Monday, August 25, 2014

Cloud Refactoring

A draft review of “Cloud refactoring: automated transitioning to cloud-based services” by Young-Woo Kwon and Eli Tilevich, from Automated Software Engineering, (2014) 21:345–372, DOI 10.1007/s10515-013-0136-9; to appear in Computing Reviews.

Refactoring is the process of changing how a program works without changing what it does, but even before the term was coined, it was practised by programmers as "program restructuring" from the early 1950s. Refactoring itself came to prominence with work by Opdyke, Griswold and Johnson in the early 1990s and was popularised by Fowler's 1999 book. Refactoring is done to improve code for a range of reasons: to make it conform to coding standards, to make it easier to read, or to prepare the code for modification or enhancement.

Whatever the case, refactorings of large code bases can be infeasible without automated – or semi-automated – tools, and many IDEs incorporate refactorings for a variety of languages, although it is perhaps most developed in the IDEs for the Java language, including IntelliJ and Eclipse. Refactoring "in the small" is the preserve of the developer, and may be done as a part of his or her day to day development process; larger-scale refactoring is often anticipated, but perhaps harder to justify as part of an aggressive release cycle, unless, of course, there is some concrete gain to be made. What better example could there be of this than migrating an existing system to the cloud?

Taking a system and moving it to the cloud must be the right thing to do: it provides scalability, resilience, and also fits the zeitgeist. However, as the authors make very clear, it is not without difficulties. It is important to preserve functionality – the system should not change what it does – but also it should maintain non-functional properties like efficiency (e.g. latency and throughput) and questions of resilience are more acute in a distributed/cloud setting. In common with many other refactoring tool builders, the authors propose a two-pronged approach to the problem: first, they give an analysis to provide recommendations of how systems might be refactored and then they develop a tool to implement the refactorings identified. Their first phase combines static clustering analysis with runtime profiling to identify potential services which can be migrated, and their second implements refactorings that make this change, through introducing the appropriate interfaces and proxies and at the same time adding fault-handling facilities to deal with the additional risks introduced by moving to a more distributed application platform.

These refactorings are included in Eclipse and available through its standard refactoring workflow, and the work is demonstrated through two small examples and a larger case study performed for GE Research. One of the key questions faced by designers of a cloud refactoring tool and their users alike is how much of the refactoring workflow should be automated. In discussing a number of examples the authors say that "we selected appropriate classes for the reason of the performance, call-by-reference, and meaning of features", thus making clear the role for the domain-aware engineer in the process. It would have been interesting to have heard more about the view of the developers of the software about the results of the analysis: were the services identified meaningful to them? would they have structured things somewhat differently from the analyses of the tool?

In summary, this paper makes a clear and well-described contribution to what is a fast moving field: Google Scholar, for example, gives over 1000 references matching the keywords "cloud" and "refactor", and this number can only be set to grow as migrating cloud proves to be more of a challenge than its advocates suggest.

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