Fault-Tolerant Dynamic Deduplication for Utility Computing

Utility computing is an increasingly important paradigm, whereby computing resources are provided on-demand as utilities. An important component of utility computing is storage, data volumes are growing rapidly, and mechanisms to mitigate this growth need to be developed. Data deduplication is a promising technique for drastically reducing the amount of data stored in such system systems, however, current approachs are static in nature, using an amount of redundancy fixed at design time. This is inappropriate for truly dynamic modern systems. We propose a real-time adaptive deduplication system for Cloud and Utility computing that monitors in real-time for changing system, user, and environmental behaviour in order to fulfill a balance between changing storage efficiency, performance, and fault tolerance requirements. We evaluate our system through simulation, with experimental results showing that our system is both efficient and sclable. We also perform experimentation to evaluate the fault tolerance of the system by measuring Mean Time to Repair (MTTR), and using these values to calculate availability of the system. The results show that higher replication levels result in higher system availability, however, the number of files in the system also effects recovery time. We show that the tradeoff between replication levels and recovery time when the system overloads needs further investigation