scholarly journals Storage systems for IT infrastructure

2020 ◽  
pp. 082-093
Author(s):  
S.Yu. Punda ◽  
◽  
Keyword(s):  
Data Storage ◽  
System Performance ◽  
Storage Systems ◽  
Storage System ◽  
It Infrastructure ◽  
Solid State Drives ◽  
Advantages And Disadvantages ◽  
System A ◽  
Data Storage System ◽  
Business User

A review of modern data storage architectures was conducted, the advantages and disadvantages of each of them were given. The data storage systems of the IBM FlashSystem family were analyzed, as well as Spectrum Virtualize software, which is responsible for virtualization, compression, distribution and replication of data stored on the storage system. A mathematical model of the data storage system of IBM Storwize v5030E was developed. Well-known metrics are used to evaluate its performance when using spindle and solid-state drives. The effect of hardware and software data compression on system performance has been experimentally revealed. Recommendations are formulated by which it is possible to determine which media and which technology stack should be used by a business user to complete the tasks assigned to him.

scholarly journals REQUEST BALANCING METHOD FOR INCREASING THEIR PROCESSING EFFICIENCY WITH INFORMATION REPLICATION IN A DISTRIBUTED DATA STORAGE SYSTEM

2021 ◽  
pp. 75-82
Author(s):  
Igor Boyarshin ◽  
Anna Doroshenko ◽  
Pavlo Rehida
Keyword(s):  
Data Storage ◽  
Storage Systems ◽  
Storage System ◽  
New Method ◽  
Distributed Data ◽  
Processing Efficiency ◽  
Distributed Data Storage ◽  
Shared Data ◽  
Multiple Data ◽  
Data Storage System

The article describes a new method of improving efficiency of the systems that deal with storage and providing access of shared data of many users by utilizing replication. Existing methods of load balancing in data storage systems are described, namely RR and WRR. A new method of request balancing among multiple data storage nodes is proposed, that is able to adjust to input request stream intensity in real time and utilize disk space efficiently while doing so.

scholarly journals Benchmarking database systems for Genomic Selection implementation

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Yaw Nti-Addae ◽  
Dave Matthews ◽  
Victor Jun Ulat ◽  
Raza Syed ◽  
Guilhem Sempéré ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Data Storage ◽  
Storage Systems ◽  
Storage System ◽  
Database Systems ◽  
Turnaround Time ◽  
Breeding Programs ◽  
Open Source Data ◽  
Data Extract ◽  
Data Storage System

Abstract Motivation With high-throughput genotyping systems now available, it has become feasible to fully integrate genotyping information into breeding programs. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able to collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize it in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs, one would need an efficient genotyping data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems. Results We found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix. Availability http://gobiin1.bti.cornell.edu:6083/projects/GBM/repos/benchmarking/browse

Overview of Big-Data-Intensive Storage and Its Technologies

2018 ◽  
pp. 33-74
Author(s):  
Richard S. Segall ◽  
Jeffrey S. Cook
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Storage Systems ◽  
Storage System ◽  
Management Strategies ◽  
Sensor Data ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Future Challenges ◽  
Data Storage System

This chapter deals with a detailed discussion on the storage systems for data-intensive computing using Big Data. The chapter begins with a brief introduction about data-intensive computing and types of parallel processing approaches. It also highlights the points that display how data-intensive computing systems differ from other forms of computing. A discussion on the importance of Big Data computing is put forth. The current and future challenges of storage in genomics are discussed in detail. Also, storage and data management strategies are given. The chapter's focus is then on the software challenges for storage. Storage use cases are provided like DataDirect Networks, SDSC, etc. The list of storage tools and their details are provided. A small section discusses the sensor data storage system. Then a table is provided that shows the top 10 cloud storage systems for data-intensive computing using Big Data in the world. Top 500 Big Data storage servers statistics are also displayed effectively by the images from Top500 website.

First Prototype of High-Density Ferroelectric Data Storage System

2003 ◽  
Vol 784 ◽  
Author(s):  
Yoshiomi Hiranaga ◽  
Yasuo Cho ◽  
Yasuo Wagatsuma
Keyword(s):  
Data Storage ◽  
Transfer Rate ◽  
Data Transfer ◽  
Storage System ◽  
High Density ◽  
Practical Application ◽  
Data Array ◽  
Data Transfer Rate ◽  
System A ◽  
Data Storage System

ABSTRACTThe first prototype of high-density ferroelectric data storage system based on scanning nonlinear dielectric microscopy was developed in order to establish basic elemental technologies for actual read/write functions aiming for practical application. Using this system, a data transfer rate was evaluated. 9 kbps reading and 50 kbps writing were demonstrated with respect to 440 kbit/inch bit data array written on a lithium tantalate single crystal thin plate. Additionally, we considered future prospects for developing the data storage system with further fast data transfer rate.

scholarly journals RANCANG BANGUN SISTEM INFORMASI PAYROLL DAN LAPORAN PPH 21 BERBASIS DESKTOP PADA PT CATERPILLAR INDONESIA BATAM

2021 ◽  
Vol 9 (01) ◽  
pp. 29-36
Author(s):  
Narti Eka Putria ◽  
Erlin Elisa
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Data Storage ◽  
Storage Systems ◽  
Storage System ◽  
Work Hours ◽  
Government Agencies ◽  
Important Thing ◽  
Data Storage System ◽  
A Company

The development of information technology has created convenience and accuracy in decision making within government agencies and companies. The problem that often arises in a company is about the payroll system, this is a lot we see in electronic and print media there are employees who do demos to demand an increase in salary and no transparency in payroll. The use of computers in any company is very common and ordinary, because there are already many and even all companies use computers as a medium for storing data and controlling data. But the data storage system is still manual for example with Ms. Excell is still lacking in data storage systems. Where so far the recording of the salary of employees is still using the manual system. Given the payroll in a company is an important thing and requires proper handling so that there are no errors in the calculation of employee work hours or extra hours / overtime hours, so we need a system that can overcome these problems. Using VB.Net 2008 Programming can help in making reports that are fast, accurate, timely and also eliminate the constraints that exist in making salary data reports.

scholarly journals An Anomaly Detection and Explainability Framework using Convolutional Autoencoders for Data Storage Systems

2020 ◽  
Author(s):  
Roy Assaf ◽  
Ioana Giurgiu ◽  
Jonas Pfefferle ◽  
Serge Monney ◽  
Haris Pozidis ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Data Storage ◽  
Time Series Data ◽  
Storage Systems ◽  
Storage System ◽  
Multivariate Time Series ◽  
Abnormal Behavior ◽  
Series Data ◽  
Sequential Data ◽  
Data Storage System

Anomaly detection in data storage systems is a challenging problem due to the high dimensional sequential data involved, and lack of labels. The state of the art for automating anomaly detection in these systems typically relies on hand crafted rules and thresholds which mainly allow to distinguish between normal and abnormal behavior of each indicator in isolation. In this work we present an end-to-end framework based on convolutional autoencoders which not only allows for anomaly detection on multivariate time series data, but also provides explainability. This is done by identifying similar historic anomalies and extracting the most influential indicators. These are then presented to relevant personnel such as system designers and architects, or to support engineers for further analysis. We demonstrate the application of this framework along with an intuitive interactive web interface which was developed for data storage system anomaly detection. We discuss how this framework along with its explainability aspects enables support engineers to effectively tackle abnormal behaviors, all while allowing for crucial feedback.

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