With the rapid adoption of multi-tenant databases, the cloud provider consolidates multiple tenants’ database on server machines, where the tenants share a common application and database instances. To ensure the quality of service (QoS) for the leased resources, both sides (i.e., the user and the provider) create a Service Level Agreement (SLA). Higher SLA violations result in high SLA contractual penalties and increase the possibility of losing the tenant. In addition, the unusual workload patterns of each tenant transactions require seamless adjustments due to the sudden burden changes and variability. As a result, to satisfy simultaneously availability and performance tenant requirements, it is necessary to perform reliable tenant migration and replication to distribute the workload to a flexible set of sites and avoid SLA violations. In this research, a cluster-based multi-tenant database management system (CB-MT DBMS) is proposed, which takes the migration and replication decisions in advance by monitoring and acting before the violation of the SLA occurs. In addition, a dynamic proactive multi-tenant database migration and replication MTDB-MR algorithm is proposed to reduce collisions and inconsistencies between migration and replication decisions for a group of violated tenants. Experimental results show that the proposed MTDB-MR algorithm is the ideal candidate for migration and replication of the violated multi-tenant databases, as it minimizes the total number of SLA violations, the number of multi-tenant clients SLA violations, client sites average response time and total execution time of each multi-tenant client site as compared to the previous algorithms.

A Predictive Multi-Tenant Database Migration and Replication in the Cloud Environment

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.

A predictive replication for multi‐tenant databases using deep learning

A Multi-tenant architecture is a cost optimization for Software as a Service, where customers, also referred to as tenants are consolidated into the same server. The main idea of this architecture ...

Analysis and Performance Evaluation of the Data-layer in a Multi-tenant Architecture

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Analysis and Performance Evaluation of the Data-layer in a Multi-tenant Architecture