No abstract

Flash memories are in ubiquitous use for storage on sensor nodes, mobile devices, and enterprise servers. However, they present significant challenges in designing tree indexes due to their fundamentally different read and write characteristics in comparison to magnetic disks. In this paper, we present the Lazy-Adaptive Tree (LA-Tree), a novel index structure that is designed to improve performance by minimizing accesses to flash. The LA-tree has three key features: 1) it amortizes the cost of node reads and writes by performing update operations in a lazy manner using cascaded buffers, 2) it dynamically adapts buffer sizes to workload using an online algorithm, which we prove to be optimal under the cost model for raw NAND flashes, and 3) it optimizes index parameters, memory management, and storage reclamation to address flash constraints. Our performance results on raw NAND flashes show that the LA-Tree achieves 2x to 12x gains over the best of alternate schemes across a range of workloads and memory constraints. Initial results on SSDs are also promising, with 3x to 6x gains in most cases.

Abstract-Although memory is an important constraint in embedded sensor nodes, existing sensor applications and systems are typically designed to work under the memory constraints of a single platform and do not consider the interplay between memory and flash storage. In this paper, we present the design of a memory-adaptive flash-based sensor system that allows an application to exploit the presence of flash and adapt to different amounts of RAM on the embedded device. We describe how such a system can be exploited by sensor data management applications. Our design involves several novel features: flash and memory-efficient storage and indexing, techniques for efficient storage reclamation, and intelligent buffer management to maximize write coalescing. Our results show that our system is highly energy-efficient under different workloads, and can be configured for sensor platforms with memory constraints ranging from a few kilobytes to hundreds of kilobytes.

No abstract

No abstract