Workflow application

Abstract: A system and method for managing the storage of files within an HSM system incorporate an architecture and methodology that facilitate the storage and retrieval of large image files as part of an overall image processing workflow. In particular, the system and method may find ready application in a workflow that involves the processing of groups of images associated with particular customers, projects, or transactions, and may act as a storage server for a client application that implements the workflow. The system and method may be useful, for example, in handling the storage of images uploaded from scanned photographic film, or digital images submitted to a photo-processing shop by amateur or professional photographers. In this case, the client application can be a photo-processing application that could provide for various media formats, sizes, and quantities of image reproductions for a consumer. As another example, the system and method may be useful in handling the storage of medical diagnostic images associated with a particular medical patient or study. In this case, the client application could be a picture archival communication system (PACS) that manages the archival of imagery for viewing by physicians. Further, the system and method may be useful in handling the storage of images associated with particular printing jobs, e.g., for publishers, advertising customers, and the like. In this case, the client application could be a digital prepress workflow application.

Abstract: Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to the resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GA-PSO algorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA, WSGA, and MTCT algorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over the available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster and with higher quality compared to other algorithms.

Abstract: Clouds are rapidly becoming an important platform for scientific applications In this paper we describe our experiences running a scientific workflow application in the cloud The application was developed to process astronomy data released by the Kepler project, a NASA mission to search for Earth-like planets orbiting other stars This workflow was deployed across multiple clouds using the Pegasus Workflow Management System The clouds used include several sites within the FutureGrid, NERSC's Magellan cloud, and Amazon EC2 We describe how the application was deployed, evaluate its performance executing in different clouds (based on Nimbus, Eucalyptus, and EC2), and discuss the challenges of deploying and executing workflows in a cloud environment We also demonstrate how Pegasus was able to support sky computing by executing a single workflow across multiple cloud infrastructures simultaneously

Abstract: This invention is about engineering approach to development of transactional workflow applications and about ability of this way produced applications to concurrently process large number of workflow requests of identical type with high speed. It provides methods and articles of manufacture: for graphical development of fully executable workflow application; for producing configuration of class objects and threads with capacity for concurrent processing of multitude of requests of identical type for transactional workflow and for concurrent execution and synchronization of parallel workflow-activity sequences within processing of a workflow request; for application self-scaling up and self-scaling down of its processing capacity; and for real-time visualization of application's thread structures, thread quantity, thread usage, and scaling-enacted changes in threads structure and quantity.