Parallel Data Storage and Access

The research on establishing an automated data acquisition system with high speed and accuracy is a hot topic in measurement and testing area. The desire is extremely high in data acquisition systems with high speed, multi-channels and large capacity, therefore research into this field becomes very important and significant. In this paper, a LXI high speed parallel data acquisition system which is combined LXI bus with high-speed data acquisition ability is proposed. And the signal conditioning circuit, ADC, data storage circuit, FPGA main control circuit and LXI interface circuit are introduced in detail. The remote control of the data acquisition system and the high speed transmission of data are realized by using the LXI bus.

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A Parallel Data Storage Interface to GridFTP

On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE - Lecture Notes in Computer Science ◽

10.1007/11914952_10 ◽

2006 ◽

pp. 1203-1212 ◽

Cited By ~ 1

Author(s):

Alberto Sánchez ◽

María S. Pérez ◽

Pierre Gueant ◽

Jesús Montes ◽

Pilar Herrero

Keyword(s):

Data Storage ◽

Parallel Data

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Performance Evaluation of Network-Parallel Data Storage

2006 2nd International Conference on Information & Communication Technologies ◽

10.1109/ictta.2006.1684962 ◽

2006 ◽

Author(s):

Q. Malluhi ◽

Zhi Lin ◽

M. Malouhi

Keyword(s):

Performance Evaluation ◽

Data Storage ◽

Parallel Data

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Performance-efficient Recommendation and Prediction Service for Big Data frameworks focusing on Data Compression and In-memory Data Storage Indicators

Scalable Computing Practice and Experience ◽

10.12694/scpe.v22i4.1945 ◽

2021 ◽

Vol 22 (4) ◽

pp. 401-412

Author(s):

Hrachya Astsatryan ◽

Arthur Lalayan ◽

Aram Kocharyan ◽

Daniel Hagimont

Keyword(s):

Big Data ◽

Data Compression ◽

Data Storage ◽

File Systems ◽

Large Datasets ◽

Data Sets ◽

Mapreduce Framework ◽

Data Intensive ◽

Parallel Data ◽

Data Intensive Applications

The MapReduce framework manages Big Data sets by splitting the large datasets into a set of distributed blocks and processes them in parallel. Data compression and in-memory file systems are widely used methods in Big Data processing to reduce resource-intensive I/O operations and improve I/O rate correspondingly. The article presents a performance-efficient modular and configurable decision-making robust service relying on data compression and in-memory data storage indicators. The service consists of Recommendation and Prediction modules, predicts the execution time of a given job based on metrics, and recommends the best configuration parameters to improve Hadoop and Spark frameworks' performance. Several CPU and data-intensive applications and micro-benchmarks have been evaluated to improve the performance, including Log Analyzer, WordCount, and K-Means.

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