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On the use of big data frameworks for big service composition
Journal of Network and Computer Applications ( IF 8.7 ) Pub Date : 2020-06-04 , DOI: 10.1016/j.jnca.2020.102732
Mokhtar Sellami , Haithem Mezni , Mohand Said Hacid

Over the last years, big data has emerged as a new paradigm for the processing and analysis of massive volumes of data. Big data processing has been combined with service and cloud computing, leading to a new class of services called “Big Services”. In this new model, services can be seen as an abstract layer that hides the complexity of the processed big data. To meet users' complex and heterogeneous needs in the era of big data, service reuse is a natural and efficient means that helps orchestrating available services' operations, to provide customer on-demand big services. However different from traditional Web service composition, composing big services refers to the reuse of, not only existing high-quality services, but also high-quality data sources, while taking into account their security constraints (e.g., data provenance, threat level and data leakage). Moreover, composing heterogeneous and large-scale data-centric services faces several challenges, apart from security risks, such as the big services' high execution time and the incompatibility between providers' policies across multiple domains and clouds. Aiming to solve the above issues, we propose a scalable approach for big service composition, which considers not only the quality of reused services (QoS), but also the quality of their consumed data sources (QoD). Since the correct representation of big services requirements is the first step towards an effective composition, we first propose a quality model for big services and we quantify the data breaches using L-Severity metrics. Then to facilitate processing and mining big services' related information during composition, we exploit the strong mathematical foundation of fuzzy Relational Concept Analysis (fuzzy RCA) to build the big services' repository as a lattice family. We also used fuzzy RCA to cluster services and data sources based on various criteria, including their quality levels, their domains, and the relationships between them. Finally, we define algorithms that parse the lattice family to select and compose high-quality and secure big services in a parallel fashion. The proposed method, which is implemented on top of Spark big data framework, is compared with two existing approaches, and experimental studies proved the effectiveness of our big service composition approach in terms of QoD-aware composition, scalability, and security breaches.



中文翻译:

关于使用大数据框架进行大服务组合

在过去的几年中,大数据已经成为处理和分析大量数据的新范例。大数据处理已与服务和云计算结合在一起,从而产生了一种称为“大服务”的新型服务。在这种新模型中,服务可以看作是抽象层,它隐藏了处理后的大数据的复杂性。为了满足大数据时代用户的复杂和异构需求,服务重用是一种自然而有效的手段,可以帮助协调可用服务的运营,以提供客户按需的大服务。但是,与传统的Web服务组合不同,组成大型服务是指不仅考虑现有的高质量服务,而且还考虑高质量数据源的安全性约束(例如,数据来源,威胁级别和数据泄漏)。此外,组成异构的和大规模的以数据为中心的服务除了安全风险外,还面临一些挑战,例如大服务的执行时间长,跨多个域和云的提供商的策略之间不兼容。为了解决上述问题,我们提出了一种用于大型服务组合的可伸缩方法,该方法不仅考虑重用服务的质量(QoS),而且还考虑其使用的数据源的质量(QoD)。由于正确表示大服务需求是迈向有效组合的第一步,因此我们首先为大服务提出质量模型,然后使用L-Severity指标对数据泄露进行量化。然后,为了便于在组合过程中处理和挖掘大服务的相关信息,我们利用模糊关系概念分析(模糊RCA)的强大数学基础将大型服务的存储库构建为晶格族。我们还使用模糊RCA根据各种标准对服务和数据源进行聚类,这些标准包括它们的质量级别,它们的域以及它们之间的关系。最后,我们定义了解析晶格族的算法,以并行方式选择和组合高质量和安全的大服务。将该方法在Spark大数据框架之上实现,并与两种现有方法进行了比较,实验研究证明了我们的大服务组合方法在QoD感知组合,可伸缩性和安全漏洞方面的有效性。存储库作为晶格族。我们还使用模糊RCA根据各种标准对服务和数据源进行聚类,这些标准包括它们的质量级别,它们的域以及它们之间的关系。最后,我们定义了解析晶格族的算法,以并行方式选择和组合高质量和安全的大服务。该提议的方法在Spark大数据框架之上实现,并与两种现有方法进行了比较,实验研究证明了我们的大服务组合方法在QoD感知组合,可伸缩性和安全漏洞方面的有效性。存储库作为晶格族。我们还使用模糊RCA根据各种标准对服务和数据源进行聚类,这些标准包括它们的质量级别,它们的域以及它们之间的关系。最后,我们定义了解析晶格族的算法,以并行方式选择和组合高质量和安全的大服务。将该方法在Spark大数据框架之上实现,并与两种现有方法进行了比较,实验研究证明了我们的大服务组合方法在QoD感知组合,可伸缩性和安全漏洞方面的有效性。我们定义了解析晶格族的算法,以并行方式选择和组合高质量和安全的大型服务。该提议的方法在Spark大数据框架之上实现,并与两种现有方法进行了比较,实验研究证明了我们的大服务组合方法在QoD感知组合,可伸缩性和安全漏洞方面的有效性。我们定义了解析晶格族的算法,以并行方式选择和组合高质量和安全的大型服务。将该方法在Spark大数据框架之上实现,并与两种现有方法进行了比较,实验研究证明了我们的大服务组合方法在QoD感知组合,可伸缩性和安全漏洞方面的有效性。

更新日期:2020-06-04
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