Replication and Uploading Data on Cloud for Disaster Recovery Process

2021 ◽  
Vol 12 (2) ◽  
pp. 1387-1395
Author(s):  
K. S. Sakunthala Prabha, Et. al.
Keyword(s):  
Disaster Recovery ◽  
Input Data ◽  
Service Providers ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cloud Service ◽  
Recovery Process ◽  
Optimal Location ◽  
Data Loss ◽  
Replication Process

Disaster recovery is a diligent issue in IT business. This issue is progressively significant in cloud computing, since Cloud Service Providers (CSPs) are bound to provide all facilities to their clients regardless of whether the server farm is down, because of a disaster. During the disaster, the data may be lost. To overcome this problem, replication is generated for each input data. The main objective of this paper is to upload different data on optimal location of cloud. The proposed system consists of three modules, namely, replica generation; choose optimal location and recovery process. Initially, to avoid the data loss, the input data are replicated. After replication process, the data are stored on cloud with the help of oppositional gravitational search algorithm (OGSA) which then retrieves only the request based data. Hence, we could avoid the data loss due to disaster. The presentation of proposed methodology is analyzed by different metrics comparing with various methods.

scholarly journals Study on Disaster Recovery in Cloud Environment

2018 ◽  
Vol 7 (2.32) ◽  
pp. 100
Author(s):  
Dr K.Ravindranath ◽  
N Raghupriya ◽  
P Krishna Vamsi ◽  
D Sharath Kumar
Keyword(s):  
Private Information ◽  
Disaster Recovery ◽  
Service Providers ◽  
Cloud Service ◽  
Data Recovery ◽  
Need For Recovery ◽  
Data Services ◽  
World Information ◽  
Gathering Data ◽  
Open Issues

In Today's world information been produced in huge sum, which requires data recovery assistance. The cloud service providers give security to the client  regardless  of  the  possibility  that systems are down, because of disaster. A lot of private information is produced which is put away in cloud. In this manner, the need for recovery of data services are developing in an order and needs an advancement of an well-organized powerful data rescue strategies, when  information is lost in a disaster. The motivation behind recovery strategy to support client from gathering data from any alternate server whenever that server lost information and incapable to provide information to the client. On the way to accomplish the reason, numerous diverse procedures have been proposed. In circumstances like Flood, Fire, seismic tremors or any equipment glitch or any accidental deletion of information may never again remain accessible. The target of this recovery is to condense the intense data recovery procedures that are utilized as a part of cloud computing area. It additionally describes the cloud-based disaster recovery stages and recognize open issues identified with disaster recovery. 

A Multi-objective Hybrid Heuristic Approach for Optimal Setting of FACTS Devices in Power System Operation

2016 ◽  
Vol 5 (3) ◽  
pp. 120
Author(s):  
Sai Ram Inkollu ◽  
Venkata Reddy Kota
Keyword(s):  
Power System ◽  
Power Flow ◽  
Transmission Loss ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Test System ◽  
Optimal Location ◽  
Voltage Profile ◽  
Loss Minimization ◽  
Facts Devices

<p>Improvement of power system performance in terms of increased voltage profile and decreased transmission loss is becoming one of the challenging tasks to the system operators under open access environment. Apart from traditional power flow controlling devices, use of Flexible AC Transmission System (FACTS) devices can give an attractive solution for the operation and control of deregulated power system. The type, size, location and number of FACTS devices are to be optimized appropriately in order to get the targeted benefits. In this paper, two FACTS devices, Thyristor Controller Phase Shift Transformer (TCPST) and Interline Power Flow Controller (IPFC) are selected to obtain the required performance such as improvement of voltage profile and loss minimization. To search the optimal location and optimal rating of the selected FACTS devices, a hybrid algorithm which formulated with Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) is proposed. At the first step, the optimization problem is solved for finding the optimal location of FACTS devices using PSO with an objective of voltage profile maximization and later GSA is implemented to optimize their parameters with an objective of transmission loss minimization. The proposed method is implemented on IEEE 30-bus test system and from the simulation results it can be proved that this technique is well suited for real-time application.  </p><p align="center"><strong><br /></strong></p>

Minimization of losses and FACTS installation cost using proposed differential gravitational search algorithm optimization technique

2016 ◽  
Vol 23 (2) ◽  
pp. 235-251
Author(s):  
SN Deepa ◽  
J Rizwana
Keyword(s):  
Power System ◽  
Power Flow ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Optimal Location ◽  
Optimization Techniques ◽  
System Stability ◽  
Gravitational Search ◽  
Installation Cost

The optimal location of Flexible AC Transmission Systems (FACTS) controllers in a multi-machine power system using proposed differential gravitational search algorithm (DGSA) optimization method is proposed in this paper. The main objective of this paper is to employ DGSA optimization technique to solve optimal power flow problem in the presence of Unified Power Flow controller for improving voltage profile by reducing losses along with the installation cost thereby enhancing the power system stability. A differential operator is incorporated into the gravitational search algorithm for effective search of the better solution. Due to this, the convergence and accuracy will be faster. The IEEE-6 bus, IEEE-14 bus and IEEE-30 bus systems are tested along with three other optimization techniques to validate the effectiveness of this proposed method. This proposed algorithm presents an optimal location of FACTS devices in transmission lines.

