Evaluation of the nutritional embedding evaluation programme

Undernutrition among children remains a significant challenge in Kenya with 26% of children under the age of five registering low height-for-age ratios or, in other words, experiencing stunted growth. The problem is often attributed to parents’ scant knowledge of optimal feeding practices. Augmenting this knowledge by providing caregivers with information on nutrition has proven to be effective - inducing positive changes in caregivers’ behavior and, in turn, improving health outcomes among children. Designed to supply this critically needed information, NEEP was tested as a potentially impactful, cost-effective and scalable innovation to reduce undernutrition and improve growth outcomes among children

Towards a peer-to-peer bandwidth marketplace

Peer-to-peer systems are a popular means of transferring files over the Internet, accounting for a third of the upload bandwidth of end users as of 2013. However, recent studies have highlighted that peer-to-peer systems are affected by a lack of balance between the supply and demand of bandwidth. This imbalance stems from the skewed popularity distribution of the files transfered in the system; newly released files may exhibit an undersupply of bandwidth while older ones may exhibit oversupply. In this work, we introduce a bandwidth marketplace for peers, with the aim of aligning supply and demand without the need for human intervention. Peers constantly monitor their performance and gossip with each other about undersupplied files. Peers with idle upload bandwidth that learn about an undersupplied file can autonomously start a special help mode download, with the goal of supplying as much upload bandwidth as possible to the other peers. We present an analytical model of help mode downloading and derive from it bounds for the performance of helper peers. Furthermore, we evaluate a recent existing implementation of help mode in Libtorrent, a popular BitTorrent library. Our tests show that Libtorrent help mode is effective at alleviating undersupply, although its performance relative to our model can be improved

Investment strategies for credit-based P2P communities

P2P communities that use credits to incentivize their members to contribute have emerged over the last few years. In particular, private BitTorrent communities keep track of the total upload and download of each member and impose a minimum threshold for their upload/download ratio, which is known as their sharing ratio. It has been shown that these private communities have significantly better download performance than public communities. However, this performance is based on oversupply, and it has also been shown that it is hard for users to maintain a good sharing ratio to avoid being expelled from the community. In this paper, we address this problem by introducing a speculative download mechanism to automatically manage user contribution in BitTorrent private communities. This mechanism, when integrated in a BitTorrent client, identifies the swarms that have the biggest upload potential, and automatically downloads and seeds them. In other words, it tries to invests the bandwidth of the user in a profitable way. In order to accurately asses the upload potential of swarms we analyze a private BitTorrent community and derive through multiple regression a predictor for the upload potential based on simple parameters accessible to each peer. The speculative download mechanism uses the predictor to build a cache of profitable swarms to which the peer can contribute. Our results show that 75 % of investment decisions result in an increase in upload bandwidth utilization, with a median 207 % return on investment

Decentralized Credit Mining in P2P Systems

Accounting mechanisms based on credit are used in peer-to-peer systems to track the contribution of peers to the community for the purpose of deterring freeriding and rewarding good behavior. Most often, peers earn credit for uploading files, but other activities might be rewarded in the future as well, such as making useful comments or reporting spam. Credit earned can be used for accessing new content, or for receiving preferential treatment in case of network congestion. We define credit mining as the activity performed by peers for the purpose of earning credit. In this paper, we design, implement, and evaluate a system for decentralized credit mining that maximizes the contribution of idle peers to the community by automatically uploading popular files. Building on previous theoretical insights into the economics of communities, we select autonomous algorithms for bandwidth investment as the basis of our credit mining system. Additionally, we describe our experience with important challenges arising from Internet deployment, that are frequently neglected in emulation, including duplicate content avoidance, spam prevention, and the cost of keeping peer information updated. Furthermore, we implement an archival mode of operation, which prevents the disappearance of old content from thecommunity. We show the feasibility and usefulness of our credit mining system through measurements from our implementation on top of Tribler, an Internet-deployed peer-to-peer system.Distributed System

