scholarly journals Developing a procedure for conducting a security audit of a software package for predicting storage system failures

2018 ◽  
Vol 245 ◽  
pp. 10007 ◽  
Author(s):  
Marina Bolsunovskaya ◽  
Svetlana Shirokova ◽  
Aleksandra Loginova ◽  
Mikhail Uspenskij
Keyword(s):  
Information Security ◽  
Data Storage ◽  
Software Package ◽  
Storage Systems ◽  
Storage System ◽  
Current Situation ◽  
Security Audit ◽  
System Failures ◽  
Audit System ◽  
Software And Hardware

The aim of the work is to develop a procedure for conducting an information security audit of the software system for predicting data storage failures in order to identify existing threats to information security, evaluate information security tools, and improve the efficiency of existing information security tools and introduce new ones. It is necessary to monitor the current situation to ensure information security in organizations where data storage systems are used. For this purpose, an audit system has been developed, including both organizational measures and software and hardware parts.

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 State of current data storage market and development of tools for increasing data storage systems reliability

2019 ◽  
Vol 135 ◽  
pp. 04076 ◽  
Author(s):  
Marina Bolsunovskaya ◽  
Svetlana Shirokova ◽  
Aleksandra Loginova
Keyword(s):  
Data Storage ◽  
Traffic Management ◽  
Smart Home ◽  
Storage Systems ◽  
Storage System ◽  
Large Data ◽  
Current Data ◽  
Management Systems ◽  
Software Complex ◽  
Market State

This paper is devoted to the problem of developing and application of data storage systems (DSS) and tools for managing such systems to predict failures and provide fault tolerance specifications. Nowadays DSS are widely used for collecting data in Smart Home and Smart Cites management systems. For example, large data warehouses are utilized in traffic management systems. The results of the current data storage market state analysis are shown, and the project the purpose of which is to develop a hardware and software complex to predict failures in the storage system is presented.

Geo-Location-Based File Security System for Healthcare Data

2018 ◽  
pp. 125-135
Author(s):  
Govinda K.
Keyword(s):  
Data Storage ◽  
Credit Card ◽  
Medical Information ◽  
Storage Systems ◽  
Storage System ◽  
Security System ◽  
Sensitive Data ◽  
Healthcare Data ◽  
File Storage ◽  
Cryptographic Techniques

Nowadays, a person's medical information is just as important as their financial records as they may include not only names and addresses but also various sensitive data such as their employee details, bank account/credit card information, insurance details, etc. However, this fact is often overlooked when designing a file storage system for storing healthcare data. Storage systems are increasingly subject to attacks, so the security system is quickly becoming a mandatory feature of the data storage systems. For the purpose of security, we are dependent on various methods such as cryptographic techniques, two-step verification, and even biometric scanners. This chapter provides a mechanism to create a secure file storage system that provides two-layer security. The first layer is in the form of a password, through which the file is encrypted at the time of storage, and second is the locations at which the user wants the files to be accessed. Thus, this system would allow a user to access a file only at the locations specified by him/her. Therefore, the objective is to create a system that provides secure file storage based on geo-location information.

Confidential Data Storage Systems for Wearable Platforms

2018 ◽  
pp. 65-83
Author(s):  
Mingzhong Wang ◽  
Don Kerr
Keyword(s):  
Data Storage ◽  
Flash Memory ◽  
Cloud Storage ◽  
Storage Systems ◽  
Storage System ◽  
Security And Privacy ◽  
Storage Solutions ◽  
Confidential Data ◽  
Wearable Products ◽  
Confidentiality Protection

With the features of mobility, reality augmentation, and context sensitivity, wearable devices are widely deployed into various domains. However, the sensitivity of collected data makes security and privacy protection one of the first priority in the advancement of wearable technologies. This chapter provides a study on encryption-based confidentiality protection for data storage systems in wearable platforms. The chapter first conducts a review to storage solutions in consumer wearable products and explores a two-tier, local flash memory and remote cloud storage, storage system in wearable platforms. Then encryption-based confidentiality protection and implementation methods for both flash memory and remote cloud storage are summarized. According to the interaction and integration of these two components, a categorization of confidential storage systems in wearable platforms is proposed. In addition, the benefits and selection criteria for each category are also discussed.

Confidential Data Storage Systems for Wearable Platforms

2016 ◽  
pp. 74-97
Author(s):  
Mingzhong Wang ◽  
Don Kerr
Keyword(s):  
Data Storage ◽  
Flash Memory ◽  
Cloud Storage ◽  
Storage Systems ◽  
Storage System ◽  
Security And Privacy ◽  
Storage Solutions ◽  
Confidential Data ◽  
Wearable Products ◽  
Confidentiality Protection

With the features of mobility, reality augmentation, and context sensitivity, wearable devices are widely deployed into various domains. However, the sensitivity of collected data makes security and privacy protection one of the first priority in the advancement of wearable technologies. This chapter provides a study on encryption-based confidentiality protection for data storage systems in wearable platforms. The chapter first conducts a review to storage solutions in consumer wearable products and explores a two-tier, local flash memory and remote cloud storage, storage system in wearable platforms. Then encryption-based confidentiality protection and implementation methods for both flash memory and remote cloud storage are summarized. According to the interaction and integration of these two components, a categorization of confidential storage systems in wearable platforms is proposed. In addition, the benefits and selection criteria for each category are also discussed.

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

scholarly journals PRELIMINARY STUDY ON APPLICATION OF MAX PLUS ALGEBRA IN DISTRIBUTED STORAGE SYSTEM THROUGH NETWORK CODING

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Agus Maman Abadi ◽  
Musthofa Musthofa ◽  
Emut Emut
Keyword(s):  
Network Coding ◽  
Data Storage ◽  
Algebraic Structure ◽  
Storage Systems ◽  
Distributed Storage ◽  
Storage System ◽  
Distributed Data Storage ◽  
Erasure Code ◽  
Distributed Storage Systems ◽  
Set Up

The increasing need in techniques of storing big data presents a new challenge. One way to address this challenge is the use of distributed storage systems. One strategy that implemented in distributed data storage systems is the use of Erasure Code which applied to network coding. The code used in this technique is based on the algebraic structure which is called as vector space. Some studies have also been carried out to create code that is based on other algebraic structures such as module.  In this study, we are going to try to set up a code based on the algebraic structure which is a generalization of the module that is semimodule by utilizing the max operations and sum operations at max plus algebra. The results of this study indicate that the max operation and the addition operation on max plus algebra cannot be used to establish a semimodule code, but by modifying the operation "+" as "min", we get a code based on semimodule. Keywords:   code, distributed storage systems, network coding, semimodule, max plus algebra

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.

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.

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