high percentage of nodes are behind NATs. We overcome this problem in Gozar using one-hop relaying to communicate with the nodes behind NATs. Croupier similarly implements a gossip-based PSS, but without the use of relaying

Gozar: NAT-friendly Peer Sampling with One-Hop Distributed NAT Traversal

Gossip-based peer sampling protocols have been widely used as a building block for many large-scale distributed applications. However, Network Address Translation gateways (NATs) cause most existing gossiping protocols to break down, as nodes cannot establish direct connections to nodes behind NATs (private nodes). In addition, most of the existing NAT traversal algorithms for establishing connectivity to private nodes rely on third party servers running at a well-known, public IP addresses. In this paper, we present Gozar, a gossip-based peer sampling service that: (i) provides uniform random samples in the presence of NATs, and (ii) enables direct connectivity to sampled nodes using a fully distributed NAT traversal service, where connection messages require only a single hop to connect to private nodes. We show in simulation that Gozar preserves the randomness properties of a gossip-based peer sampling service. We show the robustness of Gozar when a large fraction of nodes reside behind NATs and also in catastrophic failure scenarios. For example, if 80% of nodes are behind NATs, and 80% of the nodes fail, more than 92% of the remaining nodes stay connected. In addition, we compare Gozar with existing NAT-friendly gossip-based peer sampling services, Nylon and ARRG. We show that Gozar is the only system that supports one-hop NAT traversal, and its overhead is roughly half of Nylon’s

Distributed Optimization of P2P Media Delivery Overlays

Media streaming over the Internet is becoming increasingly popular. Currently, most media is delivered using global content-delivery networks, providing a scalable and robust client-server model. However, content delivery infrastructures are expensive. One approach to reduce the cost of media delivery is to use peer-to-peer (P2P) overlay networks, where nodes share responsibility for delivering the media to one another. The main challenges in P2P media streaming using overlay networks include: (i) nodes should receive the stream with respect to certain timing constraints, (ii) the overlay should adapt to the changes in the network, e.g., varying bandwidth capacity and join/failure of nodes, (iii) nodes should be intentivized to contribute and share their resources, and (iv) nodes should be able to establish connectivity to the other nodes behind NATs. In this work, we meet these requirements by presenting P2P solutions for live media streaming, as well as proposing a distributed NAT traversal solution. First of all, we introduce a distributed market model to construct an approximately minimal height multiple-tree streaming overlay for content delivery, in gradienTv. In this system, we assume all the nodes are cooperative and execute the protocol. However, in reality, there may exist some opportunistic nodes, free-riders, that take advantage of the system, without contributing to content distribution. To overcome this problem, we extend our market model in Sepidar to be effective in deterring free-riders. However, gradienTv and Sepidar are tree-based solutions, which are fragile in high churn and failure scenarios. We present a solution to this problem in GLive that provides a more robust overlay by replacing the tree structure with a mesh. We show in simulation, that the mesh-based overlay outperforms the multiple-tree overlay. Moreover, we compare the performance of all our systems with the state-of-the-art NewCoolstreaming, and observe that they provide better playback continuity and lower playback latency than that of NewCoolstreaming under a variety of experimental scenarios. Although our distributed market model can be run against a random sample of nodes, we improve its convergence time by executing it against a sample of nodes taken from the Gradient overlay. The Gradient overlay organizes nodes in a topology using a local utility value at each node, such that nodes are ordered in descending utility values away from a core of the highest utility nodes. The evaluations show that the streaming overlays converge faster when our market model works on top of the Gradient overlay. We use a gossip-based peer sampling service in our streaming systems to provide each node with a small list of live nodes. However, in the Internet, where a high percentage of nodes are behind NATs, existing gossiping protocols break down. To solve this problem, we present Gozar, a NAT-friendly gossip-based peer sampling service that: (i) provides uniform random samples in the presence of NATs, and (ii) enables direct connectivity to sampled nodes using a fully distributed NAT traversal service. We compare Gozar with the state-of-the-art NAT-friendly gossip-based peer sampling service, Nylon, and show that only Gozar supports one-hop NAT traversal, and its overhead is roughly half of Nylon’s

GLive: The Gradient overlay as a market maker for mesh-based P2P live streaming

Peer-to-Peer (P2P) live video streaming over the Internet is becoming increasingly popular, but it is still plagued by problems of high playback latency and intermittent playback streams. This paper presents GLive, a distributed market-based solution that builds a mesh overlay for P2P live streaming. The mesh overlay is constructed such that (i) nodes with increasing upload bandwidth are located closer to the media source, and (ii) nodes with similar upload bandwidth become neighbours. We introduce a market-based approach that matches nodes willing and able to share the stream with one another. However, market-based approaches converge slowly on random overlay networks, and we improve the rate of convergence by adapting our market-based algorithm to exploit the clustering of nodes with similar upload bandwidths in our mesh overlay. We address the problem of free-riding through nodes preferentially uploading more of the stream to the best uploaders. We compare GLive with our previous tree-based streaming protocol, Sepidar, and NewCoolstreaming in simulation, and our results show significantly improved playback continuity and playback latency

