Abstract
Online memory compression is a technology that increases the amount of memory available to applications by dynamically compressing and decompressing their working datasets on demand. It has proven extremely useful in embedded systems with tight physical RAM constraints. The technology can be used to increase functionality, reduce size, and reduce cost, without modifying applications or hardware. This article presents a new software-based online memory compression algorithm for embedded systems. In comparison with the best algorithms used in online memory compression, our new algorithm has a competitive compression ratio but is twice as fast. In addition, we describe several practical problems encountered in developing an online memory compression infrastructure and present solutions. We present a method of adaptively managing the uncompressed and compressed memory regions during application execution. This memory management scheme adapts to the predicted memory requirements of applications. It permits efficient compression for a wide range of applications. We have evaluated our techniques on a portable embedded device and have found that the memory available to applications can be increased by 2.5× with negligible performance and power consumption penalties, and with no changes to hardware or applications. Our techniques allow existing applications to execute with less physical memory. They also allow applications with larger working datasets to execute on unchanged embedded system hardware, thereby increasing functionality.
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