Enterprise IT performance can be improved by providing reactive and predictive monitoring tools that anticipate problem detection. It requires advanced approaches for creating more agile, adaptable, sustainable and intelligent information systems. Service-oriented architecture (SOA) has been used in significant performance-based approaches by information system practitioners. Organizations are interested in performance-based decision support along the layers of SOA to maintain their sustainability for service reuse. Reusability is a very important aspect of Service-based systems (SBS) to analyze service or process reuse. This helps in achieving business agility to meet changing marketplace needs. However currently, there are many challenges pertaining tothe complexities of service reuse evolution along SBS. These challenges are related to the sustainability of service behavior during its lifecycle and the limitations of existing monitoring tools. There is a need for a consolidated classified knowledge-based performance profile, analytical assessment, prediction and recommendation. Therefore, this paper provides a semantic performance-oriented decision support system (SPODSS) for SOA. SPODSS provides recommendations for suggesting service reuse during its evolution. SPODSS is supported by five building blocks. These blocks are data, semantic, traces, machine learning, and decision. SPODSS classify data, validate (analytical assessment, traces, semantic enrichment) at different time intervals and increased consumption and prediction based on consolidated results. It handles the dynamic evolution of SBS and new or changed user requirements by ontology development. Finally, SPODSS generates recommendations for atomic service, composite service, and resourceallocation provisioning. To motivate this approach, we illustrate the implementation of the proposed algorithms for all the five blocks by a business process use case and public data set repositories of shared services. Sustainability and adaptability of service-based systems areensured by handling new business requirements, dynamicity issues and ensuring performance. Performance criterion includes functional suitability, time behavior, resource utilization, and reliability in terms of availability, maturity, and risk.

中文翻译：

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面向服务架构中面向性能决策支持的机器学习方法

通过提供预测问题检测的反应性和预测性监控工具，可以提高企业 IT 性能。它需要先进的方法来创建更敏捷、适应性更强、可持续和智能的信息系统。面向服务的架构 (SOA) 已被信息系统从业者用于重要的基于性能的方法。组织对沿着 SOA 层的基于性能的决策支持感兴趣，以保持其服务重用的可持续性。可重用性是基于服务的系统 (SBS) 分析服务或流程重用的一个非常重要的方面。这有助于实现业务敏捷性，以满足不断变化的市场需求。然而，目前，随着 SBS 的服务重用演进的复杂性，存在许多挑战。这些挑战与服务行为在其生命周期内的可持续性以及现有监控工具的局限性有关。需要一个综合的基于知识的分类绩效概况、分析评估、预测和建议。因此，本文为 SOA 提供了一个面向语义性能的决策支持系统（SPODSS）。SPODSS 提供了在其演进过程中建议服务重用的建议。SPODSS 由五个构建块支持。这些块是数据、语义、跟踪、机器学习和决策。SPODSS 在不同的时间间隔对数据进行分类、验证（分析评估、跟踪、语义丰富），并根据综合结果增加消耗和预测。它通过本体开发处理 SBS 的动态演变和新的或改变的用户需求。最后，SPODSS 为原子服务、复合服务和资源分配供应生成建议。为了激发这种方法，我们通过业务流程用例和共享服务的公共数据集存储库说明了所有五个块的建议算法的实现。通过处理新的业务需求、动态问题和确保性能，确保了基于服务的系统的可持续性和适应性。性能标准包括在可用性、成熟度和风险方面的功能适用性、时间行为、资源利用率和可靠性。为了激发这种方法，我们通过业务流程用例和共享服务的公共数据集存储库说明了所有五个块的建议算法的实现。通过处理新的业务需求、动态问题和确保性能，确保了基于服务的系统的可持续性和适应性。性能标准包括在可用性、成熟度和风险方面的功能适用性、时间行为、资源利用率和可靠性。为了激发这种方法，我们通过业务流程用例和共享服务的公共数据集存储库说明了所有五个块的建议算法的实现。通过处理新的业务需求、动态问题和确保性能，确保了基于服务的系统的可持续性和适应性。性能标准包括在可用性、成熟度和风险方面的功能适用性、时间行为、资源利用率和可靠性。