F e B R uA RY 2 0 1 2 | Vo L. 5 5 | N o. 2 | c oM M u n i c aT i o n s o f T he ac M 81in performing them for terabyte or larger datasets (increasingly common across scientific disciplines) are quite different from those that applied when data volumes were measured in kilobytes. The result is a computational crisis in many laboratories and a growing need for far more powerful data-management tools, yet the typical researcher lacks the resources and expertise to operate these tools.The answer may be to deliver research data-management capabilities to users as hosted "software as a service," or SaaS, 18 a software-delivery model in which software is hosted centrally and accessed by users using a thin client (such as a Web browser) over the Internet. As demonstrated in many business and consumer tools, SaaS leverages intuitive Web 2.0 in-a S B i g D ata emerges as a force in science, 2,3 so, too, do new, onerous tasks for researchers. Data from specialized instrumentation, numerical simulations, and downstream manipulations must be collected, indexed, archived, shared, replicated, and analyzed. These tasks are not new, but the complexities involved software as a service for Data scientists The costs of research data life-cycle management are growing dramatically as data becomes larger and more complex.saas approaches are a promising solution, outsourcing time-consuming research data management tasks to third-party services.Globus online demonstrates the potential of saas for research data management, simplifying data movement for researchers and research facilities alike.

Abstract-In this paper, we describe the design and implementation of two mechanisms for fault-tolerance and recovery for complex scientific workflows on computational grids. We present our algorithms for over-provisioning and migration, which are our primary strategies for fault-tolerance. We consider application performance models, resource reliability models, network latency and bandwidth and queue wait times for batch-queues on compute resources for determining the correct fault-tolerance strategy. Our goal is to balance reliability and performance in the presence of soft, real-time constraints like deadlines and expected success probabilities, and to do it in a way that is transparent to scientists. We have evaluated our strategies by developing a Fault-Tolerance and Recovery (FTR) service and deploying it as a part of the Linked Environments for Atmospheric Discovery (LEAD) production infrastructure. Results from real usage scenarios in LEAD show that the failure rate of individual steps in workflows decreases from about 30% to 5% by using our fault-tolerance strategies.

Web service architectures have gained popularity in recent years within the scientific grid research community. One reason for this is that web services allow software and services from various organizations to be combined easily to provide integrated and distributed applications. However, most applications developed and used by scientific communities are not web-service-oriented, and there is a growing need to integrate them into grid applications based on service-oriented architectures. In this paper, we describe a framework that allows scientists to provide a web service interface to their existing applications as web services without having to write extra code or modify their applications in any way. In addition, application providers do not need to be experts in web services standards, such as Web Services Description Language, Web Services Addressing, Web Services Security, or secure authorization, because the framework automatically generates these details. The framework also enables users to discover these application services, interact with them, and compose scientific workflows from the convenience of a grid portal.

We review the efforts of the Open Grid Computing Environments collaboration. By adopting a generalthree-tiered architecture based on common standards for portlets and Grid Web Services, we can deliver numerous capabilities to science gateways from our diverse constituent efforts. In this paper, we discuss our support for standards-based Grid portlets using the Velocity development environment. Our Grid portlets are based on abstraction layers provided by the Java CoG kit, which hide the differences of different Grid toolkits. Sophisticated services are decoupled from the portal container using Web service strategies. We describe advance information, semantic data, collaboration, and science application services developed by our consortium.

Abstract.Grid computing is about allocating distributed collections of resources including computers, storage systems, networks and instruments to form a coherent system devoted to a "virtual organization" of users who share a common interest in solving a complex problem or building an efficient agile enterprise. Service oriented architectures have emerged as the standard way to build Grids. This paper provides a brief look at the Open Grid Service Architecture, a standard being proposed by the Global Grid Forum, which provides the foundational concepts of most Grid systems. Above this Grid foundation is a layer of applicationoriented services that are managed by workflow tools and "science gateway" portals that provide users transparent access to the applications that use the resources of a Grid. In this paper we will also describe these Gateway framework services and discuss how they relate to and use Grid services.