Enosis: Bridging the Semantic Gap between File-Based and Object-Based Data Models
Workshop: The Eighth International Workshop on Data-Intensive Computing in the Clouds
Abstract: File and block storage are well-defined concepts in computing and have been used as common components of computer systems for decades. Big data has led to new types of storage. The predominant data model in cloud storage is the object-based storage and it is highly successful. Object stores follow a simpler API with get() and put() operations to interact with the data. A wide variety of data analysis software have been developed around objects using their APIs. However, object storage and traditional file storage are designed for different purpose and for different applications. Organizations maintain file-based storage clusters and a high volume of existing data are stored in files. Moreover, many new applications need to access data from both types of storage.
In this paper, we first explore the key differences between object-based and the more traditional file-based storage systems. We have designed and implemented several file-to-object mapping algorithms to bridge the semantic gap between these data models. Our evaluation shows that by achieving an efficient such mapping, our library can grant 2x-27x higher performance against a naive one-to-one mapping and with minimal overheads. Our study exposes various strengths and weaknesses of each mapping strategy and frames the extended potential of a unified data access system.