67,091 research outputs found
Performance Evaluation of Flash File Systems
Today, flash memory are strongly used in the embedded system domain. NAND
flash memories are the building block of main secondary storage systems. Such
memories present many benefits in terms of data density, I/O performance, shock
resistance and power consumption. Nevertheless, flash does not come without
constraints: the write / erase granularity asymmetry and the limited lifetime
bring the need for specific management. This can be done through the operating
system using dedicated Flash File Systems (FFSs). In this document, we present
general concepts about FFSs, and implementations example that are JFFS2, YAFFS2
and UBIFS, the most commonly used flash file systems. Then we give performance
evaluation results for these FFSs.Comment: Colloque du GDR SoC-SiP, Paris : France (2012
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Benefit of DDN's IME-FUSE for I/O intensive HPC applications
Many scientific applications are limited by I/O performance offered by parallel file systems on conventional storage systems. Flash- based burst buffers provide significant better performance than HDD backed storage, but at the expense of capacity. Burst buffers are consid- ered as the next step towards achieving wire-speed of interconnect and providing more predictable low latency I/O, which are the holy grail of storage. A critical evaluation of storage technology is mandatory as there is no long-term experience with performance behavior for particular applica- tions scenarios. The evaluation enables data centers choosing the right products and system architects the integration in HPC architectures. This paper investigates the native performance of DDN-IME, a flash- based burst buffer solution. Then, it takes a closer look at the IME-FUSE file systems, which uses IMEs as burst buffer and a Lustre file system as back-end. Finally, by utilizing a NetCDF benchmark, it estimates the performance benefit for climate applications
Efficient and Reliable Task Scheduling, Network Reprogramming, and Data Storage for Wireless Sensor Networks
Wireless sensor networks (WSNs) typically consist of a large number of resource-constrained nodes. The limited computational resources afforded by these nodes present unique development challenges. In this dissertation, we consider three such challenges. The first challenge focuses on minimizing energy usage in WSNs through intelligent duty cycling. Limited energy resources dictate the design of many embedded applications, causing such systems to be composed of small, modular tasks, scheduled periodically. In this model, each embedded device wakes, executes a task-set, and returns to sleep. These systems spend most of their time in a state of deep sleep to minimize power consumption. We refer to these systems as almost-always-sleeping (AAS) systems. We describe a series of task schedulers for AAS systems designed to maximize sleep time. We consider four scheduler designs, model their performance, and present detailed performance analysis results under varying load conditions. The second challenge focuses on a fast and reliable network reprogramming solution for WSNs based on incremental code updates. We first present VSPIN, a framework for developing incremental code update mechanisms to support efficient reprogramming of WSNs. VSPIN provides a modular testing platform on the host system to plug-in and evaluate various incremental code update algorithms. The framework supports Avrdude, among the most popular Linux-based programming tools for AVR microcontrollers. Using VSPIN, we next present an incremental code update strategy to efficiently reprogram wireless sensor nodes. We adapt a linear space and quadratic time algorithm (Hirschberg\u27s Algorithm) for computing maximal common subsequences to build an edit map specifying an edit sequence required to transform the code running in a sensor network to a new code image. We then present a heuristic-based optimization strategy for efficient edit script encoding to reduce the edit map size. Finally, we present experimental results exploring the reduction in data size that it enables. The approach achieves reductions of 99.987% for simple changes, and between 86.95% and 94.58% for more complex changes, compared to full image transmissions - leading to significantly lower energy costs for wireless sensor network reprogramming. The third challenge focuses on enabling fast and reliable data storage in wireless sensor systems. A file storage system that is fast, lightweight, and reliable across device failures is important to safeguard the data that these devices record. A fast and efficient file system enables sensed data to be sampled and stored quickly and batched for later transmission. A reliable file system allows seamless operation without disruptions due to hardware, software, or other unforeseen failures. While