PIYAS-Proceeding to Intelligent Service Oriented Memory Allocation for Flash Based Data Centric Sensor Devices in Wireless Sensor Networks

Flash memory has become a more widespread storage medium for modern wireless devices because of its effective characteristics like non-volatility, small size, light weight, fast access speed, shock resistance, high reliability and low power consumption. Sensor nodes are highly resource constrained in terms of limited processing speed, runtime memory, persistent storage, communication bandwidth and finite energy. Therefore, for wireless sensor networks supporting sense, store, merge and send schemes, an efficient and reliable file system is highly required with consideration of sensor node constraints. In this paper, we propose a novel log structured external NAND flash memory based file system, called Proceeding to Intelligent service oriented memorY Allocation for flash based data centric Sensor devices in wireless sensor networks (PIYAS). This is the extended version of our previously proposed PIYA [1]. The main goals of the PIYAS scheme are to achieve instant mounting and reduced SRAM space by keeping memory mapping information to a very low size of and to provide high query response throughput by allocation of memory to the sensor data by network business rules. The scheme intelligently samples and stores the raw data and provides high in-network data availability by keeping the aggregate data for a longer period of time than any other scheme has done before. We propose effective garbage collection and wear-leveling schemes as well. The experimental results show that PIYAS is an optimized memory management scheme allowing high performance for wireless sensor networks

Flash-memories in Space Applications: Trends and Challenges

Nowadays space applications are provided with a processing power absolutely overcoming the one available just a few years ago. Typical mission-critical space system applications include also the issue of solid-state recorder(s). Flash-memories are nonvolatile, shock-resistant and power-economic, but in turn have different drawbacks. A solid-state recorder for space applications should satisfy many different constraints especially because of the issues related to radiations: proper countermeasures are needed, together with EDAC and testing techniques in order to improve the dependability of the whole system. Different and quite often contrasting dimensions need to be explored during the design of a flash-memory based solid- state recorder. In particular, we shall explore the most important flash-memory design dimensions and trade-offs to tackle during the design of flash-based hard disks for space application

Exploring Design Dimensions in Flash-based Mass-memory Devices

Mission-critical space system applications present several issues: a typical one is the design of a mass-memory device (i.e., a solid- state recorder). This goal could be accomplished by using flash- memories: the exploration of a huge number of parameters and trade-offs is needed. On the one hand flash-memories are nonvolatile, shock-resistant and power-economic, but on the other hand their cost is higher than normal hard disk, the number of erasure cycles is bounded and other different drawbacks have to be considered. In addition space environment presents various issues especially because of radiations: the design of a flash- memory based solid-state recorder implies the exploration of different and quite often contrasting dimensions. No systematic approach has so far been proposed to consider them all as a whole: as a consequence the design of flash-based mass-memory device for space applications is intended to be supported by a novel design environment currently under development and refinemen

FLARE: A design environment for FLASH-based space applications

Designing a mass-memory device (i.e., a solid-state recorder) is one of the typical issues of mission-critical space system applications. Flash-memories could be used for this goal: a huge number of parameters and trade-offs need to be explored. Flash-memories are nonvolatile, shock-resistant and power-economic, but in turn have different drawback: e.g., their cost is higher than normal hard disk and the number of erasure cycles is bounded. Moreover space environment presents various issues especially because of radiations: different and quite often contrasting dimensions need to be explored during the design of a flash-memory based solid-state recorder. No systematic approach has so far been proposed to consider them all as a whole: as a consequence a novel design environment currently under development is aimed at supporting the design of flash-based mass-memory device for space application

Dynamic Virtual Page-based Flash Translation Layer with Novel Hot Data Identification and Adaptive Parallelism Management

Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of NAND flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data pre-classifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of NAND flash devices with improved GC-costs, compared with related works.Peer reviewe

On Benchmarking Embedded Linux Flash File Systems

Due to its attractive characteristics in terms of performance, weight and power consumption, NAND flash memory became the main non volatile memory (NVM) in embedded systems. Those NVMs also present some specific characteristics/constraints: good but asymmetric I/O performance, limited lifetime, write/erase granularity asymmetry, etc. Those peculiarities are either managed in hardware for flash disks (SSDs, SD cards, USB sticks, etc.) or in software for raw embedded flash chips. When managed in software, flash algorithms and structures are implemented in a specific flash file system (FFS). In this paper, we present a performance study of the most widely used FFSs in embedded Linux: JFFS2, UBIFS,and YAFFS. We show some very particular behaviors and large performance disparities for tested FFS operations such as mounting, copying, and searching file trees, compression, etc.Comment: Embed With Linux, Lorient : France (2012

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe