Dynamic Distributed Database over Cloud Environment

2014 ◽  
pp. 67-76 ◽  
Author(s):  
Ahmed E. Abdel Raouf ◽  
Nagwa L. Badr ◽  
Mohamed Fahmy Tolba
Keyword(s):  
Distributed Database ◽  
Cloud Environment

scholarly journals Advanced consistency management of highly-distributed transactional database in a hybrid cloud environment using novel R-TBC/RTA approach

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jasmina Dizdarevic ◽  
Zikrija Avdagic ◽  
Fahrudin Orucevic ◽  
Samir Omanovic
Keyword(s):  
Distributed Database ◽  
Database Management System ◽  
Multidimensional Data ◽  
Hybrid Cloud ◽  
Cloud Environment ◽  
Bayesian Hierarchical ◽  
Consistency Management ◽  
Model Research ◽  
Sufficient Degree ◽  
Novel Strategy

AbstractThis paper examines possibilities for improving the existing strategies of consistency management for highly-distributed transactional database in a hybrid cloud environment. With a detailed analysis of the existing consistency models for distributed database and standard strategies including Classic, Quorum and Tree Based Consistency (TBC), it is concluded that an improved advanced model of so-called visible adaptive consistency needs to be applied in a highly-distributed cloud environment, as necessary and sufficient degree of synchronization of all replicas. Along with the proposed model, research and development of an advanced novel strategy for consistency management Rose TBC (R-TBC) approach has been conducted, by improving standard TBC approach. Regarding implementation, a specific agglomerative Rose Tree Algorithm (RTA) has been developed, based on Bayesian hierarchical clustering and Graph Partitioning Algorithm - Multidimensional Data Clustering (GPA-MDC) intelligent partitioning of transactional Cloud Database Management System (CDBMS). The final result is constructed R-TBC model that changes in accordance with dynamic changes of entire heterogeneous CDBMS environment.

EFFECTIVE AND EFFICIENT WAY OF REDUCE DEPENDENCY ON DATASET WITH THE HELP OF MAPREDUCE ON BIG DATA

2015 ◽  
Vol 3 (6) ◽  
pp. 401
Author(s):  
Satish Londhe ◽  
Smita Mahajan
Keyword(s):  
Big Data ◽  
Performance Analysis ◽  
Performing Arts ◽  
High Performance ◽  
Research Work ◽  
Distributed Database ◽  
Cloud Environment ◽  
Information Index ◽  
Fast Development ◽  
Index Building

With the fast development of networks these days organizations has overflowing with the collection of millions of data with big number of combination. This big data challenges over trade troubles. It requires more analysis for the high-performance procedure. The new method of hadoop and MapReduce methods are discussed starting the data mining standpoint. In the proposed research work we have to progress performance through parallelization of different operations such as loading the information, index building and evaluating the queries. Thus the performance analysis is completed with the minimum of three nodes with in the Amazon cloud environment. Hbase is a open source, non-relational and distributed database model. It executes on the pinnacle of Hadoop. It consists of a single key with multiple values. Looping is avoid in retrieving a meticulous data from huge datasets and it consume less amount of time for execute the data. HDFS file system is used to store the data after performing arts the map reduces operations and the execution time is decreased when the amount of nodes gets increased. The performance analysis is tuned with the parameters such as the carrying out complexity.

Overview of Replication Techniques on Distributed Database in Cloud Environment

2017 ◽  
Vol 23 (11) ◽  
pp. 11105-11108 ◽  
Author(s):  
Sharifah Hafizah Sy Ahmad Ubaidillah ◽  
A Noraziah
Keyword(s):  
Distributed Database ◽  
Cloud Environment

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

Selection of the Rational Variant for Construction of a Specialized Distributed Database in a Fuzzy Multicriterion Environment

2006 ◽  
Vol 65 (3) ◽  
pp. 261-269
Author(s):  
A. A. Burushkin ◽  
V. G. Gerasimenko ◽  
S. A. Golovin ◽  
S. V. Zhilinskii
Keyword(s):  
Distributed Database ◽  
Selection Of

A Review on Load Balancing Model Using Best Partition Technique

2017 ◽  
Vol 7 (8) ◽  
pp. 284
Author(s):  
M. Chaitanya ◽  
K. Durga Charan
Keyword(s):  
Cloud Computing ◽  
Fault Tolerance ◽  
Load Balancing ◽  
Load Balance ◽  
Large Impact ◽  
Cloud Environment ◽  
Public Cloud ◽  
The Public ◽  
Partition Technique ◽  
Textual Content

Load balancing makes cloud computing greater knowledgeable and could increase client pleasure. At reward cloud computing is among the all most systems which offer garage of expertise in very lowers charge and available all the time over the net. However, it has extra vital hassle like security, load administration and fault tolerance. Load balancing inside the cloud computing surroundings has a large impact at the presentation. The set of regulations relates the sport idea to the load balancing manner to amplify the abilties in the public cloud environment. This textual content pronounces an extended load balance mannequin for the majority cloud concentrated on the cloud segregating proposal with a swap mechanism to select specific strategies for great occasions.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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