Paolo Trunfio

Toward a synergy between P2P and grids

In spite of current practices and thoughts, the grid and P2P models share several features and have more in common than we perhaps generally recognize.A synergy between the two research communities...Peer-to-peer (P2P) networks and grids are distributed computing models that enable decentralized collaboration by integrating computers into networks in which each can consume and offer services. P2P is a class of self-organizing systems or applications that takes advantage of distributed resources storage, processing, information, and human presence available at the Internets edges. A grid is a geographically distributed computation platform comprising a set of heterogeneous machines that users can access through a single interface. Both are hot research topics because they offer promising paradigms for developing efficient distributed systems and applications. Unlike the classic client-server model, in which roles are well separated, P2P and grid networks can assign each node a client or server role according to the operations they are to perform on the network - even if some nodes act more as server than as client in current implementations. In spite of current practices and thoughts, the grid and P2P models share several features and have more in common than we perhaps generally recognize. It is time to consider how to integrate these two models. A synergy between the two research communities, and the two computing models, could start with identifying the similarities and differences between them.

Peer-to-Peer resource discovery in Grids: Models and systems

Resource location or discovery is a key issue for Grid systems in which applications are composed of hardware and software resources that need to be located. Classical approaches to Grid resource location are either centralized or hierarchical and will prove inefficient as the scale of Grid systems rapidly increases. On the other hand, the Peer-to-Peer (P2P) paradigm emerged as a successful model that achieves scalability in distributed systems. One possibility would be to borrow existing methods from the P2P paradigm and to adopt them to Grid systems taking into consideration the existing differences. Several such attempts have been made during the last couple of years. This paper aims to serve as a review of the most promising Grid systems that use P2P techniques to facilitate resource discovery in order to perform a qualitative comparison of the existing approaches and to draw conclusions about their advantages and weaknesses. Future research directions are also discussed.

Distributed data mining on grids: services, tools, and applications

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called KNOWLEDGE GRID. This paper describes the KNOWLEDGE GRID framework and presents the toolset provided by the KNOWLEDGE GRID for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the KNOWLEDGE GRID tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.

Distributed data mining on the grid

In many industrial, scientific and commercial applications, it is often necessary to analyze large data sets, maintained over geographically distributed sites, by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for knowledge discovery applications. We describe a software architecture for geographically distributed high-performance knowledge discovery applications called KNOWLEDGE GRID, which is designed on top of computational grid mechanisms, provided by grid environments such as Globus. The KNOWLEDGE GRID uses the basic grid services such as communication, authentication, information, and resource management to build more specific parallel and distributed knowledge discovery tools and services. The paper discusses how the KNOWLEDGE GRID can be used to implement distributed data mining services.

Weka4WS: a WSRF-enabled weka toolkit for distributed data mining on grids

This paper presents Weka4WS, a framework that extends the Weka toolkit for supporting distributed data mining on Grid environments. Weka4WS adopts the emerging Web Services Resource Framework (WSRF) for accessing remote data mining algorithms and managing distributed computations. The Weka4WS user interface is a modified Weka Explorer environment that supports the execution of both local and remote data mining tasks. On every computing node, a WSRF-compliant Web Service is used to expose all the data mining algorithms provided by the Weka library. The paper describes the design and the implementation of Weka4WS using a first release of the WSRF library. To evaluate the efficiency of the proposed system, a performance analysis of Weka4WS for executing distributed data mining tasks in different network scenarios is presented.

