Idrissa Sarr

DTR: Distributed Transaction Routing in a Large Scale Network

Grid systems provide access to huge storage and computing resources at large scale. While they have been mainly dedicated to scientific computing for years, grids are now considered as a viable solution for hosting data-intensive applications. To this end, databases are replicated over the grid in order to achieve high availability and fast transaction processing thanks to parallelism. However, achieving both fast and consistent data access on such architectures is challenging at many points. In particular, centralized control is prohibited because of its vulnerability and lack of efficiency at large scale. In this article, we propose a novel solution for the distributed control of transaction routing in a large scale network. We leverage a cluster-oriented routing solution with a fully distributed approach that uses a large scale distributed directory to handle routing metadata. Moreover, we demonstrate the feasibility of our implementation through experimentation: results expose linear scale-up, and transaction routing time is fast enough to make our solution eligible for update intensive applications such as world wide online booking.

TransPeer: adaptive distributed transaction monitoring for Web2.0 applications

In emerging Web2.0 applications such as virtual worlds or social networking websites, the number of users is very important (tens of thousands), hence the amount of data to manage is huge and dependability is a crucial issue. The large scale prevents from using centralized approaches or locking/two-phase-commit approach. Moreover, Web2.0 applications are mostly interactive, which means that the response time must always be less than few seconds. To face these problems, we present a novel solution, TransPeer, that allows applications to scale-up without the need to buy expensive resources at a data center. To this end, databases are replicated over a P2P system in order to achieve high availability and fast transaction processing thanks to parallelism. A distributed shared dictionary, implemented on top of a DHT, contains metadata used for routing transactions efficiently. Both metadata and data are accessed in an optimistic way: there is no locking on metadata and transactions are executed on nodes in a tentative way. We demonstrate the feasibility of our approaches through experimentation.

Managing node disappearance based on information flow in social networks

Social networks are dynamic structures in which entities and links appear and disappear for different reasons. Starting from the observation that each entity plays a more or less important role in transmitting the information inside a network, the objective of this article is to propose a method which exploits the role played by a given node to both estimate the impact of its disappearance on the information flow, and conduct network changes to restore the information flow with a similar quality as before the node disappearance. To this end, we propose a network restructuring approach that categorizes nodes into critical and non-critical classes based on their role, and hence, manages their disappearance appropriately by adding new links in a parsimonious way and selecting a substitute for a deleted critical node. As opposed to a previously defined solution, our approach adds links that are just enough to maintain the quality of the information flow within the network as before a node deletion. A prototype is designed and implemented using an open source social network analysis library (NetworkX). Its validation is conducted using network data sets with various sizes. The empirical study shows a low network update, a quite constant quality of the information flow and reasonable execution times after a node deletion.

Social Network Analysis - Community Detection and Evolution

This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edited work will appeal to researchers, practitioners and students interested in the latest developments of social network analysis.

Dealing with Disappearance of an Actor Set in Social Networks

Social networks are dynamic structures that contain a set of entities and links. In such a dynamic environment, a specific node or a group of nodes can play an important role in the information flow transmission within the network and therefore, its disappearance may lead to a disconnected network or a breakdown in the information flow. The objective of this paper is to extend our previous work on managing a node disappearance to handling the disappearance of a group of nodes. The proposed approach relies on the role played by a group of nodes to conduct network changes and maintain the network connected while restoring the information flow with a similar quality as before the group disappearance. We consider two situations (only one versus many communities) and categorize groups of nodes into three classes (scattered, contiguous and hybrid). Hence, we manage a group disappearance with respect to its class and the network topology by adding new links in a parsimonious way and finding a substitute for a leaving group. Our approach differs from existing link prediction solutions by the fact that it uses the information flow quality as a key performance indicator to identify the potential links to add and/or the possible substitute to a disappearing group. We implement a prototype by using an open source social network analysis library (NetworkX) and we validate our solution through experiments. The results show the benefits of our solution in terms of response time and the number of added links.

Blockchain-based Model for Social Transactions Processing

The goal of this work in progress is to handle transactions of social applications by using their access classes. Basically, social users access simultaneously to a small piece of data owned by a user or a few ones. For instance, a new post of a Facebook user can create the reactions of most of his/her friends, and each of such reactions is related to the same data. Thus, grouping or chaining transactions that require the same access classes may reduce significantly the response time since several transactions are executed in one shot while ensuring consistency as well as minimizing the number of access to the persistent data storage. With this insight, we propose a middleware-based transaction scheduler that uses various strategies to chain transactions based on their access classes. The key novelties lie in (1) our distributed transaction scheduling devised on top of a ring to ensure communication when chaining transactions and (2) our ability to deal with multi-partitions transactions. The scheduling phase is based on Blockchain principle, which means in our context to record all transactions requiring the same access class into a master list in order to ensure consistency and to plan efficiently their processing. We designed and simulated our approach using SimJava and preliminary results show interesting and promising results.

