scholarly journals Multiobjective Glowworm Swarm Optimization-Based Dynamic Replication Algorithm for Real-Time Distributed Databases

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Saadi Hamad Thalij ◽  
Veli Hakkoymaz
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Distributed Databases ◽  
Data Replication ◽  
Data Availability ◽  
Access Time ◽  
Swarm Optimization ◽  
Dynamic Data ◽  
Glowworm Swarm Optimization ◽  
Dynamic Data Replication

Distributed systems offer resources to be accessed geographically for large-scale data requests of different users. In many cases, replication of the vital data files and storing their replica in multiple locations accessible to the requesting clients is vital in improving the data availability, reliability, security, and reduction of the execution time. It is important that real-time distributed databases maintain the consistency constraints and also guarantee the time constraints required by the client requests. However, when the size of the distributed system increases, the user access time also tends to increase, which in turn increases the vitality of the replica placement. Thus, the primary issues that emerge are deciding upon an optimal replication number and identifying perfect locations to store the replicated data. These open challenges have been considered in this study, which turns to develop a dynamic data replication algorithm for real-time distributed databases using a multiobjective glowworm swarm optimization (MGSO) strategy. The proposed algorithm adapts the random patterns of the read-write requests and employs a dynamic window mechanism for replication. It also models the replica number and placement problem as a multiobjective optimization problem and utilizes MGSO for resolving it. The cost models are presented to ensure the time constraint satisfaction in servicing user requests. The performance of the MGSO dynamic data replication algorithm has been studied using competitive analysis, and the results show the efficiency of the proposed algorithm for the distributed databases.

scholarly journals A four-phase data replication algorithm for data grid

2015 ◽  
Vol 4 (1) ◽  
pp. 163 ◽  
Author(s):  
Alireza Saleh ◽  
Reza Javidan ◽  
Mohammad Taghi FatehiKhajeh
Keyword(s):  
Large Scale ◽  
Data Replication ◽  
Data Grid ◽  
Data Availability ◽  
Access Time ◽  
File Transfer ◽  
System Availability ◽  
Large Scale Data ◽  
Effective Network ◽  
Average Access Time

<p>Nowadays, scientific applications generate a huge amount of data in terabytes or petabytes. Data grids currently proposed solutions to large scale data management problems including efficient file transfer and replication. Data is typically replicated in a Data Grid to improve the job response time and data availability. A reasonable number and right locations for replicas has become a challenge in the Data Grid. In this paper, a four-phase dynamic data replication algorithm based on Temporal and Geographical locality is proposed. It includes: 1) evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 2) analyzing and modeling the relationship between system availability and the number of replicas, and calculating a suitable number of new replicas; 3) evaluating and identifying the popular data in each site, and placing replicas among them; 4) removing files with least cost of average access time when encountering insufficient space for replication. The algorithm was tested using a grid simulator, OptorSim developed by European Data Grid Projects. The simulation results show that the proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, effective network usage and percentage of storage filled.</p>

scholarly journals State Management for Cloud-Native Applications

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 423
Author(s):  
Márk Szalay ◽  
Péter Mátray ◽  
László Toka
Keyword(s):  
Large Scale ◽  
Distributed Databases ◽  
Access Time ◽  
Replica Placement ◽  
State Management ◽  
Placement Decisions ◽  
Dynamic Replication ◽  
Cloud Databases ◽  
Placement Algorithm ◽  
The One

The stateless cloud-native design improves the elasticity and reliability of applications running in the cloud. The design decouples the life-cycle of application states from that of application instances; states are written to and read from cloud databases, and deployed close to the application code to ensure low latency bounds on state access. However, the scalability of applications brings the well-known limitations of distributed databases, in which the states are stored. In this paper, we propose a full-fledged state layer that supports the stateless cloud application design. In order to minimize the inter-host communication due to state externalization, we propose, on the one hand, a system design jointly with a data placement algorithm that places functions’ states across the hosts of a data center. On the other hand, we design a dynamic replication module that decides the proper number of copies for each state to ensure a sweet spot in short state-access time and low network traffic. We evaluate the proposed methods across realistic scenarios. We show that our solution yields state-access delays close to the optimal, and ensures fast replica placement decisions in large-scale settings.

scholarly journals A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 40240-40254
Author(s):  
Ahmed Awad ◽  
Rashed Salem ◽  
Hatem Abdelkader ◽  
Mustafa Abdul Salam
Keyword(s):  
Data Replication ◽  
Cloud Environment ◽  
Dynamic Data ◽  
Dynamic Data Replication ◽  
Intelligent Approach

An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

2021 ◽  
pp. 101-107
Author(s):  
Mohammad Alshehri ◽  
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Initial Position ◽  
Swarm Optimization ◽  
The Real ◽  
Assisted Navigation ◽  
Glowworm Swarm Optimization ◽  
Tracking Process ◽  
Wide Range ◽  
Localization And Tracking

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.

Dynamic data replication in LCG 2008

2008 ◽  
Vol 20 (11) ◽  
pp. 1259-1271 ◽  
Author(s):  
C. Nicholson ◽  
D. G. Cameron ◽  
A. T. Doyle ◽  
A. P. Millar ◽  
K. Stockinger
Keyword(s):  
Data Replication ◽  
Dynamic Data ◽  
Dynamic Data Replication
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