Using Cloud Technologies to Optimize Data-Intensive Service Applications

Dirk Habich, Wolfgang Lehner, Sebastian Richly, U. Assmann
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引用次数: 14

Abstract

The role of data analytics increases in several application domains to cope with the large amount of captured data. Generally, data analytics are data-intensive processes, whose efficient execution is a challenging task. Each process consists of a collection of related structured activities, where huge data sets have to be exchanged between several loosely coupled services. The implementation of such processes in a service-oriented environment offers some advantages, but the efficient realization of data flows is difficult. Therefore, we use this paper to propose a novel SOA-aware approach with a special focus on the data flow. The tight interaction of new cloud technologies with SOA technologies enables us to optimize the execution of data-intensive service applications by reducing the data exchange tasks to a minimum. Fundamentally, our core concept to optimize the data flows is found in data clouds. Moreover, we can exploit our approach to derive efficient process execution strategies regarding different optimization objectives for the data flows.
使用云技术优化数据密集型服务应用
为了处理大量捕获的数据,数据分析在几个应用程序领域中的作用越来越大。通常,数据分析是数据密集型过程,其高效执行是一项具有挑战性的任务。每个流程由一组相关的结构化活动组成,其中必须在几个松散耦合的服务之间交换大量数据集。在面向服务的环境中实现这类流程提供了一些优势,但是很难有效地实现数据流。因此,我们利用本文提出一种新颖的soa感知方法,特别关注数据流。新云技术与SOA技术的紧密交互使我们能够通过将数据交换任务减少到最低限度来优化数据密集型服务应用程序的执行。从根本上说,我们优化数据流的核心概念是在数据云中找到的。此外,我们还可以利用我们的方法,针对数据流的不同优化目标派生出有效的流程执行策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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