Xavier Bonnaire

Brief paper: A revision of recent approaches for two-dimensional strip-packing problems

In this paper, we present a review of the recent approaches proposed in the literature for strip-packing problems. Many of them have been concurrently published, given some similar results for the same set of benchmarks. Due to the quantity of published papers, it is difficult to ascertain the level of current research in this area.

Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective

Abstract The Internet of Things (IoT) generates massive streams of data which call for ever more efficient real time processing. Designing and implementing a big data service for the real time processing of such data requires an extensive knowledge of both input load and data distribution in order to provide a service which can cope with the workload. In this context, we study in this paper the challenges inherent to the real time processing of massive data flows from the IoT. We provide a detailed analysis of traces gathered from a well-known healthcare sport-oriented application in order to illustrate our conclusions from a big data perspective.

DRing: A Layered Scheme for Range Queries over DHTs

Traditional DHT structures provide very poor support for range queries, since uniform hashing destroys data locality. Several schemes have been proposed to overcome this issue, but they fail to combine load balancing, low message overhead, and low latency in search operations. In this article we present DRing, an efficient layered solution that directly supports range queries over a ring-like DHT structure. We improve load balancing by using only the nodes that store data, and by updating neighbour information through an optimistic approach. DRing produces low overhead and low latency in environments where queries significantly outnumber data insertion operations. We analyze DRing through simulation and show that our solution does not rely on data distribution.

CORPS: Building a Community of Reputable PeerS in Distributed Hash Tables

Building trust is a major concern in Peer-to-Peer networks as several kinds of applications rely on the presence of trusted . Traditional techniques do not scale, produce very high overheads or rely on unrealistic assumptions. In this paper, we propose a new membership algorithm (Community Of Reputable PeerS, CORPS) for Distributed Hash Tables which builds a community of reputable nodes and thus enables the implementation of pseudo-trusted services. CORPS uses a reputation-based approach to decide whether a node can be a member of the group or not. We demonstrate the benefits of this approach and evaluate how much it improves the reliability of a trusted routing service.

WTR: a reputation metric for distributed hash tables based on a risk and credibility factor

The growing number of popular peer to peer applications during the last five years has implied for researchers to focus on how to build trust in such very large scale distributed systems. Reputation systems have shown to be a very good solution to build trust in presence of malicious nodes. We propose in this paper a new metric for reputation systems on top of a Distributed Hash Table that uses a notion of risk to make the applications aware of certain behaviours of malicious nodes. We show that our metric is able to significantly reduce the number of malicious transactions, and that it also provides very strong resistance to several traditional attacks of reputations systems. We also show that our solution can easily scale, and can be adapted to various Distributed Hash Tables.

A Scalable Architecture for Spatio-Temporal Range Queries over Big Location Data

Spatio-temporal range queries over Big Location Data aim to extract and analyze relevant data items generated around a given location and time. They require concurrent processing of massive and dynamic data flows. Current solutions for Big Location Data are ill-suited for continuous spatio-temporal processing because (i) most of them follow a batch processing model and (ii) they rely on spatial indexing structures maintained on a central master server. In this paper, we propose a scalable architecture for continuous spatio-temporal range queries built by coalescing multiple computing nodes on top of a Distributed Hash Table. The key component of our architecture is a distributed spatio-temporal indexing structure which exhibits low insertion and low index maintenance costs. We assess our solution with a public data set released by Yahoo! which comprises millions of geotagged multimedia files.

A critical analysis of latest advances in building trusted P2P networks using reputation systems

Building trusted P2P networks is a very difficult task, due to the size of the networks, the high volatility of the nodes, and potential byzantine behaviors. Interest in this area of research is ever increasing, because of the popularity of P2P networks, and their capability to provide a high availability of resources and services. In the last years, reputation systems have shown to be a good solution to build trust in large scale networks. Many papers have been published on this subject, and having a complete understanding on how they work is not easy. In this paper, we make a critical analysis of the most relevant results about reputation systems in the last three years, and propose a classification to clearly discriminate their common advantages and specific problems.

A Tabu Based Cache to Improve Latency and Load Balancing on Prefix Trees

Distributed Hash Tables (DHTs) provide the substrate to build large scale distributed applications over Peer-to-Peer networks. A major limitation of DHTs is that they only support exact-match queries. In order to offer range queries over a DHT it is necessary to build additional indexing structures. Prefix-based indexes, such as Prefix Hash Tree (PHT), are interesting approaches for building distributed indexes on top of DHTs. Nevertheless, the lookup operation of these indexes usually generates a high amount of unnecessary traffic overhead which degrades system performance by increasing response time. In this paper, we propose a novel distributed cache system called Tabu Prefix Table Cache (TPT-C), aiming at improving the performance of the Prefix-trees. We have implemented our solution over PHT, and the results confirm that our searching approach reduces up to a 70% the search latency and traffic overhead.

EA-MP: An evolutionary algorithm for a mine planning problem

In this paper we introduce an evolutionary algorithm for solving a copper mine planning problem. In the last 10 years this real-world problem has been tackled using linear integer programming and constraint programming. However, because it is a large scale problem, the model must be simplified by relaxing many constraints in order to obtain a near-optimal solution in a reasonable time. We now present an algorithm which takes into account most of the problem constraints and it is able to find better feasible solutions than the approach that has been used until now.

Recursive replication: a survival solution for structured P2P information systems to denial of service attacks

Structured Peer to Peer overlays have shown to be a very good solution for building very large scale distributed information systems. Most of them are based on Distributed Hash Tables (DHTs) that provide an easy way to manage replicas, thus facilitating high availability of data as well as fault tolerance. However, DHTs can also be affected by some well known Distributed Denial of Services attacks that can lead to almost complete unavailability of the stored objects. Very few powerful solutions exist for this kind of security weakness, and increasing the number of replicas for a given object seems to be the best known one. In this paper, we show how a recursive replicating schema can provide a good solution for this kind of attack.