Dynamic data deduplication in cloud storage

Cloud computing plays a major role in the business domain today as computing resources are delivered as a utility on demand to customers over the Internet. Cloud storage is one of the services provided in cloud computing which has been increasing in popularity. The main advantage of using cloud storage from the customers' point of view is that customers can reduce their expenditure in purchasing and maintaining storage infrastructure while only paying for the amount of storage requested, which can be scaled-up and down upon demand. With the growing data size of cloud computing, a reduction in data volumes could help providers reducing the costs of running large storage system and saving energy consumption. So data deduplication techniques have been brought to improve storage efficiency in cloud storages. With the dynamic nature of data in cloud storage, data usage in cloud changes overtime, some data chunks may be read frequently in period of time, but may not be used in another time period. Some datasets may be frequently accessed or updated by multiple users at the same time, while others may need the high level of redundancy for reliability requirement. Therefore, it is crucial to support this dynamic feature in cloud storage. However current approaches are mostly focused on static scheme, which limits their full applicability in dynamic characteristic of data in cloud storage. In this paper, we propose a dynamic deduplication scheme for cloud storage, which aiming to improve storage efficiency and maintaining redundancy for fault tolerance

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