A Methodology for Estimating Performance and Power Consumption of Embedded Flash File Systems

Abstract

International audienceIn the embedded systems domain, obtaining performance and power consumption estimations is extremely valuable in numerous cases. This is particularly true during the design stage, as designers of complex embedded systems face an increasingly large design space. Secondary storage is a well-known performance bottleneck and has also been reported as an important factor of power consumption. Flash memory is the main secondary storage media in an embedded system and exhibits specific constraints in its usage. One popular way to manage these constraints is to use dedicated Flash File Systems (FFS). In this article, we propose a methodology to estimate the performance and power consumption of applicative I/Os on an FFS-based storage system within embedded Linux. The methodology is divided into three sequential steps. In the exploration phase, the main factors of an FFS storage system impacting performance and power consumption are identified. In the modeling phase, this impact is formalized into models. Finally, in the last phase, the models are implemented in a simulator named OpenFlash. OpenFlash allows obtaining performance and power consumption estimations for an applicative workload processed by the Linux FFS storage stack on an embedded platform. The simulator is validated against real measurements and the estimation error stays below 10%

The Lannion report on Big Data and Security Monitoring Research

“…Flash memory is already considered as a mature technology deployed as a high performance storage media in embedded systems, Cloud and High Performance Computing (HPC) infrastructures, or even in common laptops. Several traditional algorithms and applications were revised to take full advantage of the Flash memory properties [41], such as embedded systems [42], databases [43], Cloud storage systems [44], or HPC [45].…”

Exploring research hypotheses in green computing

“…There is a significant difference between the energy consumption prediction of the linear models and real values [18][19][20] 5…”