This article introduces an architecture for a document-partitioned search engine, based on a novel approach combining collection selection and load balancing, called load-driven routing . By exploiting the query-vector document model, and the incremental caching technique, our architecture can compute very high quality results for any query, with only a fraction of the computational load used in a typical document-partitioned architecture. By trading off a small fraction of the results, our technique allows us to strongly reduce the computing pressure to a search engine back-end; we are able to retrieve more than 2/3 of the top-5 results for a given query with only 10% the computing load needed by a configuration where the query is processed by each index partition. Alternatively, we can slightly increase the load up to 25% to improve precision and get more than 80% of the top-5 results. In fact, the flexibility of our system allows a wide range of different configurations, so as to easily respond to different needs in result quality or restrictions in computing power. More important, the system configuration can be adjusted dynamically in order to fit unexpected query peaks or unpredictable failures. This article wraps up some recent works by the authors, showing the results obtained by tests conducted on 6 million documents, 2,800,000 queries and real query cost timing as measured on an actual index.

In this paper, we propose a new method, called Convergent Scheduling, for scheduling a continuous stream of batch jobs on the machines of large-scale computing farms. This method exploits a set of heuristics that guide the scheduler in making decisions. Each heuristics manages a specific problem constraint, and contributes to carry out a value that measures the degree of matching between a job and a machine. Scheduling choices are taken to meet the QoS requested by the submitted jobs, and optimizing the usage of hardware and software resources. We compared it with some of the most common job scheduling algorithms, i.e. Backfilling, and Earliest Deadline First. Convergent Scheduling is able to compute good assignments, while being a simple and modular algorithm.

No abstract available.

Abstract The Grid and its related technologies enable large‐scale sharing of resources of various types. We envision that in the near future applications will be completely built in a bottom‐up fashion using software components deployed on various locations and interconnected to form a workflow graph. In this paper, we make some proposals on the design of a component search service, enabling users to locate the components they need to deploy an application. Copyright © 2005 John Wiley & Sons, Ltd.

Component-oriented software design of Grid applications is commanding growing attention for business and scientific problems. The goal is create applications by assembling together independently developed software components. The components are independently developed, composable, reusable, substitutable software solutions, with clearly defined interface and behaviour.

Today, the development of Grid applications is considered a nightmare, due to lack of grid programming environments, standards, off-the-shelf software components, and so on.

No abstract available.

Convergent scheduling is a general framework for cluster assignment and instruction scheduling on spatial architectures. A convergent scheduler is composed of independent passes, each implementing a heuristic that addresses a particular problem or constraint. The passes share a simple, common interface that provides spatial and temporal preference for each instruction. Preferences are not absolute; instead, the interface allows a pass to express the confidence of its preferences, as well as preferences for multiple space and time slots. A pass operates by modifying these preferences. By applying a series of passes that address all the relevant constraints, the convergent scheduler can produce a schedule that satisfies all the important constraints. Because all passes are independent and need to understand only one interface to interact with each other, convergent scheduling simplifies the problem of handling multiple constraints and co-developing different heuristics. We have applied convergent scheduling to two spatial architectures: the Raw processor and a clustered VLIW machine. It is able to successfully handle traditional constraints such as parallelism, load balancing, and communication minimization, as well as constraints due to preplaced instructions, which are instructions with predetermined cluster assignment. Convergent scheduling is able to obtain an average performance improvement of 21% over the existing space-time scheduler of the Raw processor, and an improvement of 14% over state-of-the-art assignment and scheduling techniques on a clustered VLIW architecture.