Exploiting the Interplay between Memory and Flash Storage in Embedded Sensor Devices

Agrawal, Devesh; Li, Boduo; Cao, Zhao; Ganesan, Deepak; Diao, Yanlei; Shenoy, Prashant

doi:10.1109/rtcsa.2010.10

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…However, these techniques introduce additional translation delay and require large memory to keep the translation tables. To resolve the limitation of main memory, various issues on the interplay between main memory and flash storage in indexing were studied in [7], and an adaptive memory management scheme was proposed. Observing that many databases for flash memory were not optimized for all types of flash devices and for all workloads, Nath and Kansal [29] designed a dynamic self-tuning database system that can dynamically adapt its storage structure to workloads and underlying storage devices.…”

Section: Related Workmentioning

confidence: 99%

“…In a CPS, sensors are deployed in an operation environment to monitor the real-time status of various entities in the operation environment. They periodically publish sensor measurements as updates to be installed into an embedded database system which normally has its database resided in flash memory instead of a disk storage system [5][6][7]. Each update creates a new version for a data item while the old versions are still maintained, i.e., multi-version databases [8].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the on-shelf LOTUS LPR2400 sensor node platform is equipped with 64 KB of RAM and 64 MB of flash memory [24], while the on-shelf Atmel AVR2016 development kit has only 8 KB of RAM and 16 MB of flash memory [25]. Moreover, some previous studies have considered sensor nodes with 16 MB or even hundreds of megabytes of flash memory storage on board [26][27][28]. The gap between the size of the on-board RAM and flash memory thus imposes great difficulties on the management of data and flash memory space in memory-constrained CPSes, such as wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

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Block-Based Multi-Version B$^+$-Tree for Flash-Based Embedded Database Systems

Wang

Lam

Chang

et al. 2015

IEEE Trans. Comput.

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In this paper, we propose a novel multi-version B + -tree index structure, called block-based multi-version B + -tree (BbMVBT ), for indexing multi-versions of data items in an embedded multi-version database (EMVDB) on flash memory. An EMVDB needs to support streams of update transactions and version-range queries to access different versions of data items maintained in the database. In BbMVBT, the index is divided into two levels. At the higher level, a multi-version index is maintained for keeping successive versions of each data item. These versions are allocated consecutively in a version block. At the lower level, a version array is used to search for a specific data version within a version block. With the reduced index structure of BbMVBT, the overhead for managing the index in processing update operations can be greatly reduced. At the same time, BbMVBT can also greatly reduce the number of accesses to the index in processing versionrange queries. To ensure sufficient free blocks for creating version blocks for efficient execution of BbMVBT, in this paper, we also discuss how to perform garbage collection using the purging-range queries for reclaiming "old" versions of data items and their associated entries in the index nodes. Analysis of the performance of BbMVBT is presented and verified with performance studies using both synthetic and real workloads. The performance results illustrate that BbMVBT can significantly improve the read and write performance to the multi-version index as compared with MVBT even though the sizes of the version blocks are not large.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Block-Based Multi-Version B$^+$-Tree for Flash-Based Embedded Database Systems

Wang

Lam

Chang

et al. 2015

IEEE Trans. Comput.

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“…Mathur et al [2006] perform an extensive study of available flash memory candidates for sensor devices and demonstrate that an off-chip parallel NAND flash memory decreases the energy consumption of storage. Considering the off-chip NAND flash memory as the best candidate for sensor devices, Agrawal et al [2010] propose a method that allows sensor devices to exploit their flash memory while adapting to different amounts of RAM. Newer storage technology such as phase-change memory (PCM) or ferro-electric RAM (FRAM) provide low-power, non-volatile storage for embedded devices.…”

Section: Related Workmentioning

confidence: 99%

Half-Wits

Salajegheh

Wang²,

Jiang³

et al. 2013

ACM Trans. Embed. Comput. Syst.

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This work analyzes the stochastic behavior of writing to embedded flash memory at voltages lower than recommended by a microcontroller's specifications in order to reduce energy consumption. Flash memory integrated within a microcontroller typically requires the entire chip to operate on a common supply voltage almost twice as much as what the CPU portion requires. Our software approach allows the flash memory to tolerate a lower supply voltage so that the CPU may operate in a more energy-efficient manner. Energyefficient coding algorithms then cope with flash memory writes that behave unpredictably.Our software-only coding algorithms (in-place writes, multiple-place writes, RS-Berger codes, and slow writes) enable reliable storage at low voltages on unmodified hardware by exploiting the electrically cumulative nature of half-written data in write-once bits. For a sensor monitoring application using the MSP430, coding with in-place writes reduces the overall energy consumption by 34%. In-place writes are competitive when the time spent on low-voltage operations such as computation are at least four times greater than the time spent on writes to flash memory. Our evaluation shows that tightly maintaining the digital abstraction for storage in embedded flash memory comes at a significant cost to energy consumption with minimal gain in reliability. We find our techniques most effective for embedded workloads that have significant duty cycling, rare writes, or energy harvesting.

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“…Due to its performance and energy benefits, flash memory is often adopted as an alternative storage medium to hard disk for the maintenance of embedded databases in many CPSes [Li and Liu 2009;Zeinalipour-Yazti et al 2005;Agrawal et al 2010]. However, the unique performance characteristics of flash memory also introduce a number of challenging issues in the management of embedded databases on flash memory.…”

Section: Introductionmentioning

confidence: 99%

Garbage Collection for Multiversion Index in Flash-Based Embedded Databases

Huang

Chang

Lam

et al. 2014

ACM Trans. Des. Autom. Electron. Syst.

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Recently, flash-based embedded databases have gained their momentum in various control and monitoring systems, such as cyber-physical systems (CPSes). To support the functionality to access the historical data, a multiversion index is adopted to simultaneously maintain multiple versions of data items, as well as their index information. However, maintaining a multiversion index on flash memory incurs considerable performance overheads on garbage collection, which is to reclaim the spaces occupied by the outdated/invalid data items and their index information on flash memory. In this work, we propose an efficient garbage collection strategy to solve the garbage collection issues of flash-based multiversion databases. In particular, a version-tracking method is proposed to accelerate the performance on the process on identifying/reclaiming the space of invalid data and their indexes, and a pre-summary method is also designed to solve the cascading update problem that is caused by the write-once nature of flash memory and is worsened when more versions refer to the same data item. The capability of the proposed strategy is then verified by analytical and experimental studies.

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Exploiting the Interplay between Memory and Flash Storage in Embedded Sensor Devices

Cited by 7 publications

References 18 publications

Block-Based Multi-Version B$^+$-Tree for Flash-Based Embedded Database Systems

Block-Based Multi-Version B$^+$-Tree for Flash-Based Embedded Database Systems

Half-Wits

Garbage Collection for Multiversion Index in Flash-Based Embedded Databases

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