Domenico Talia

Models and languages for parallel computation

We survey parallel programming models and languages using six criteria to assess their suitability for realistic portable parallel programming. We argue that an ideal model should by easy to program, should have a software development methodology, should be architecture-independent, should be easy to understand, should guarantee performance, and should provide accurate information about the cost of programs. These criteria reflect our belief that developments in parallelism must be driven by a parallel software industry based on portability and efficiency. We consider programming models in six categories, depending on the level of abstraction they provide. Those that are very abstract conceal even the presence of parallelism at the software level. Such models make software easy to build and port, but efficient and predictable performance is usually hard to achieve. At the other end of the spectrum, low-level models make all of the messy issues of parallel programming explicit (how many threads, how to place them, how to express communication, and how to schedule communication), so that software is hard to build and not very portable, but is usually efficient. Most recent models are near the center of this spectrum, exploring the best tradeoffs between expressiveness and performance. A few models have achieved both abstractness and efficiency. Both kinds of models raise the possibility of parallelism as part of the mainstream of computing.

The knowledge grid

Designing, building, and implementing an architecture for distributed knowledge discovery.

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.

A super-peer model for resource discovery services in large-scale grids

As deployed Grids increase from tens to thousands of nodes, peer-to-peer (P2P) techniques and protocols can be used to implement scalable services and applications. The super-peer model is a novel approach that helps the convergence of P2P models and Grid environments and can be used to deploy a P2P information service in Grids. A super-peer serves a single physical organization in a Grid, and manages metadata associated to the resources provided by the nodes of that organization. Super-peers connect to each other to form a peer network at a higher level. This paper examines how the super-peer model can handle membership management and resource discovery services in a multi-organizational Grid. A simulation analysis evaluates the performance of a resource discovery protocol; simulation results can be used to tune protocol parameters in order to increase search efficiency.

Clouds for Scalable Big Data Analytics

Extracting useful knowledge from huge digital datasets requires smart and scalable analytics services, programming tools, and applications.

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.

The Open Grid Services Architecture: where the grid meets the Web

Recently, the Globus Project and IBM initiated a development effort to align Grid technologies with Web services technologies, using the Open Grid Services Architecture. OGSA enables the integration of services and resources across distributed, heterogeneous, dynamic environments and communities. To achieve this integration, the OGSA model adopts the Web Services Description Language (WSDL) and defines the Grid service concept.