Concurrency control and data replication strategies for large-scale and wide-distributed databases

2001 ◽  
Author(s):  
J. Tao ◽  
J.G. Williams
Keyword(s):  
Concurrency Control ◽  
Large Scale ◽  
Distributed Databases ◽  
Data Replication

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 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 Concurrency control in a system for distributed databases (SDD-1)

1980 ◽  
Vol 5 (1) ◽  
pp. 18-51 ◽  
Author(s):  
Philip A. Bernstein ◽  
David W. Shipman ◽  
James B. Rothnie
Keyword(s):  
Concurrency Control ◽  
Distributed Databases

Towards a New Data Replication Management in Cloud Systems

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20
Author(s):  
Abdenour Lazeb ◽  
Riad Mokadem ◽  
Ghalem Belalem
Keyword(s):  
Large Scale ◽  
Data Replication ◽  
Cloud Provider ◽  
Large Scale Data ◽  
Dynamic Replication ◽  
Replication Strategy ◽  
Multiple Copies ◽  
Cloud Environments ◽  
Real Challenge ◽  
Scale Data

Applications produce huge volumes of data that are distributed on remote and heterogeneous sites. This generates problems related to access and sharing data. As a result, managing data in large-scale environments is a real challenge. In this context, large-scale data management systems often use data replication, a well-known technique that treats generated problems by storing multiple copies of data, called replicas, across multiple nodes. Most of the replication strategies in these environments are difficult to adapt to cloud environments. They aim to achieve the best performance of the system without meeting the important objectives of the cloud provider. This article proposes a new dynamic replication strategy. The proposed algorithm significantly improves provider gain without neglecting customer satisfaction.

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