商业云服务评价的系统性综述
On Evaluating Commercial Cloud Services: A Systematic Review
摘要：
背景：云计算在行业中日益蓬勃发展，有许多竞争的提供商和服务。因此，评估商业云服务是必要的。然而，现有的评价研究相对混乱。云服务评估的实践与理论之间存在着巨大的困惑和差距。目的：为了促进缓解上述混乱，这项工作的目的是综合现有的评估实施，概述了国家的实践，并确定在云服务评估的研究机会。方法：基于一个包含6个步骤的概念性评价模型，采用系统文献综述（SLR）方法收集相关证据，对云服务评价进行了分步研究。结果：本SLR识别了82项相关评价研究。从这些研究中收集的总体数据基本上代表了当前实施云服务评估的实际情况，反过来可以重用以促进未来的评估工作。结论：商业云服务的评估已经成为一个全球性的研究课题。本SLR的一些调查结果确定了云服务评估领域的几个研究空白（例如，商业云服务的弹性和安全性评估可能是一个长期的挑战），而其他一些发现表明了应用商业云服务的趋势（例如，与PaaS相比，IaaS似乎更适合客户，在行业中尤为重要）。这项SLR研究本身也证实了以前的一些经验，并揭示了新的循证软件工程（EBSE）的教训。
英文摘要：
Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
