A Decentralized Online Social Network with Efficient User-Driven Replication

Unprecedented growth of online social networks (OSNs) increasingly makes privacy advocates and government agencies worrisome alike. In this paper, we propose My3, a privacy-friendly decentralized alternative for online social networking. The My3 system exploits well-known interesting properties of the current online social networks in its novel design namely, locality of access, predictable access times, geolocalization of friends, unique access requirements of the social content, and implicit trust among friends. It allows users to exercise finer granular access control on the content, thus making My3 extremely privacy-preserving. Moreover, we propose different replication strategies that users may independently choose for meeting their personalized performance objectives. A detailed performance study evaluates the system regarding profile availability, access delay, freshness and storage load. By using real-world data traces, we prove that My3 offers high availability even with low average online time of users in the network

Revisiting Content Availability in Distributed Online Social Networks

Online Social Networks (OSN) are among the most popular applications in today's Internet. Decentralized online social networks (DOSNs), a special class of OSNs, promise better privacy and autonomy than traditional centralized OSNs. However, ensuring availability of content when the content owner is not online remains a major challenge. In this paper, we rely on the structure of the social graphs underlying DOSN for replication. In particular, we propose that friends, who are anyhow interested in the content, are used to replicate the users content. We study the availability of such natural replication schemes via both theoretical analysis as well as simulations based on data from OSN users. We find that the availability of the content increases drastically when compared to the online time of the user, e. g., by a factor of more than 2 for 90% of the users. Thus, with these simple schemes we provide a baseline for any more complicated content replication scheme.Comment: 11pages, 12 figures; Technical report at TU Berlin, Department of Electrical Engineering and Computer Science (ISSN 1436-9915

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed