Analyzing and Migrating an Implemented System

Abstract

While modern systems are often built in a way that respects cloud computing requirements, the majority of existing systems have been built before dynamically allocatable resources became mainstream. Those systems have been designed and implemented for dedicated hardware, often large-scale servers, which had been sized to the maximum workload the systems were expected to face. This approach led to a high amount of wasted resources, which were operated in a stand-by fashion only to deal with seldom high-load situations. Therefore, it is desirable to migrate legacy systems in a way that they can benefit from the cloud computing approach. However, several issues arise. Legacy systems were often built without upfront modeling or the models became outdated over time. Additionally, while there are several tools that analyze legacy systems to detect insufficient coding style or bad designs, almost no tooling exists that spots defects in the systems. This deficiency hinders systems to smoothly operate in cloud computing environments, i.e., these systems have a limited scalability. In CloudScale, we address this issue by dedicated, built-in method support for system evolution, i.e., for migrating legacy systems to cloud computing environments. In this chapter, we outline CloudScale’s evolution support and present tools which help software architects to migrate legacy systems to scalable, cloud computing applications.

Section 7.2 describes the process steps when spotting HowNotTos. Statical detection of HowNotTos is further elaborated in Sect. 7.3, while dynamic detection is discussed in Sect. 7.4. Section 7.5 links HowNotTos with best-practice HowTos. An example is described in Sect. 7.6.