Applications in cyber-physical systems are increasingly coupled with online instruments to perform long running, continuous data processing. Such "always on" dataflow applications are dynamic, where they need to change the applications logic and performance at runtime, in response to external operational needs. Floe is a continuous dataflow framework that is designed to be adaptive for dynamic applications on Cloud infrastructure. It offers advanced dataflow patterns like BSP and MapReduce for flexible and holistic composition of streams and files, and supports dynamic recomposition at runtime with minimal impact on the execution. Adaptive resource allocation strategies allow our framework to effectively use elastic Cloud resources to meet varying data rates. We illustrate the design patterns of Floe by running an integration pipeline and a tweet clustering application from the Smart Power Grids domain on a private Eucalyptus Cloud. The responsiveness of our resource adaptation is validated through simulations for periodic, bursty and random workloads.
Application dynamism have be explored using concepts such as frames, templates and dynamic embedding @cite_15 . However, these are in the context of scientific workflows where the choice for the dynamic task is finalized before the execution of that task begins which is in contrast to the requirement in the continuous dataflows where an executing task needs to be seamlessly updated at runtime as is support by .
@cite_15
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Developers of rapidly growing applications must be able to anticipate potential scalability problems before they cause performance issues in production environments. A new type of data independence, called scale independence, seeks to address this challenge by guaranteeing a bounded amount of work is required to execute all queries in an application, independent of the size of the underlying data. While optimization strategies have been developed to provide these guarantees for the class of queries that are scale-independent when executed using simple indexes, there are important queries for which such techniques are insufficient. Executing these more complex queries scale-independently requires precomputation using incrementally-maintained materialized views. However, since this precomputation effectively shifts some of the query processing burden from execution time to insertion time, a scale-independent system must be careful to ensure that storage and maintenance costs do not threaten scalability. In this paper, we describe a scale-independent view selection and maintenance system, which uses novel static analysis techniques that ensure that created views do not themselves become scaling bottlenecks. Finally, we present an empirical analysis that includes all the queries from the TPC-W benchmark and validates our implementation's ability to maintain nearly constant high-quantile query and update latency even as an application scales to hundreds of machines. This paper presents Dynamically Pooled Complementary Features (DPCF), a unified approach to dynamic scene recognition that analyzes a short video clip in terms of its spatial, temporal and color properties. The complementarity of these properties is preserved through all main steps of processing, including primitive feature extraction, coding and pooling. In the feature extraction step, spatial orientations capture static appearance, spatiotemporal oriented energies capture image dynamics and color statistics capture chromatic information. Subsequently, primitive features are encoded into a mid-level representation that has been learned for the task of dynamic scene recognition. Finally, a novel dynamic spacetime pyramid is introduced. This dynamic pooling approach can handle both global as well as local motion by adapting to the temporal structure, as guided by pooling energies. The resulting system provides online recognition of dynamic scenes that is thoroughly evaluated on the two current benchmark datasets and yields best results to date on both datasets. In-depth analysis reveals the benefits of explicitly modeling feature complementarity in combination with the dynamic spacetime pyramid, indicating that this unified approach should be well-suited to many areas of video analysis. Scientific workflows are commonplace in eScience applications. Yet, the lack of integrated support for data models, including streaming data, structured collections and files, is limiting the ability of workflows to support emerging applications in energy informatics that are stream oriented. This is compounded by the absence of Cloud data services that support reliable and performant streams. In this paper, we propose and present a scientific workflow framework that supports streams as first-class data, and is optimized for performant and reliable execution across desktop and Cloud platforms. The workflow framework features and its empirical evaluation on a private Eucalyptus cloud are presented. Abstract In many domains of science, scientific applications are represented by workflows. In this paper, we introduce a resource management strategy to maximize the success rate of concurrent workflow applications constrained by individual deadline and budget values. The Multi-Workflow Deadline-Budget Scheduling (MW-DBS) algorithm can schedule multiple workflows that can arrive in the system at any time, with the aim of satisfying individual job requirements. MW-DBS produces schedules without performing optimizations but guarantees that the deadline and budget defined for each job are not exceeded. Experimental results show that our strategy increases the scheduling success rate of finding valid solutions.