Adopting Autonomic Computing Capabilities in Existing Large-Scale Systems

In current DevOps practice, developers are responsible for the operation and maintenance of software systems. However, the human costs for the operation and maintenance grow fast along with the increasing functionality and complexity of software systems. Autonomic computing aims to reduce or eliminate such human intervention. However, there are many existing large systems that did not consider autonomic computing capabilities in their design. Adding autonomic computing capabilities to these existing systems is particularly challenging, because of 1) the significant amount of efforts that are required for investigating and refactoring the existing code base, 2) the risk of adding additional complexity, and 3) the difficulties for allocating resources while developers are busy adding core features to the system. In this paper, we share our industrial experience of re-engineering autonomic computing capabilities to an existing large-scale software system. Our autonomic computing capabilities effectively reduce human intervention on performance configuration tuning and significantly improve system performance. In particular, we discuss the challenges that we encountered and the lessons that we learned during this re-engineering process. For example, in order to minimize the change impact to the original system, we use a variety of approaches (e.g., aspect-oriented programming) to separate the concerns of autonomic computing from the original behaviour of the system. We also share how we tested such autonomic computing capabilities under different conditions, which has never been discussed in prior work. As there are numerous large-scale software systems that still require expensive human intervention, we believe our experience provides valuable insights to software practitioners who wish to add autonomic computing capabilities to these existing large-scale software systems.