Data Partitioning and Reduplication for Providing Integrity to Data in the Cloud

2020 ◽  
Vol 17 (9) ◽  
pp. 4070-4074
Author(s):  
H. M. Nishkala ◽  
S. H. Anu ◽  
D. A. Bindushree ◽  
S. L. Manoj
Keyword(s):  
Data Security ◽  
Privacy Protection ◽  
Service Providers ◽  
Cloud Service ◽  
Major Elements ◽  
Data Partitioning ◽  
Security And Privacy ◽  
Data Loss ◽  
Cloud Data ◽  
Cloud Service Providers

Cloud Computing is a boon to the field of information and technology. The two major elements of client worries are Data security and Privacy Protection. Data may be revised and improved when client stores the information in the cloud so there might be danger of data loss. Therefore client information is moved to the data hub which cannot be controlled by the clients. Hence high safety efforts are required to secure data inside the cloud. Here data is divided into fragments and they are converted into encrypted file. This encrypted file is issued to arbitrarily chosen cloud service providers by the cloud data owners. Even after the successful attack, attackers do not get the meaning full information. If cloud data clients access to get any document that relating to encrypted file is regenerated from the fragments and clients must download it. When the applicant coordinates the strategy with the original details, then only file can be decoded. Therefore it demonstrates that prospective strategy improves the data integrity and confidentiality.

scholarly journals User-Centric Cyber Disaster Recovery as a Service

2019 ◽  
Vol 1 ◽  
pp. 238-246
Author(s):  
U Karim ◽  
H C Inyiama ◽  
R Karim
Keyword(s):  
Disaster Recovery ◽  
Performance Metrics ◽  
Service Providers ◽  
System Development ◽  
Cloud Service ◽  
Service Users ◽  
Analysis And Design ◽  
Data Backup ◽  
Cost Constraints ◽  
User Centric

In a world of interdependent economies and online transactions, a large volume of data hosted on the cyberspace a daily bases. Cyber threats and attacks are steadily increasing. Most time, these threats and attacks are targeted at service providers but service users are greatly affected by the attacks due to their vulnerability level. When disasters knockdown the infrastructures of a single service provider, it will have ripple effects on thousands of innocent service users. Therefore, service users need more than ever to prepare for major crises targeted at their service providers. To cope with this trends, every service user requires an independent business continuity plan (BCP) or disaster recovery plan (DRP) and data backup policy which falls within their cost constraints while achieving the target recovery requirements in terms of recovery time objective (RTO) and recovery point objective (RPO). The aim of this paper is to develop a model for a user-centric disaster recovery system to enable service users to independently develop their data backup policies that best suits their remote databases, and host same as a cloud service deployable on public cloud for users to subscribe to and be billed on pay-as-you-go billing model. The system developed is highly compatible with MYSQL, MSSQL and Oracle databases. A combination of Dynamic System Development Methodology (DSDM) and Object- Oriented Analysis and Design Methodology (OOADM) were used to design the system while Java Enterprise Edition (JEE) is used to develop the system. The encryption and compression mechanisms of the system were tested with various sizes of backup files ranging from 64 Kb to 20Mb and several performance metrics such as (1) Encryption time; (2) Compression size; (3) CPU clock cycles and battery power are compared and analysed with some well-known encryption and compression algorithms.

scholarly journals Disaster Recovery in Cloud Computing: A Survey

2014 ◽  
Vol 7 (4) ◽  
pp. 39 ◽  
Author(s):  
Mohammad Ali Khoshkholghi ◽  
Azizol Abdullah ◽  
Rohaya Latip ◽  
Shamala Subramaniam ◽  
Mohamed Othman
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Disaster Recovery ◽  
Service Providers ◽  
Cloud Service ◽  
Persistent Problem ◽  
The Past ◽  
Recovery Mechanisms ◽  
Cloud Environments ◽  
Open Issues

Disaster recovery is a persistent problem in IT platforms. This problem is more crucial in cloud computing, because Cloud Service Providers (CSPs) have to provide the services to their customers even if the data center is down, due to a disaster. In the past few years, researchers have shown interest to disaster recovery using cloud computing, and a considerable amount of literature has been published in this area. However, to the best of our knowledge, there is a lack of precise survey for detailed analysis of cloud-based disaster recovery. To fill this gap, this paper provides an extensive survey of disaster recovery concepts and research in the cloud environments. We present different taxonomy of disaster recovery mechanisms, main challenges and proposed solutions. We also describe the cloud-based disaster recovery platforms and identify open issues related to disaster recovery.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&amp;rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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