Technologies for High Availability and Low (or No) Data Loss

2016 ◽  
pp. 207-218
Keyword(s):  
High Availability ◽  
Data Loss

scholarly journals Designing Highly Loaded Systems

2020 ◽  
Vol 32 (6) ◽  
pp. 79-86
Author(s):  
Vasilii Andreevich Rudometkin
Keyword(s):  
System Architecture ◽  
Negative Impact ◽  
High Load ◽  
High Availability ◽  
Data Loss ◽  
Reading And Writing ◽  
System Components ◽  
To Receive ◽  
Highly Loaded

Nowadays, most of the services are moving online, which allows users to receive the service at any time. The high availability of the service leads to an increase in the number of users, which entails an increase in the load on the system. High load has a negative impact on system components, which can lead to malfunctions and data loss. To avoid this, the article discusses several design and monitoring approaches, the observance of which will help prevent system malfunctioning. The article describes the most popular way to distribute the area of responsibility of each service, in accordance with the DDD pattern, the use of which will allow you to separate the components of the system logically by use and physically when scaling the system. This approach will also be useful when scaling a team and allow developers to work independently on different system components without interfering with each other. The integration of new people into the project will also take the shortest possible time. When designing the system architecture, it is worth paying attention to the scheme of interaction between services. Using the CQRS pattern allows you to separate reading and writing into different components, which later allows the user to quickly receive a response from the system. Particular attention in the article is paid to monitoring the system, since with an increase in the size of the system, the time to search for errors in the system reaches a large amount of time, which can lead to a long unavailability of the system, which will entail the loss of clients. All the methods described in the article have been applied on many projects, for example, MTS POISK. Thanks to a properly designed system, it was possible to reduce the waiting time for a service response from two minutes to several seconds without losing the quality of the result, and a sophisticated system monitoring system allows you to monitor all processes within the system in real time and prevent accidents. As a result, at the beginning of the system design, special attention should be paid to the architecture, the issue of monitoring and testing the system. Subsequently, these temporary investments will reduce the risks of data loss and system unavailability.

scholarly journals Implementation and verification of high data availability on database

2019 ◽  
Vol 7 (5) ◽  
Author(s):  
Yen-Jen Chen ◽  
Han Tsai
Keyword(s):  
System Architecture ◽  
Management System ◽  
Database Management ◽  
Hard Disk ◽  
Data Access ◽  
High Availability ◽  
Database Management System ◽  
Test Method ◽  
Cache Memory ◽  
Data Loss

This study provides a low-cost and high-availability database management system architecture for general Small/Medium Enterprises (SMEs) to backup database data access. To prove that the proposed architecture can support the high availability of the database, and can effectively avoid data loss in memory caused by failovers, this study applies the main test method of powering off the virtual machine and verified three cases on two commonly used databases MySQL and PostgreSQL: Case 1 proves that this study combines the database native disaster recovery mechanism to effectively achieve high availability of the database. Case 2 proves that it effectively controls the WAL (Write Ahead Log) of the PostgreSQL database and Redo log mechanism of the MySQL database, so that data correctness is maintained during failovers. Case 3 proves that it can analyze and control the timing of the database in writing data in the cache memory to the hard disk. This study also designed a failover process to avoid data loss during failovers due to no enough time to write the data in the cache memory back to the hard disk; and finally to realize the high-availability of the database management system architecture in a practical way.   Keywords: Database, DBMS, High Availability, Failover, DRBD

scholarly journals An Incremental Snapshot System using Smart Backup for Persistent Disks in Cloud

2021 ◽  
Author(s):  
Veena S ◽  
D.John Aravindhar ◽  
L. Sudha ◽  
K.B. Aruna
Keyword(s):  
Essential Feature ◽  
High Availability ◽  
Cloud Provider ◽  
Data Loss ◽  
End User ◽  
Online Systems ◽  
File Storage ◽  
Save Energy ◽  
Access To Data ◽  
The Cost

