Abstract

Abstract
The exponential growth of data produced, the ever faster and ubiquitous connectivity, and the collaborative processing tools lead to a clear shift of data stores from local servers to the cloud. This migration occurring across different application domains and types of users|individual or corporate|raises two immediate challenges. First, outsourcing data introduces security risks, hence protection mechanisms must be put in place to provide guarantees such as privacy, confidentiality and integrity. Second, there is no \one-size-fits-all" solution that would provide the right level of safety or performance for all applications and users, and it is therefore necessary to provide mechanisms that can be tailored to the various deployment scenarios. In this paper, we address both challenges by introducing SafeFS, a modular architecture based on software-defined storage principles featuring stackable building blocks that can be combined to construct a secure distributed file system. SafeFS allows users to specialize their data store to their specific needs by choosing the combination of blocks that provide the best safety and performance tradeoffs. The file system is implemented in user space using FUSE and can access remote data stores. The provided building blocks notably include mechanisms based on encryption, replication, and coding. We implemented SafeFS and performed indepth evaluation across a range of workloads. Results reveal that while each layer has a cost, one can build safe yet efficient storage architectures. Furthermore, the different combinations of blocks sometimes yield surprising tradeoffs. © 2017 ACM.

Abstract
With the increasing number of connected devices, it becomes essential to find novel data management solutions that can leverage their computational and storage capabilities. However, developing very large scale data management systems requires tackling a number of interesting distributed systems challenges, namely continuous failures and high levels of node churn. In this context, epidemic-based protocols proved suitable and effective and have been successfully used to build DataFlasks, an epidemic data store for massive scale systems. Ensuring resiliency in this data store comes with a significant cost in storage resources and network bandwidth consumption. Deduplication has proven to be an efficient technique to reduce both costs but, applying it to a large-scale distributed storage system is not a trivial task. In fact, achieving significant space-savings without compromising the resiliency and decentralized design of these storage systems is a relevant research challenge. In this paper, we extend DataFlasks with deduplication to design DDFlasks. This system is evaluated in a real world scenario using Wikipedia snapshots, and the results are twofold. We show that deduplication is able to decrease storage consumption up to 63% and decrease network bandwidth consumption by up to 20%, while maintaining a fullydecentralized and resilient design. © IFIP International Federation for Information Processing 2017.

Abstract

Abstract