Overlaying Social Networks of Different Perspectives for Inter-network Community Evolution

In many real-life social networks, a group of individuals may be involved in multiple kinds of activities such as professional, leisure and friendship ones. Even though individuals may belong to a social network with a very precise type of links such as professional ties in LinkedIn, the interactions that may happen in other social networks such as Facebook are not reflected in the original network. We believe that overlaying networks with various types of links helps discover interesting patterns. The objective of this paper is then to overlay two or many social networks with different kinds of social activities in order to unveil homogeneous groups that could not appear in a unique social network. To that end, we propose a community detection approach based on possibility theory, which identifies time-based perspective communities for each kind of social activities that occur within a sequence of time windows. Furthermore, different perspectives are layered to detect communities that may belong to several networks in a given time period. Discovered communities in a given network for a time period can be perceived as views or perspectives in one or many networks.

Database replication in large scale systems: optimizing the number of replicas

In distributed systems, replication is used for ensuring availability and increasing performances. However, the heavy workload of distributed systems such as web2.0 applications or Global Distribution Systems, limits the benefit of replication if its degree (i.e., the number of replicas) is not controlled. Since every replica must perform all updates eventually, there is a point beyond which adding more replicas does not increase the throughput, because every replica is saturated by applying updates. Moreover, if the replication degree exceeds the optimal threshold, the useless replica would generate an overhead due to extra communication messages. In this paper, we propose a suitable replication management solution in order to reduce useless replicas. To this end, we define two mathematical models which approximate the appropriate number of replicas to achieve a given level of performance. Moreover, we demonstrate the feasibility of our replication management model through simulation. The results expose the effectiveness of our models and their accuracy.

An Accurate Probabilistic Model for Community Evolution Analysis in Social Network

The scope of the work is to build a framework able to study the evolution of a set of communities based on their underlying social activities. Generally, for a given community, many subgroups may exist and evolve with different and various opinions or behaviors. So, in this paper, we will focus on the identification of the potential subgroups and their potential relation/correlation corresponding to self-similarity over time. Clearly, we want to know if the subgroups remain unchanged then being stable or might they evolve to merge by forming new groups. In this respect, social engagement that refers to the participation of actors from a community to the activities of a social group is used to distribute activities into several classes. So building subgroups will be our first challenge and analyzing temporal correlation between them will be another interesting issue in this present work. The first problem can be solved by analyzing the activities inside the given initial community. We believe that, in many situations, activities should be characterized by parametric distributions as the gaussians. So, by means of the gaussian mixture modeler (GMM), subgroups can be identified successfully. Thereafter, the intrinsic relation between subgroups and their temporal evolution can be studied clearly with the calibration of hidden Markov models (HMM). The achievement of this study can help management operators to take decisions in two ways: i) since each GMM subgroup may correspond to a single individuals opinion/behavior, typical decision could be made for a given social group ii) also, the manager can take advantageous decisions by merging opinions for subgroups which have self-similarities, the HMM is here to learn more about this issue. We show the effectiveness of our approach by using real life data from Reddit.com.

Group disappearance in social networks with communities

The purpose of this paper is to handle the disappearance of a group of nodes in a social network. The quality of the information flow is used as a key performance indicator to conduct network changes after group disappearance. Nodes as well as node sets are first classified into categories (critical and non-critical nodes, and scattered, contiguous and hybrid groups) and then analyzed according to two distinct perspectives: the network as a whole or its identified communities. Finally, algorithms are devised to manage group disappearance according to different cases. New links are added in a parsimonious way and a possible substitute for a leaving group is found based on the adage “birds of a feather flock together” and the homophily principle. This means that new links (e.g., relationships) and a potential substitute are found only between individuals that share common characteristics such as beliefs, values, and education, i.e., individuals that are more likely neighbors of the leaving node or group. To validate our approach, an empirical study is conducted using various kinds of data sets and a set of criteria. The results show the benefits of our solution in terms of response time, number of added links and metrics of the overall network topology.