Towards a Self-Adaptive Data Management System for Cloud Environments

The emergence of Cloud computing brings forward many challenges that may limit the adoption rate of the Cloud paradigm. As data volumes processed by applications running on Clouds increase, the need for efficient and secure data management emerges as a crucial requirement. This work aims to enable BlobSeer, a large-scale data management system, as a Cloud data service, by addressing a series of self-management requirements. The first step towards an autonomic behavior was to equip the BlobSeer platform with introspection capabilities, which can serve as input data for a self-adaptive engine designed to address such goals as self-configuration, self-optimization or self-protection. We developed the self-protection direction within a generic security management framework allowing providers of Cloud data management systems to define and enforce complex security policies. In addition, we designed an expressive policy description language enabling system administrators to define a large array of security attacks and to enforce various types of restrictions upon the detected malicious clients. Finally, we are integrating our data-management system as a storage back end within the Nimbus Cloud environment.