Goal-oriented Self-management of In-memory Distributed Data Grid Platforms

This paper addresses the self-management of in-memory distributed data grid platforms. A growing number of applications rely in these platforms to speed up access to large sets of data. However, they are complex to manage due to the diversity of configuration and load profiles. The proposed approach employs an adaptation policy expressed in terms of high-level goals to facilitate the task of the system manager, and address the complexity issues posed by the management of multiple configurations. The approach is validated experimentally using the open-source RedHat´s Infinispan platform.