Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational grid. In this chapter, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must correspond to social structures. Throughout, we reflect on the lessons of experience and chart the course traveled by research ideas as they grow into production systems.

Since 1984, the Condor project has helped ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational grid. In this chapter, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must reflect the sociology of communities. Throughout, we reflect on the lessons of experience and chart the course travelled by research ideas as they grow into production systems.

processing on computational grids

Software Component Frameworks are well known in the commercial business application world and now this technology is being explored with great interest as a way to build large-scale scientific application on parallel computers. In the case of Grid systems, the current architectural model is based on the emerging web services framework. In this paper we describe progress that has been made on the Common Component Architecture model (CCA) and discuss its success and limitations when applied to problems in Grid computing. Our primary conclusion is that a component model fits very well with a services-oriented Grid, but the model of composition must allow for a very dynamic (both in space and it time) control of composition. We note that this adds a new dimension to conventional service workflow and it extends the “Inversion of Control ” aspects of must component systems. 1.

In this paper we propose a framework dedicated to the development and the execution of parallel applications over large scale global computing platforms. A workflow programming environment will be introduced, based on a new workflow language YvetteML and a Human-GRID middleware interface called YML. This language allows description of different kind of components to be allocated to GRID resources. Depending of the different targeted resources, the components may be associated to computation, migration of data or other resource controls. YML is designed to have several back-ends for different middleware, as a welldesigned front end is developed independently of any dedicated middleware. In order to make the framework immediately useful, YML comes with pre-configured interfaces to some numerical routines and a numerical library for iterative linear algebra methods. We will present experimentations done on some large scale platforms using a peer to peer middleware with a numerical application case study. 1.

In this paper, we present a scalable authorization service, based on the concept of fine-grained access control (FGAC), for large-scale Grid infrastructures that span multiple independent domains. FGAC enables participating resource owners to specify fine-grained policies concerning which user can access can their resources under which mode. We argue that such an authorization service must be integrated with the resource broker service to avoid scheduling requests onto resources which do not authorize the user request. For this reason, we develop a novel resource broker service that integrates access control with resource scheduling. In our system, both resource owners and users define their resource access and usage policies. The resource broker schedules a user request only within the set of resources whose policies match the user credentials (and vice-versa). Since this process of evaluating authorization policies of resources and user, in addition to checking the resource requirement, can be a potential bottleneck for a large scale Grid, we also analyze the problem of efficient evaluation of FGAC policies. In this context, we present a novel method for policy organization and compare its performance with other strategies. Preliminary results show that the proposed method can significantly enhance performance. 1

Dynasoar is an infrastructure for dynamically deploying Web Services over a Grid or the Internet. It enables an approach to Grid computing in which distributed applications are built around services instead of jobs. Dynasoar automatically deploys a service on an available host if no existing deployments exist, or if performance requirements cannot be met by existing deployments. This is analogous to remote job scheduling, but offers the opportunity for improved performance as the cost of moving and deploying the service can be shared across the processing of many messages. A key feature of the architecture is that it makes a clear separation between Web Service Providers, who offer services to consumers, and Host Providers, who offer computational resources on which services can be deployed, and messages sent to them processed. Separating these two components and defining their interactions, opens up the opportunity for interesting new organisational / business models. 1.

Abstract. A Task Graph (TG) is a model of a parallel program that consists of many subtasks that can be executed simultaneously on different processing elements. Subtasks exchange data via an interconnection network. The dependencies between subtasks are described by means of a Directed Acyclic Graph. Unfortunately, due to their characteristics, scheduling a TG requires dedicated or uninterruptible resources. Moreover, scheduling a TG by itself results in a low resource utilization because of the dependencies among the subtasks. Therefore, in order to solve the above problems, we propose a scheduling approach for TGs by using advance reservation in a cluster environment. In addition, to improve resource utilization, we also propose a scheduling solution by interweaving one or more TGs within the same reservation block and/or backfilling with independent jobs. 1

Abstract. This paper focuses on two areas that experience in building databaseoriented e-science applications has shown to be important. Firstly, methods of promoting data locality are vital due to the high cost of moving data in servicebased distributed systems. Databases provide an excellent basis for achieving this due to their potential for moving computation to data. The paper also describes a new infrastructure that further promotes locality by enabling servicebased computations to migrate to data. Secondly, the ability to combine information from a set of distributed databases has proved invaluable in many applications. The paper describes the design of an adaptive distributed query processing system that is able to exploit facilities offered by an underlying grid infrastructure. In addressing these two areas, the paper gives an overview of some of the generic components that have been designed to simplify the integration of databases into e-science applications. 1

Abstract — Compute-intensive scientific applications are heavily reliant on the available quantity of computing resources. The Grid paradigm provides a large scale computing environment for scientific users. However, conventional Grid job submission tools do not provide a high-level job scheduling environment for these users across multiple institutions. For extremely large number of jobs, a more scalable job scheduling framework that can leverage highly distributed clusters and supercomputers is required. In this paper, we propose a high-level job scheduling Web service framework, Swarm. Swarm is developed for scientific applications that must submit massive number of high-throughput jobs or workflows to highly distributed computing clusters. The Swarm service itself is designed to be