flash technology provides persistent storage by itself, it has limitations that prevent it from being used in mission-critical deployment scenarios. Hybrid memory models which utilize newer non-volatile memory technologies, such as ferroelectric RAM (FRAM), can mitigate the physical disadvantages of flash. In this vein, we present the design and implementation of LoggerFS, a fast, lightweight, and reliable file system for wireless sensor networks, which uses a hybrid memory design consisting of RAM, FRAM, and flash. LoggerFS is engineered to provide fast data storage, have a small memory footprint, and provide data reliability across system failures. LoggerFS adapts a log-structured file system approach, augmented with data persistence and reliability guarantees. A caching mechanism allows for flash wear-leveling and fast data buffering. We present a performance evaluation of LoggerFS using a prototypical in-situ sensing platform and demonstrate between 50% and 800% improvements for various workloads using the FRAM write-back cache over the implementation without the cache
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
DOH: A Content Delivery Peer-to-Peer Network
Many SMEs and non-pro¯t organizations su®er when their Web
servers become unavailable due to °ash crowd e®ects when their web site
becomes popular. One of the solutions to the °ash-crowd problem is to place
the web site on a scalable CDN (Content Delivery Network) that replicates
the content and distributes the load in order to improve its response time.
In this paper, we present our approach to building a scalable Web Hosting
environment as a CDN on top of a structured peer-to-peer system of collaborative
web-servers integrated to share the load and to improve the overall
system performance, scalability, availability and robustness. Unlike clusterbased
solutions, it can run on heterogeneous hardware, over geographically
dispersed areas. To validate and evaluate our approach, we have developed a
system prototype called DOH (DKS Organized Hosting) that is a CDN implemented
on top of the DKS (Distributed K-nary Search) structured P2P
system with DHT (Distributed Hash table) functionality [9]. The prototype
is implemented in Java, using the DKS middleware, the Jetty web-server, and
a modiÂŻed JavaFTP server. The proposed design of CDN has been evaluated
by simulation and by evaluation experiments on the prototype
SimpleSSD: Modeling Solid State Drives for Holistic System Simulation
Existing solid state drive (SSD) simulators unfortunately lack hardware
and/or software architecture models. Consequently, they are far from capturing
the critical features of contemporary SSD devices. More importantly, while the
performance of modern systems that adopt SSDs can vary based on their numerous
internal design parameters and storage-level configurations, a full system
simulation with traditional SSD models often requires unreasonably long
runtimes and excessive computational resources. In this work, we propose
SimpleSSD, a highfidelity simulator that models all detailed characteristics of
hardware and software, while simplifying the nondescript features of storage
internals. In contrast to existing SSD simulators, SimpleSSD can easily be
integrated into publicly-available full system simulators. In addition, it can
accommodate a complete storage stack and evaluate the performance of SSDs along
with diverse memory technologies and microarchitectures. Thus, it facilitates
simulations that explore the full design space at different levels of system
abstraction.Comment: This paper has been accepted at IEEE Computer Architecture Letters
(CAL
Elevating commodity storage with the SALSA host translation layer
To satisfy increasing storage demands in both capacity and performance,
industry has turned to multiple storage technologies, including Flash SSDs and
SMR disks. These devices employ a translation layer that conceals the
idiosyncrasies of their mediums and enables random access. Device translation
layers are, however, inherently constrained: resources on the drive are scarce,
they cannot be adapted to application requirements, and lack visibility across
multiple devices. As a result, performance and durability of many storage
devices is severely degraded.
In this paper, we present SALSA: a translation layer that executes on the
host and allows unmodified applications to better utilize commodity storage.
SALSA supports a wide range of single- and multi-device optimizations and,
because is implemented in software, can adapt to specific workloads. We
describe SALSA's design, and demonstrate its significant benefits using
microbenchmarks and case studies based on three applications: MySQL, the Swift
object store, and a video server.Comment: Presented at 2018 IEEE 26th International Symposium on Modeling,
Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS
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