Tit-for-Token: fair rewards for moving data in decentralized storage networks

Centralized data silos are not only becoming prohibitively expensive but also raise issues of data ownership and data availability. These developments are affecting the industry, researchers, and ultimately society in general. Decentralized storage solutions present a promising alternative. Furthermore, such systems can become a crucial layer for new paradigms of edge-centric computing and web3 applications. Decentralized storage solutions based on p2p networks can enable scalable and self-sustaining open-source infrastructures. However, like other p2p systems, they require well-designed incentive mechanisms for participating peers. These mechanisms should be not only effective but also fair in regard to individual participants. Even though several such systems have been studied in deployment, there is still a lack of systematic understanding regarding these issues. We investigate the interplay between incentive mechanisms, network characteristics, and fairness of peer rewards. In particular, we identify and evaluate three core and up-to-date reward mechanisms for moving data in p2p networks: distance-based payments, reciprocity, and time-limited free service. Distance-based payments are relevant since libp2p Kademlia, which enables distance-based algorithms for content lookup and retrieval, is part of various modern p2p systems. We base our model on the Swarm network that uses a combination of the three mechanisms and serves as inspiration for our Tit-for-Token model. We present our Tit-for-Token model and develop a tool to explore the behaviors of these payment mechanisms. Our evaluation provides novel insights into the functioning and interplay of these mechanisms and helps. Based on these insights, we propose modifications to these mechanisms that better address fairness concerns and outline improvement proposals for the Swarm network

Cognitive networking techniques on content distribution networks

First we want to design a strategy based on Artificial Intelligence (AI) techniques with the aim of increasing peers download performance. Some AI algorithms can find patterns in the information available to a peer locally, and use it to predict values that cannot be calculated by means of mathematical formulas. An important aspect of these techniques is that can be trained in order to improve its interpretation of the local available information. With this process they can make more accurate predictions and perform better results. We will use this prediction system to increase our knowledge about the swarm and the peers who are part of it. This global knowledge increase can be used to optimize the algorithms of BitTorrent and can represent a great improvement in peers download capacity. Our second challenge is to create a reduced group of peers (Crowd) that focus their efforts on improving the condition of the swarm through collaborative techniques. The basic idea of this approach is to organize a group of peers to act as a single node and focus them on getting all pieces of the content they are interested in. This involves avoiding, as far as possible, to download pieces that any of the members already have. The main goal of this technique consists of reaching as quickly as possible a copy of the content distributed between all members of the Crowd. Getting a distributed copy of the content is expected to increase the availability of parts and reduce dependence on the seeds (users who have the complete content), which would represent a great benefit for the whole swarm. Another aspect that we want to investigate is the use of a priority system among members of the Crowd. We consider that in certain situations to prioritize the Crowd peers at expense of regular peers can result in a significant increase of the download ratio

Signaling and Reciprocity:Robust Decentralized Information Flows in Social, Communication, and Computer Networks

Complex networks exist for a number of purposes. The neural, metabolic and food networks ensure our survival, while the social, economic, transportation and communication networks allow us to prosper. Independently of the purposes and particularities of the physical embodiment of the networks, one of their fundamental functions is the delivery of information from one part of the network to another. Gossip and diseases diffuse in the social networks, electrochemical signals propagate in the neural networks and data packets travel in the Internet. Engineering networks for robust information flows is a challenging task. First, the mechanism through which the network forms and changes its topology needs to be defined. Second, within a given topology, the information must be routed to the appropriate recipients. Third, both the network formation and the routing mechanisms need to be robust against a wide spectrum of failures and adversaries. Fourth, the network formation, routing and failure recovery must operate under the resource constraints, either intrinsic or extrinsic to the network. Finally, the autonomously operating parts of the network must be incentivized to contribute their resources to facilitate the information flows. This thesis tackles the above challenges within the context of several types of networks: 1) peer-to-peer overlays – computers interconnected over the Internet to form an overlay in which participants provide various services to one another, 2) mobile ad-hoc networks – mobile nodes distributed in physical space communicating wirelessly with the goal of delivering data from one part of the network to another, 3) file-sharing networks – networks whose participants interconnect over the Internet to exchange files, 4) social networks – humans disseminating and consuming information through the network of social relationships. The thesis makes several contributions. Firstly, we propose a general algorithm, which given a set of nodes embedded in an arbitrary metric space, interconnects them into a network that efficiently routes information. We apply the algorithm to the peer-to-peer overlays and experimentally demonstrate its high performance, scalability as well as resilience to continuous peer arrivals and departures. We then shift our focus to the problem of the reliability of routing in the peer-to-peer overlays. Each overlay peer has limited resources and when they are exhausted this ultimately leads to delayed or lost overlay messages. All the solutions addressing this problem rely on message redundancy, which significantly increases the resource costs of fault-tolerance. We propose a bandwidth-efficient single-path Forward Feedback Protocol (FFP) for overlay message routing in which successfully delivered messages are followed by a feedback signal to reinforce the routing paths. Internet testbed evaluation shows that FFP uses 2-5 times less network bandwidth than the existing protocols relying on message redundancy, while achieving comparable fault-tolerance levels under a variety of failure scenarios. While the Forward Feedback Protocol is robust to message loss and delays, it is vulnerable to malicious message injection. We address this and other security problems by proposing Castor, a variant of FFP for mobile ad-hoc networks (MANETs). In Castor, we use the same general mechanism as in FFP; each time a message is routed, the routing path is either enforced or weakened by the feedback signal depending on whether the routing succeeded or not. However, unlike FFP, Castor employs cryptographic mechanisms for ensuring the integrity and authenticity of the messages. We compare Castor to four other MANET routing protocols. Despite Castor's simplicity, it achieves up to 40% higher packet delivery rates than the other protocols and recovers at least twice as fast as the other protocols in a wide range of attacks and failure scenarios. Both of our protocols, FFP and Castor, rely on simple signaling to improve the routing robustness in peer-to-peer and mobile ad-hoc networks. Given the success of the signaling mechanism in shaping the information flows in these two types of networks, we examine if signaling plays a similar crucial role in the on-line social networks. We characterize the propagation of URLs in the social network of Twitter. The data analysis uncovers several statistical regularities in the user activity, the social graph, the structure of the URL cascades as well as the communication and signaling dynamics. Based on these results, we propose a propagation model that accurately predicts which users are likely to mention which URLs. We outline a number of applications where the social network information flow modelling would be crucial: content ranking and filtering, viral marketing and spam detection. Finally, we consider the problem of freeriding in peer-to-peer file-sharing applications, when users can download data from others, but never reciprocate by uploading. To address the problem, we propose a variant of the BitTorrent system in which two peers are only allowed to connect if their owners know one another in the real world. When the users know which other users their BitTorrent client connects to, they are more likely to cooperate. The social network becomes the content distribution network and the freeriding problem is solved by leveraging the social norms and reciprocity to stabilize cooperation rather than relying on technological means. Our extensive simulation shows that the social network topology is an efficient and scalable content distribution medium, while at the same time provides robustness to freeriding