SMC: Alternative Smart Media Compression Techniques for Edge Storage Offloading

Abstract

With the pervasiveness and growth in media technology, user-generated content has become intertwined with our day-to-day life. Such advancements, however, have enabled the exponential growth in media file sizes, which leads to shortage of storage on small-scale edge devices. While online clouds have been the default solution, they raise privacy concerns, are not fully automated, and do not adapt to different networking environments. Distributed storage systems rely on distributed file partitioning to combat concerns over privacy, and are adaptable to different networking environments. Nevertheless, such systems lack optimization via compression due to energy concerns on edge devices. In this work, we propose Smart Media Compression (SMC), a system that can be integrated with various distributed edge cloud (DEC)storage systems. SMC utilizes both a deterministic as well as a machine learning approach to classify the relevance of files to the user. Once classification is performed, SMC intelligently selects which files to compress, which files to preserve as is, and which files to offload to the distributed edge storage system. SMC dynamically adapts its parameters in order to reduce the amount of needless compression, thus minimizing energy consumption. It accomplishes this while also providing faster access to user files compared to standalone DEC systems. Our results show an improvement in average file access delay by up to 90%, while only costing an additional 14% in energy consumption.