A Data Allocation Strategy Algorithm for Large Databases Based on Genetic Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 898-903
Author(s):  
Xiao Feng Li ◽  
Yuan Xin Tang ◽  
Cui Cui Gong
Keyword(s):  
Computer Network ◽  
Great Influence ◽  
Distributed Database ◽  
Database System ◽  
Data Availability ◽  
Data Allocation ◽  
Large Databases ◽  
System Improvement ◽  
Distributed Database System ◽  
Allocation Strategies

The distributed database system is the product that the database system combines with the computer network system. The data distribution problem has great influence on distributed database application system improvement, data availability, the efficiency and reliability of the distributed database. The allocation strategies in this paper have used some excellent properties in genetic algorithms, including higher parallelism and robustness, the realization of standard way, and to maintain good balance between the depth prior search and breadth prior search, etc, so the allocation strategies in this article's have high execution efficiency, with stronger ability in seeking the best global solution and easy to realize.

Database Sharding

2015 ◽  
Vol 5 (2) ◽  
pp. 36-52 ◽  
Author(s):  
Sikha Bagui ◽  
Loi Tang Nguyen
Keyword(s):  
Big Data ◽  
Fault Tolerance ◽  
Distributed Database ◽  
Database System ◽  
High Availability ◽  
Effective Management ◽  
Large Databases ◽  
Distributed Database System ◽  
Horizontal Partitioning

In this paper, the authors present an architecture and implementation of a distributed database system using sharding to provide high availability, fault-tolerance, and scalability of large databases in the cloud. Sharding, or horizontal partitioning, is used to disperse the data among the data nodes located on commodity servers for effective management of big data on the cloud.

Algorithm Development, Simulation Analysis and Parametric Studies for Data Allocation in Distributed Database Systems

2002 ◽  
pp. 157-189
Author(s):  
Amita Goyal Chin
Keyword(s):  
Simulation Analysis ◽  
Database Systems ◽  
Distributed Database ◽  
Database System ◽  
Data Allocation ◽  
Distributed Database Systems ◽  
Data Reorganization ◽  
Incremental Growth ◽  
Efficient Data ◽  
Distributed Database System

In a distributed database system, an increase in workload typically necessitates the installation of additional database servers followed by the implementation of expensive data reorganization strategies. We present the Partial REALLOCATE and Full REALLOCATE heuristics for efficient data reallocation. Complexity is controlled and cost minimized by allowing only incremental introduction of servers into the distributed database system. Using first simple examples and then, a simulator, our framework for incremental growth and data reallocation in distributed database systems is shown to produce near optimal solutions when compared with exhaustive methods.

scholarly journals Nonreplicated Static Data Allocation in Distributed Databases Using Biogeography-Based Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Arjan Singh ◽  
Karanjeet Singh Kahlon ◽  
Rajinder Singh Virk
Keyword(s):  
Data Transfer ◽  
Distributed Databases ◽  
Distributed Database ◽  
Database System ◽  
Data Allocation ◽  
Transmission Cost ◽  
Static Data ◽  
Total Data ◽  
Transfer Cost ◽  
Distributed Database System

Allocation of data is one of the key design issues of distributed database. A major cost of query execution in a distributed database system is the data transfer cost from one site to another site. The allocation of fragments among the different sites over the network plays an important role in performance of the distributed database system. The main objective of a data allocation in distributed database is to place the data fragments at different sites in such a way, so that the total data transfer cost can be minimized while executing a set of queries. In this paper, a new biogeography-based optimization (BBO) algorithm has been used to allocate the fragments during the design of distributed database system. The goal of this paper is to design a fragments allocation algorithm, so that the total data transmission cost can be minimized. To show the performance of proposed algorithm, results of biogeography-based optimization algorithm for data allocation are compared with genetic algorithm.

Incremental Data Allocation and Reallocation in Distributed Database Systems

2011 ◽  
pp. 137-160 ◽  
Author(s):  
Amita Goyal Chin
Keyword(s):  
Database Systems ◽  
Distributed Database ◽  
Database System ◽  
Data Allocation ◽  
Distributed Database Systems ◽  
Data Reorganization ◽  
Incremental Growth ◽  
Efficient Data ◽  
Distributed Database System ◽  
Database Servers

In a distributed database system, an increase in workload typically necessitates the installation of additional database servers followed by the implementation of expensive data reorganization strategies. We present the Partial REALLOCATE and Full REALLOCATE heuristics for efficient data reallocation. Complexity is controlled and cost minimized by allowing only incremental introduction of servers into the distributed database system. Using first simple examples and then, a simulator, our framework for incremental growth and data reallocation in distributed database systems is shown to produce near optimal solutions when compared with exhaustive methods.

scholarly journals Implementasi Heterogenous Distributed Database System Oracle Xe 10g dan MySQL Rekam Medis Poliklinik UIN Sunan Kalijaga

2016 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Valdi Adrian Abrar ◽  
Moh Didik R. Wahyudi
Keyword(s):  
Information Systems ◽  
Medical Records ◽  
Distributed Database ◽  
Database System ◽  
Data Availability ◽  
Data Synchronization ◽  
Multiple Servers ◽  
Distributed Database System ◽  
Medical Records System ◽  
Automatic Synchronization

Infrastruktur yang biasa digunakan oleh sistem informasi yang ada di Indonesia kebanyakan mempunyai model yang terpusat. Sehingga, jika terjadi masalah pada server seperti server mati atau terjadi kerusakan pada basis data, maka sistem informasi tidak dapat digunakan sampai masalah pada server tersebut teratasi. Untuk mengatasi hal tersebut, sistem replikasi atau duplikasi data pada sistem basis data terdistribusi diharapkan dapat meminimalisir kehilangan data rekam medis sehingga walaupun ada server yang mengalami masalah, maka data tidak akan hilang. Dalam konteks sistem rekam medis di poliklinik UIN Sunan Kalijaga, hal ini bisa menjadi solusi untuk memenuhi aspek ketersediaan data. Sinkronisasi data antara server utama dan replika dapat dilakukan secara otomatis maupun secara manual. Sinkronisasi otomatis dilakukan dengan cara menjalankan baris program secara otomatis dan berkala dengan aturan tertentu. Sinkronisasi manual dijalankan oleh operator dengan menjalankan suatu perintah. Berdasarkan hasil dan pembahasan, diperoleh kesimpulan bahwa implementasi Heterogenous Distributed Database System pada sistem informasi poliklinik dapat mengatasi masalah jika terjadi pada beberapa server dengan cara mengolah dan mendisribusikan data pada server lain yang aktif. Proses replikasi dan sinkronisasi data rekam medis yang dilakukan, ternyata dapat meminimalisir kehilangan data.Infrastructure used by the existing information systems in Indonesia, mostly have a centralized model. Thus, if a problem occurs on the server as the server is dead or there is damage to the database, the system information can not be used until the problem is solved on the server. To overcome this, the system replication or duplication of data in a distributed database system is expected to minimize the loss of medical records so that even if there is a server that has the problem, then the data will not be lost. In the context of medical records system at the clinic UIN Sunan Kalijaga, this could be a solution to meet aspects of data availability. The data synchronization between the primary and replica servers can be done automatically or manually. Automatic synchronization is done by running the program line automatically and periodically with certain rules. Manual synchronization is run by an operator to execute a command. Based on the results and discussion, we concluded that the implementation of heterogenous Distributed Database System on clinic information systems can solve the problem if it occurs on multiple servers by processing and mendisribusikan data on another server that is active. The process of replication and synchronization of medical records that do, it can minimize data loss.

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