P2P-MapReduce: Parallel data processing in dynamic Cloud environments

MapReduce is a programming model for parallel data processing widely used in Cloud computing environments. Current MapReduce implementations are based on centralized master-slave architectures that do not cope well with dynamic Cloud infrastructures, like a Cloud of clouds, in which nodes may join and leave the network at high rates. We have designed an adaptive MapReduce framework, called P2P-MapReduce, which exploits a peer-to-peer model to manage node churn, master failures, and job recovery in a decentralized but effective way, so as to provide a more reliable MapReduce middleware that can be effectively exploited in dynamic Cloud infrastructures. This paper describes the P2P-MapReduce system providing a detailed description of its basic mechanisms, a prototype implementation, and an extensive performance evaluation in different network scenarios. The performance results confirm the good fault tolerance level provided by the P2P-MapReduce framework compared to a centralized implementation of MapReduce, as well as its limited impact in terms of network overhead.

Internet of Things Based on Smart Objects

The Internet of Things (IoT) usually refers to a world-wide network of interconnected heterogeneous objects (sensors, actuators, smart devices, smart objects, RFID, embedded computers, etc) uniquely addressable, based on standard communication protocols. Beyond such a definition, it is emerging a new definition of IoT seen as a loosely coupled, decentralized system of cooperating smart objects (SOs). A SO is an autonomous, physical digital object augmented with sensing/actuating, processing, storing, and networking capabilities. SOs are able to sense/actuate, store, and interpret information created within themselves and around the neighbouring external world where they are situated, act on their own, cooperate with each other, and exchange information with other kinds of electronic devices and human users. However, such SO-oriented IoT raises many in-the-small and in-the-large issues involving SO programming, IoT system architecture/middleware and methods/methodologies for the development of SO-based applications. This Book will specifically focus on exploring recent advances in architectures, algorithms, and applications for an Internet of Things based on Smart Objects. Topics appropriate for this Book include, but are not necessarily limited to: - Methods for SO development - IoT Networking - Middleware for SOs - Data Management for SOs - Service-oriented SOs - Agent-oriented SOs - Applications of SOs in Smart Environments: Smart Cities, Smart Health, Smart Buildings, etc. Advanced IoT Projects.

Distributed data mining services leveraging WSRF

The continuous increase of data volumes available from many sources raises new challenges for their effective understanding. Knowledge discovery in large data repositories involves processes and activities that are computationally intensive, collaborative, and distributed in nature. The Grid is a profitable infrastructure that can be effectively exploited for handling distributed data mining and knowledge discovery. To achieve this goal, advanced software tools and services are needed to support the development of KDD applications. The Knowledge Grid is a high-level framework providing Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications that integrate data sources and data mining tools provided as distributed services on the Grid. All of these services are currently being re-designed and re-implemented as WSRF-compliant Grid Services. This paper highlights design aspects and implementation choices involved in such a process.

Knowledge grid : High performance knowledge discovery services on the grid

Knowledge discovery tools and techniques are used in an increasing number of scientific and commercial areas for the analysis of large data sets. When large data repositories are coupled with geographic distribution of data, users and systems, it is necessary to combine different technologies for implementing high-performance distributed knowledge discovery systems. On the other hand, computational grid is emerging as a very promising infrastructure for high-performance distributed computing. In this paper we introduce a software architecture for parallel and distributed knowledge discovery (PDKD) systems that is built on top of computational grid services that provide dependable, consistent, and pervasive access to high-end computational resources. The proposed architecture uses the grid services ard defines a set of additional layers to implement the services of distributed knowledge discovery process on grid-connected sequential or parallel computers.

Grid services: principles, implementations and use

The Open Grid Services Architecture (OGSA) defines the Grid service concept using principles from both the Grid computing and Web Services models. OGSA lets developers integrate resources and services across distributed, heterogeneous, and dynamic environments. This paper explores OGSA and Grid service concepts by discussing both current implementations and recently proposed standards, such as the Web Services Resource Framework (WSRF). To show the benefits of the OGSA approach, we present the Grid Data Integration System (GDIS), an OGSA based architecture for data integration on Grids. Data integration on Grids is a challenging issue due to the large scale, dynamic, autonomous and distributed nature of data resources. GDIS exploits OGSA services to support federation, analysis, and processing of data from different distributed sources.