  In the age of the Cloud and so-called Big Data systems must be increasingly
flexible, reconfigurable and adaptable to change in addition to being developed
rapidly. As a consequence, designing systems to cater for evolution is becoming
critical to their success. To be able to cope with change, systems must have
the capability of reuse and the ability to adapt as and when necessary to
changes in requirements. Allowing systems to be self-describing is one way to
facilitate this. To address the issues of reuse in designing evolvable systems,
this paper proposes a so-called description-driven approach to systems design.
This approach enables new versions of data structures and processes to be
created alongside the old, thereby providing a history of changes to the
underlying data models and enabling the capture of provenance data. The
efficacy of the description-driven approach is exemplified by the CRISTAL
project. CRISTAL is based on description-driven design principles; it uses
versions of stored descriptions to define various versions of data which can be
stored in diverse forms. This paper discusses the need for capturing holistic
system description when modelling large-scale distributed systems.
