New token passing distributed mutual exclusion algorithm

Eliminating interference between concurrently executing activities through mutual exclusion is one of the most fundamental problems in computer systems. The problem of mutual exclusion in a distributed system is especially interesting owing to the lack of global knowledge in the presence of variable communication delays. In this paper, a new token-based distributed mutual exclusion algorithm is proposed. The algorithm incurs approximately three messages at high loads, irrespective of the number of nodes N in the system. At low loads, it requires approximately N messages. The paper also addresses failure recovery issues, such as token loss

MPG: Not so random exploration of a city

The proliferation of mobile, ubiquitous and spatial computing has led to a number of services aiming into facilitate the exploration of a city. Platforms such as Foursquare and Yelp curate information about establishments in an area that can then be used for recommendation purposes. Traditionally an approach followed by these systems is to rank places based on their popularity, proximity or any other feature that represents the quality of the venue and then return the top-k of them. However, this approach, while simple and intuitive, is not necessarily providing a diverse set of recommendations, since similar venues typically are ranked closely. Therefore, in this paper we design and introduce MPG (which stands for Mobile Personal Guide), a mobile service that provides a set of diverse venue recommendations better aligned with user preferences. MPG takes into consideration the user preferences (e.g., distance willing to cover, types of venues interested in exploring, etc.), the popularity of the establishments, as well as their distance from the current location of the user by combining them in a single composite score. We evaluate our approach using a large-scale dataset of approximately 14 million venues collected from Foursquare. Our results indicate that MPG can increase coverage of the result set compared to the baselines considered. It further achieves a significantly better Relevancy-Diversity trade-off ratio

Enforcing policy and data consistency of cloud transactions

In distributed transactional database systems deployed over cloud servers, entities cooperate to form proofs of authorizations that are justified by collections of certified credentials. These proofs and credentials may be evaluated and collected over extended time periods under the risk of having the underlying authorization policies or the user credentials being in inconsistent states. It therefore becomes possible for a policy-based authorization systems to make unsafe decisions that might threaten sensitive resources. In this paper, we highlight the criticality of the problem. We then present the first formalization of the concept of trusted transactions when dealing with proofs of authorizations. Accordingly, we define different levels of policy consistency constraints and present different enforcement approaches to guarantee the trustworthiness of transactions executing on cloud servers. We propose a Two-Phase Validation Commit protocol as a solution, that is a modified version of the basic Two-Phase Commit protocols. We finally provide performance analysis of the different presented approaches to guide the decision makers in which approach to use. © 2011 IEEE

A Framework for Systematic Synthesis of Transactional Middleware

Transactions are contracts that guarantee a consistent, transparent, individual system state transition and their use is widespread in many different kinds of computing systems. Some well known standards (e.g. Corba) include the specification of services that provide transactional properties. In this paper, we present a formal method for the systematic synthesis of transactional middleware based on the combination of the aforementioned services. The synthesis of transactional middleware is based on (i) the formal specification of transactional properties and (ii) stub code generation. 1 Introduction The construction of a large-scale distributed software system is a complex task that involves design decisions relating to different aspects of the system's behavior. In particular, the developer has to address the provision of the system's functional (i.e. algorithmic aspect) and nonfunctional (i.e. quality of service with which the system's functions are performed) properties. Whi..

Big data visualization and analytics: Future research challenges and emerging applications

© 2020 Copyright for this paper by its author(s). In the context of data visualization and analytics, this report outlines some of the challenges and emerging applications that arise in the Big Data era. In particularly, fourteen distinguished scientists from academia and industry, and diverse related communities, i.e., Information Visualization, Human-Computer Interaction, Machine Learning, Data management & Mining, and Computer Graphics have been invited to express their opinions

Database Consistency Models

International audienceA data store allows application processes to put and get data from a shared memory. In general, a data store cannot be modelled as a strictly sequential process. Applications observe non-sequential behaviours, called anomalies. The set of possible behaviours, and conversely of possible anomalies, constitutes the consistency model of the data store