Abstract Periodical snapshots also called persistent disk snapshots are an essential feature associated with every cloud-hosted virtual instance, which minimizes the risk of unexpected data loss in the server and unavailability issues. The conventional method of creating a snapshot of a production-level server is done by temporarily disabling write access to data during the backup, either by stopping the accessing applications or by using the locking API provided by the operating system to enforce exclusive read access. This is not tolerable for high-availability always-online systems, in which service stoppages are not bearable. In order to solve this downtime issue in high-availability systems, the backup can be performed in a smarter way as incremental snapshots in which a read-only copy of the dataset frozen at a point in time is stored as snapshot by allowing applications to continue processing and writing their data to the instance. Also, incremental snapshots work in a way that only blocks which are different from the former snapshots are processed and stored in the subsequent one.This smart backup will reduce the overall space requirement of the snapshot system by storing only the differences in file storage blocks. Also, when implemented this will save energy and infrastructure requirements of the cloud provider as well as the cost and time of the end-user to create a low- latency server.

HALR: A TCP Enhancement Scheme Using Local Caching in High-Availability Cluster

2009 ◽  
Vol E92-B (1) ◽  
pp. 26-33
Author(s):  
Yi-Hsuan FENG ◽  
Nen-Fu HUANG ◽  
Yen-Min WU
Keyword(s):  
High Availability

Clustering Server Pada Cloud Computing Berbasis Proxmox VE Menggunakan Metode High Availability

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Linda Apriliana ◽  
Ucuk Darusala Darusalam ◽  
Novi Dian Nathasia
Keyword(s):  
Cloud Computing ◽  
File System ◽  
High Availability ◽  
Client Server ◽  
Network File System

Layanan dan data teknologi Cloud Computing tersimpan pada server, hal ini menjadikan faktor pentingnya server sebagai pendukung ketersediaan layanan. Semakin banyak pengguna yang mengakses layanan tersebut akan mengakibatkan beban kinerja mesin server menjadi lebih berat dan kurang optimal, karena layanan harus bekerja menyediakan data terus-menerus yang dapat diakses kapanpun oleh penggunanya melalui jaringan terkoneksi. Perangkat keras server memiliki masa performa kinerja. Hal serupa dengan perangkat lunak yang dapat mengalami crash. Dengan fungsi server yang memberikan layanan kepada client, server dituntut untuk memiliki tingkat availability yang tinggi. Hal tersebut memungkinkan mesin server mengalami down. Server juga harus dimatikan untuk keperluan pemeliharaan. Penelitian bertujuan ini membangun Clustering Server yang dapat bekerja bersama yang seolah merupakan sistem tunggal diatas lingkungan virtual. Hal ini merupakan solusi untuk mengatasi permasalahan tersebut. Pada penelitian ini penulis menggunakan server virtualisasi proxmox, FreeNAS sebagai server NAS dan DRBD untuk pendukung ketersediaan layanan tinggi dalam lingkup HA, sinkronisasi data dalam High Availability (HA) yang dapat melakukan mirroring sistem kemesin lain. Dengan diterapkannya metode HA dan sinkronasi DRBD serta penggunaan NFS (Network File System) pada sistem cluster didapatkan hasil rata-rata waktu migrasi sebesar 9.7(s) pada node1 menuju node2, 3.7(s) node2 menuju node3, dan 3(s) pada node3 menuju node1. Didaptkan juga waktu downtime yang lebih sedikit yaitu sebesar 0.58 ms pada node1, 0.02 ms pada node2, dan 0.02 ms pada node3.

Effectiveness of Nanoprober in Detecting Single/Multiple Bit Flash Data Gain & Data Loss Failures

2009 ◽  
Author(s):  
Rajesh Medikonduri
Keyword(s):  
Flash Memory ◽  
Data Loss ◽  
Voltage Contrast

Abstract This paper discusses the physics, definitions, and nanoprobing flow of a flash bit memory. In addition, a case study showing the effectiveness of nanoprobing in detecting the Single Bit Fail Data Gain and Data Loss in Flash Memory is also discussed. The paper also includes cases where no passive voltage contrast was observed at the SEM and no leakage was observed at AFM, yet the units failing SBF DG, SBF DL and depletion, were detected by nanoprobing of the single bit. The major finding of this paper is a way to resolve data gain, data loss, and depletion failures of flash memory by nanoprobing procedure, despite no PVC seen at the SEM and no leakage seen at the AFM.

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