Vitis: A Gossip-based Hybrid Overlay for Internet-scale Publish/Subscribe

Peer-to-peer overlay networks are attractive solutions for building Internet-scale publish/subscribe systems. However, scalability comes with a cost: a message published on a certain topic often needs to traverse a large number of uninterested (unsubscribed) nodes before reaching all its subscribers. This might sharply increase resource consumption for such relay nodes (in terms of bandwidth transmission cost, CPU, etc) and could ultimately lead to rapid deterioration of the system’s performance once the relay nodes start dropping the messages or choose to permanently abandon the system. In this paper, we introduce Vitis, a gossip-based publish/subscribe system that significantly decreases the number of relay messages, and scales to an unbounded number of nodes and topics. This is achieved by the novel approach of enabling rendezvous routing on unstructured overlays. We construct a hybrid system by injecting structure into an otherwise unstructured network. The resulting structure resembles a navigable small-world network, which spans along clusters of nodes that have similar subscriptions. The properties of such an overlay make it an ideal platform for efficient data dissemination in large-scale systems. We perform extensive simulations and evaluate Vitis by comparing its performance against two base-line publish/subscribe systems: one that is oblivious to node subscriptions, and another that exploits the subscription similarities. Our measurements show that Vitis significantly outperforms the base-line solutions on various subscription and churn scenarios, from both synthetic models and real-world traces

Vinifera: A Gossip-based Hybrid Overlay for Content-based Publish/Subscribe

CN

DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation

Deep learning and remote sensing techniques have significantly advanced water monitoring abilities; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DeepAqua, a self-supervised deep learning model that leverages knowledge distillation (a.k.a. teacher-student model) to eliminate the need for manual annotations during the training phase. We utilize the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images, and to train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. DeepAqua represents a significant advancement in computer vision techniques by effectively training semantic segmentation models without any manually annotated data. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 7%, Intersection Over Union by 27%, and F1 score by 14%. This approach offers a practical solution for monitoring wetland water extent changes without needing ground truth data, making it highly adaptable and scalable for wetland conservation efforts.Comment: 29 pages, 8 figures, 1 tabl

A Distributed Algorithm For Large-Scale Graph Partitioning

Detta kandidatarbete har sin placering på Skeppsbron/Skeppsbrokajen i centrala Stockholm. Inriktningen jag valde var att rita ett förslag för en fiskmarknad som skulle placeras på denna plats. Mitt arbete har fått inspireras av Sveriges största och mest kända fiskmarknad, Feskekörkan, i Göteborg. Analyser av Feskekörkan som organisation och dess planlösning har i mitt arbete lett till en tektoniskt uppbyggd struktur där material byggnadskonstruktion var viktiga element. Med bland annat en fiskfjällsfasad i mässing och en bärande skelett av storskaliga limträbalkar. Platsen som byggnaden ligger på är ett välbesökt promenadstråk med en bred och lång kajkant som används flitigt av såväl, turister som besöker gamla stan och det kungliga slottet, och Stockholmare som tar sig mellan Södermalm och Norrmalm. Jag har valt att bebygga platsen på ett sätt som både tar vara på det vackra promenadstråket men också ger möjlighet för besökande att stanna upp och ta del av fiskmarknaden.This candidate's work has its placement on Skeppsbron/Skeppsbrokajen in the central area of Stockholm. The focus I chose was to draw a proposal for a fishmarket that would be placed at this location. My work has been inspired by the largest and most famous fish market in Sweden, Feskekörkan, in Gothenburg. Analyses of Feskekörkan’s organization and its plan has, in my work, led to a tectonically constructed structure where building materials were important elements. Including a fish scale facade made of brass and a bearing skeleton of large glulam beams. The place which the building is situated on a popular promenade with a broad and long quay which is widely used by both, tourists visiting the Old Town and the Royal Palace, and the Stockholm citizens who ride between Södermalm and Norrmalm. I have chosen to build on the site in a way that both takes advantage of the beautiful promenade but also provides the opportunity for visitors to stop and take some of the fish market

ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER

Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-predicted outputs to entity categories, which are complicated undertakings. To address these challenges, we present ContrastNER, a prompt-based NER framework that employs both discrete and continuous tokens in prompts and uses a contrastive learning approach to learn the continuous prompts and forecast entity types. The experimental results demonstrate that ContrastNER obtains competitive performance to the state-of-the-art NER methods in high-resource settings and outperforms the state-of-the-art models in low-resource circumstances without requiring extensive manual prompt engineering and verbalizer design.Comment: 9 pages, 5 figures, COMPSAC202