云计算领域的知识管理
Knowledge Management for Cloud Computing Field
摘要：
将遗留系统迁移到云平台是一个知识密集型过程。有越来越多的知识报告成功和有问题的云迁移的经验方案。重复使用这些分散在学术/多声音文献中的知识体系，对于减轻传统到云和云到云迁移的进一步项目中的昂贵风险和陷阱具有实际价值。因此，与云迁移相关的知识管理系统/平台是组织的首要先决条件和战略必要性。我们进行了一项定性探索性研究，以了解在实际试验中为云迁移开发知识管理系统（KMS）的好处和挑战。虽然我们的原型系统演示支持了开发云迁移KMS（CM-KMS）的重要性和好处，但我们对11名参与者的半结构化行业访谈研究强调了开发此类KMS的挑战性障碍。因此，本研究提出了九个重大挑战，导致放弃CM-KMS的设计和维护，包括持续的变化和更新，知识的集成，知识粒度，上下文的保存，自动化，解构传统知识，对专家的依赖，供应商特定和供应商中立的云平台的混合知识，和简约。我们的结果通知云架构师注意采用CM-KMS在其组织中进行遗产到云的迁移。
英文摘要：
Migration legacy systems to cloud platforms is a knowledge intensive process. There is an ever increasing body of knowledge reporting empirical scenarios of successful and problematic cloud migration. Reusing this body of knowledge, dispersed and fragmented over the academic/multi-vocal literature, has practical values to mitigate costly risks and pitfalls in further projects of legacy to-cloud and cloud-to-cloud migration. In line with this, knowledge management systems/platforms pertinent to cloud migration are a prime prerequisite and a strategic imperative for an organization. We have conducted a qualitative exploratory study to understand the benefits and challenges of developing Knowledge Management Systems (KMS) for cloud migration in real trials. Whilst our prototype system demonstration supported the importance and bene-fits of developing Cloud Migration KMS (CM-KMS), our semi-structured industry interview study with 11 participants highlighted challenging impediments against developing this class of KMS. As a result, this study proposes nine significant challenges that cause the abandon of the design and maintenance of CM-KMS, including continuous changes and updates, integration of knowledge, knowledge granularity, preservation of context, automation, deconstruction of traditional knowledge, dependency on experts, hybrid knowledge of both vendor-specific and vendor-neutral cloud platforms, and parsimony. Our results inform cloud architects to pay attention to adopt CM-KMS for the legacy-to-cloud migration in their organizations.
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。
