Luiz Angelo Steffenel

Service Discovery Mechanism for an Intentional Pervasive Information System

Pervasive Information System (PIS) provides a new vision of Information System available anytime and anywhere. The users of these systems must evolve in a space of services, in which several services are offered to him. However, PIS should enhance the transparency and efficiency of the system. We believe that a user-centric vision is needed to ensure a transparent access to the frequently changing space of services regardless of how to perform it. In this paper, we propose a new approach of PIS, both context-aware and intentional called IPIS. In this approach, services are proposed in order to satisfy users intention in a given context. Then, we propose a context-aware intentional service discovery mechanism. Such mechanism is based on an extension of OWL-S taking into account the notion of context and intention. We present in this paper IPIS platform. Then, we detail the proposed service discovery mechanism and present experimental results that demonstrate the advantage of using our proposition.

Grid of Security: A New Approach of the Network Security

Network security is in a daily evolving domain. Every day, new attacks, virus or intrusion techniques are released. Hence, network devices, enterprise servers or personal computers are potential targets of these attacks. Current security solutions like firewalls, intrusion detection systems (IDS) and virtual private networks (VPN) are centralized solutions which rely mostly on the analyze of inbound network connections. This approach notably forgets the effects of a rogue station, whose communications cannot be easily controlled unless the administrators establish a global authentication policy using methods like 802.1x to control all network communications among each device. To the best of our knowledge, a distributed and easily manageable solution for the global security of an enterprise network does not exist. In this paper, we present a new approach to deploy a distributed security solution where communication between each device can be control in a collaborative manner. Indeed, each device has its own security rules, who can be shared and improved through exchanges with others devices. With this new approach, called grid of security, a community of devices ensures that a device is trustworthy and that communications between devices progress in respect of the control of the system policies. To support this approach, we present a new communication model that helps structuring the distribution of security services among the devices. Like this, we can secure both ad-hoc, local-area or enterprise networks in a decentralized manner, preventing the risk of a security breach in the case of a failure.

MAPREDUCE CHALLENGES ON PERVASIVE GRIDS

This study presents the advances on designing and implementing scalable techniques to support the development and execution of MapReduce application in pervasive distributed computing infrastructures, in the context of the PER-MARE project. A pervasive framework for MapReduce applications is very useful in practice, especially in those scientific, enterprises and educational centers which have many unused or underused computing resources, which can be fully exploited to solve relevant problems that demand large computing power, such as scientific computing applications, big data processing, etc. In this study, we pro-pose the study of multiple techniques to support volatility and heterogeneity on MapReduce, by applying two complementary approaches: Improving the Apache Hadoop middleware by including context-awareness and fault-tolerance features; and providing an alternative pervasive grid implementation, fully adapted to dynamic environments. The main design and implementation decisions for both alternatives are described and validated through experiments, demonstrating that our approaches provide high reliability when executing on pervasive environments. The analysis of the experiments also leads to several insights on the requirements and constraints from dynamic and volatile systems, reinforcing the importance of context-aware information and advanced fault-tolerance features to provide efficient and reliable MapReduce services on pervasive grids.

A framework for adaptive collective communications for heterogeneous hierarchical computing systems

Collective communication operations are widely used in MPI applications and play an important role in their performance. However, the network heterogeneity inherent to grid environments represent a great challenge to develop efficient high performance computing applications. In this work we propose a generic framework based on communication models and adaptive techniques for dealing with collective communication patterns on grid platforms. Toward this goal, we address the hierarchical organization of the grid, selecting the most efficient communication algorithms at each network level. Our framework is also adaptive to grid load dynamics since it considers transient network characteristics for dividing the nodes into clusters. Our experiments with the broadcast operation on a real-grid setup indicate that an adaptive framework allows significant performance improvements on MPI collective communications.

PER-MARE: Adaptive Deployment of MapReduce over Pervasive Grids

Map Reduce is a parallel programming paradigm successfully used to perform computations on massive amounts of data, being widely deployed on clusters, grid, and cloud infrastructures. Interestingly, while the emergence of cloud infrastructures has opened new perspectives, several enterprises hesitate to put sensible data on the cloud and prefer to rely on internal resources. In this paper we introduce the PER-MARE initiative, which aims at proposing scalable techniques to support existent Map Reduce data-intensive applications in the context of loosely coupled networks such as pervasive and desktop grids. By relying on the Map Reduce programming model, PER-MARE proposes to explore the potential advantages of using free unused resources available at enterprises as pervasive grids, alone or in a hybrid environment. This paper presents the main lines that orient the PER-MARE approach and some preliminary results.

Performance Improvement of Data Mining in Weka through GPU Acceleration

Data mining tools may be computationally demanding, so there is an increasing interest on parallel computing strategies to improve their performance. The popularization of Graphics Processing Units (GPUs) increased the computing power of current desktop computers, but desktop-based data mining tools do not usually take full advantage of these architectures. This paper exploits an approach to improve the performance of Weka, a popular data mining tool, through parallelization on GPU-accelerated machines. From the profiling of Weka object-oriented code, we chose to parallelize a matrix multiplication method using state-of-the-art tools. The implementation was merged into Weka so that we could analyze the impact of parallel execution on its performance. The results show a significant speedup on the target parallel architectures, compared to the original, sequential Weka code.

CONFIIT: a middleware for peer-to-peer computing

CONFIIT (Computation Over Network with Finite number of Independent and Irregular Tasks) is a purely decentralized peer-to-peer middleware for grid computing. This paper presents CONFIIT main features and how it deals with topology changes and communication faults. To illustrate CONFIIT operation, we demonstrate how the car-sequencing problem can be solved in a distributed environment.

Assessing contention effects on MPI_alltoall communications

One of the most important collective communication patterns used in scientific applications is the complete exchange, also called All-to-All. Although efficient algorithms have been studied for specific networks, general solutions like those available in well-known MPI distributions (e.g. the MPI_Alltoall operation) are strongly influenced by the congestion of network resources. In this paper we present an integrated approach to model the performance of the All-to-All collective operation, which consists in identifying a contention signature that characterizes a given network environment, using it to augment a contention-free communication model. This approach, assessed by experimental results, allows an accurate prediction of the performance of the All-to-All operation over different network architectures with a small overhead.

Fast and efficient total exchange on two clusters

Total Exchange is one of the most important collective communication patterns for scientific applications. In this paper we propose an algorithm called LG for the total exchange redistribution problem between two clusters. In our approach we perform communications in two different phases, aiming to minimize the number of communication steps through the wide-area network. Therefore, we are able to reduce the number of messages exchanged through the backbone to only 2×max(n1, n2) against 2×n1×n2 messages with the traditional strategy (where n1 and n2 are the number of nodes of each clusters). Experimental results show that we reach over than 50% of performance improvement comparing to the traditional strategies.

Improving the performance of Apache Hadoop on pervasive environments through context-aware scheduling

This article proposes to improve Apache Hadoop scheduling through a context-aware approach. Apache Hadoop is the most popular implementation of the MapReduce paradigm for distributed computing, but its design does not adapt automatically to computing nodes’ context and capabilities. By introducing context-awareness into Hadoop, we intent to dynamically adapt its scheduling to the execution environment. This is a necessary feature in the context of pervasive grids, which are heterogeneous, dynamic and shared environments. The solution has been incorporated into Hadoop and assessed through controlled experiments. The experiments demonstrate that context-awareness provides comparative performance gains, especially when some of the resources disappear during execution.