Dynamic Distributed Database over Cloud Environment

Raouf, Ahmed E. Abdel; Badr, Nagwa L.; Tolba, Mohamed F.

doi:10.1007/978-3-319-13461-1_8

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…Secondly, this service allows the client to deploy the database and set all the required SLA specifications for the database. The Clustering Service is used to group the sites into disjoint clusters. Based on the literature survey, grouping sites into disjoint clusters is an efficient way to minimize the communication time required for query processing and data allocation 31‐33 . As a result, the clustering service is used to group the sites of the multi‐tenant database system into disjoint clusters and finally updates the catalog database with the recent location of each site in multi‐tenant database system.…”

Section: The Proposed Clustered‐based Multi‐tenant Database Management System (Cb‐mtdbms)mentioning

confidence: 99%

A predictive replication for multi‐tenant databases using deep learning

Raouf

Abo-Alian

Badr

2021

Concurrency and Computation

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The service level agreement (SLA) is an agreement between clients and the service provider, which defines the minimum performance and availability requirements for software. The service provider should have effective strategies for placement, migration, and replication of the tenants to reduce their operational costs, maximize the utilization of their hardware and software resources and accordingly meet the SLA requirements with slight SLA violations. In this research, a clustered‐based multi‐tenant database management system (CB‐MT DBMS) is proposed. Additionally, a dynamic proactive provisioning technique is built using three different prediction models: The Recursive Window Forecasting Autoregressive Integrated Moving Average (ARIMA) model, Exponential Moving Average (EMA) model, and the proposed Recurrent Neural Network (RNN) with Long Short‐Term Memory (LSTM) cells model. Various experimental scenarios with different datasets have been conducted to prove that the RNN model with LSTM cells is a promising solution in multi‐tenant environments, where the tenants have irregular workload patterns. Experimental results firstly show that the RNN model accuracy is superior to their counterparts (i.e., ARIMA and EMA models) when applied to multi‐tenant database workloads generated using TPC benchmarks, as it reduces the prediction error value which is computed using the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) metrics. Secondly, Experimental results prove that the RNN prediction model accuracy is superior to their counterparts for detecting SLA violation values and windows using different SLA values.

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Section: The Proposed Clustered‐based Multi‐tenant Database Management System (Cb‐mtdbms)mentioning

confidence: 99%

A predictive replication for multi‐tenant databases using deep learning

Raouf

Abo-Alian

Badr

2021

Concurrency and Computation

Self Cite

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“…There are many approaches to represent the use cases. 7,8 The first approach lists use cases along columns, then lists the relations along the rows, and ends up with the C R U D letters that are placed in the matrix cells on the basis of the usage of each use case corresponding to each relation. This is illustrated in Table 1.…”

Section: Generate Ecrud Matrixmentioning

confidence: 99%

An improved vertical fragmentation, allocation and replication for enhancing e‐learning in distributed database environment

Sathishkumar¹,

Gunasekaran²

2020

Computational Intelligence

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E-learning is the indispensable technique to educate huge number of people and students in short period of time with optimized usage of different kind of required resources. It is employed as a crucial teaching approach by almost all kind of educational institutions all around the world. Since e-learning involves significant amount of resource utilization and cost, it requires some essential methodology to enhance the current system of e-learning more efficient. The mere publication of the educational content in websites is not enough. It is very clear that, without applying suitable strategic models and concepts and establishing appropriate communication channels between contributors of e-learning system, the educational goals cannot be achieved as we desired. Distributed database involves greater contribution in the field of cloud based e-learning process. Basically, data replication is crucial decision of companies as database distribution can be achieved effectively by the method of database replication which generates the same copies of information called replicas. In this article, we analyze the supremacy of synergetic learning and concentrates on data replication's significance in cloud based learning system. Here we propose an excellent mechanism for data replication and enhancing the performance in terms optimized access

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“…The proposed scheme addresses the limitation of the previous vertical fragmentation solutions [40][41][42][43][44][45] as well as resulting in a significant reduction of total communication costs to execute DDBS queries. The second proposed fragmentation algorithm is an enhanced horizontal FAR technique, 4 which takes the horizontal FAR decisions at the initial stage of designing the distributed database.…”

Section: Add a New Database Modulementioning

confidence: 99%

Dynamic data reallocation and replication over a cloud environment

Raouf

Badr

Tolba

2018

Concurrency and Computation

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The performance and efficiency of the distributed database system (DDBS) design are dependent on proper data fragmentation, reallocation, and replication of global relations. In this research, a Cluster Based Distributed and Parallel Database System (CB-DDBS) architecture over a cloud environment is proposed. The proposed CB-DDBS architecture processes the client's query requests, which access the clustered DDBS from anywhere. It also allows vertical and horizontal fragmentation, allocation, and replication decisions to be taken statically at the initial stage of the design. In addition, it allows migration and/or replication decisions to be taken by each cluster independently of other clusters using the proposed Optimal Fragment Reallocation and Replication (OFRAR) Algorithm. The proposed CB-DDBS architecture and the proposed OFRAR Algorithm are tested in both Amazon cloud environment and a simulated environment. Experimental results show that the proposed OFRAR Algorithm efficiently reduces the communication costs for typical access patterns, the overloads of the sites, and the frequency and time spent on fragment migration over the network sites by imposing a stricter condition for fragment reallocation and replication, resulting in a great overall improvement in the DDBS performance. It also shows that the proposed CB-DDBS architecture results in a significant reduction of the execution time needed for the transactions and for noticing an error.

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Dynamic Distributed Database over Cloud Environment

Cited by 16 publications

References 17 publications

A predictive replication for multi‐tenant databases using deep learning

A predictive replication for multi‐tenant databases using deep learning

An improved vertical fragmentation, allocation and replication for enhancing e‐learning in distributed database environment

Dynamic data reallocation and replication over a cloud environment

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