Context

Many organizations undertake large-scale projects of application migration due to availability of scalable and cost-efficient technologies. Such legacy application migration projects are very complex since the process involves in-depth profiling of the applications.

Objective

During the initial profiling phase, it is imperative to understand the underlying complexities of individual applications, as well as the interdependencies among applications in the organization. This analysis phase can take considerable time and effort, depending on number and complexity of the applications. The main goal of this paper is to provide a framework that provides a cost-effective and quick approach to study the interdependencies between legacy applications with minimal prior knowledge of application usage.

Method

In this paper, we propose a framework that uses community detection algorithms and other established techniques from graph theory, to discover interdependencies of legacy applications within an organization, group these highly interdependent legacy applications in clusters, and finally sequence the clusters for migration to a modern platform. We study the proposed framework through three case studies, using network datasets from a large US organization.

Results

The experimental results from the proposed framework suggests that legacy applications can be grouped into clusters with high interdependencies between each other. Also, the framework shows how organizations can then appropriately sequence the clusters of legacy applications into a phase-wise migration project, thereby reducing migration costs.

Conclusion

The proposed framework provides a valuable design input to organizations on how to determine the interdependencies between the various legacy applications that are in scope for migration to a modern platform. Such large-scale migration projects can be simplified and broken down to use a systematic approach, thereby reducing migration costs and data integrity challenges.

中文翻译：

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多个遗留应用程序的分阶段迁移–图论方法

语境

由于可扩展且具有成本效益的技术的可用性，许多组织进行了大规模的应用程序迁移项目。由于该过程涉及对应用程序的深入分析，因此此类传统的应用程序迁移项目非常复杂。

客观的

在初始配置阶段，必须了解单个应用程序的潜在复杂性以及组织中应用程序之间的相互依赖性。根据应用程序的数量和复杂性，此分析阶段可能会花费大量时间和精力。本文的主要目的是提供一个框架，该框架提供了一种经济高效的方法，可以以最少的应用程序使用先验知识来研究遗留应用程序之间的相互依赖性。

方法

在本文中，我们提出了一个框架，该框架使用图论中的社区检测算法和其他已建立的技术，以发现组织内遗留应用程序的相互依赖性，将这些高度相互依存的遗留应用程序分组到集群中，最后对集群进行排序以进行向现代迁移。平台。我们使用来自美国一家大型组织的网络数据集，通过三个案例研究来研究提出的框架。

结果

提出的框架的实验结果表明，可以将遗留应用程序分组为彼此之间具有高度相互依赖性的群集。此外，该框架还展示了组织如何才能将遗留应用程序的集群适当地排序到分阶段的迁移项目中，从而降低了迁移成本。

结论

拟议的框架为组织提供了宝贵的设计意见，帮助他们确定如何确定各种传统应用程序之间的相互依赖性，这些遗留应用程序将迁移到现代平台。可以简化和分解此类大型迁移项目，以使用系统的方法，从而减少迁移成本和数据完整性挑战。