Towards a peer-to-peer bandwidth marketplace

Peer-to-peer systems are a popular means of transferring files over the Internet, accounting for a third of the upload bandwidth of end users as of 2013. However, recent studies have highlighted that peer-to-peer systems are affected by a lack of balance between the supply and demand of bandwidth. This imbalance stems from the skewed popularity distribution of the files transfered in the system; newly released files may exhibit an undersupply of bandwidth while older ones may exhibit oversupply. In this work, we introduce a bandwidth marketplace for peers, with the aim of aligning supply and demand without the need for human intervention. Peers constantly monitor their performance and gossip with each other about undersupplied files. Peers with idle upload bandwidth that learn about an undersupplied file can autonomously start a special help mode download, with the goal of supplying as much upload bandwidth as possible to the other peers. We present an analytical model of help mode downloading and derive from it bounds for the performance of helper peers. Furthermore, we evaluate a recent existing implementation of help mode in Libtorrent, a popular BitTorrent library. Our tests show that Libtorrent help mode is effective at alleviating undersupply, although its performance relative to our model can be improved

The BTWorld use case for big data analytics : Description, MapReduce logical workflow, and empirical evaluation

The commoditization of big data analytics, that is, the deployment, tuning, and future development of big data processing platforms such as MapReduce, relies on a thorough understanding of relevant use cases and workloads. In this work we propose BTWorld, a use case for time-based big data analytics that is representative for processing data collected periodically from a global-scale distributed system. BTWorld enables a data-driven approach to understanding the evolution of BitTorrent, a global file-sharing network that has over 100 million users and accounts for a third of today's upstream traffic. We describe for this use case the analyst questions and the structure of a multi-terabyte data set. We design a MapReduce-based logical workflow, which includes three levels of data dependency — inter-query, inter-job, and intra-job — and a query diversity that make the BTWorld use case challenging for today's big data processing tools; the workflow can be instantiated in various ways in the MapReduce stack. Last, we instantiate this complex workflow using Pig-Hadoop-HDFS and evaluate the use case empirically. Our MapReduce use case has challenging features: small (kilobytes) to large (250 MB) data sizes per observed item, excellent (10^-6) and very poor (10^2) selectivity, and short (seconds) to long (hours) job duration

On swarm-level resource allocation in BitTorrent communities

BitTorrent is a peer-to-peer computer network protocol for sharing content in an efficient and scalable way. Modeling and analysis of the popular private BitTorrent communities has become an active area of research. In these communities users are strongly incentivized to contribute their resources, i.e., to share their files. In BitTorrent terminology, users who have finished downloading files and stay online to share these files with others in the network are called seeders. The combination of seeders and downloaders of a file is called a swarm. In this paper we examine and evaluate the efficiency of the resource allocation of seeders in multiple swarms. This is formulated as an integer linear fractional programming problem. The evaluation is done on traces representing two existing BitTorrent communities. We find that in communities, particularly with low users-to-files ratio (which is typically the case), there is room for improvementSoftware Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Investment strategies for credit-based P2P communities

P2P communities that use credits to incentivize their members to contribute have emerged over the last few years. In particular, private BitTorrent communities keep track of the total upload and download of each member and impose a minimum threshold for their upload/download ratio, which is known as their sharing ratio. It has been shown that these private communities have significantly better download performance than public communities. However, this performance is based on oversupply, and it has also been shown that it is hard for users to maintain a good sharing ratio to avoid being expelled from the community. In this paper, we address this problem by introducing a speculative download mechanism to automatically manage user contribution in BitTorrent private communities. This mechanism, when integrated in a BitTorrent client, identifies the swarms that have the biggest upload potential, and automatically downloads and seeds them. In other words, it tries to invests the bandwidth of the user in a profitable way. In order to accurately asses the upload potential of swarms we analyze a private BitTorrent community and derive through multiple regression a predictor for the upload potential based on simple parameters accessible to each peer. The speculative download mechanism uses the predictor to build a cache of profitable swarms to which the peer can contribute. Our results show that 75 % of investment decisions result in an increase in upload bandwidth utilization, with a median 207 % return on investment