The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform

K. M. Annervaz - One of the best experts on this subject based on the ideXlab platform.

  • Multi-site data distribution for disaster Recovery-A planning framework
    Future Generation Computer Systems, 2014
    Co-Authors: Shubhashis Sengupta, K. M. Annervaz
    Abstract:

    In this paper, we present DDP-DR: a Data Distribution Planner for Disaster Recovery. DDP-DR provides an optimal way of backing-up critical business data into data centers (DCs) across several Geographic locations. DDP-DR provides a plan for replication of backup data across potentially large number of data centers so that (i) the client data is recoverable in the event of catastrophic failure at one or more data centers (disaster Recovery) and, (ii) the client data is replicated and distributed in an optimal way taking into consideration major business criteria such as cost of storage, protection level against site failures, and other business and operational parameters like Recovery Point Objective (RPO), and Recovery time Objective (RTO). The planner uses Erasure Coding (EC) to divide and codify data chunks into fragments and distribute the fragments across DR sites or storage zones so that failure of one or more site / zone can be tolerated and data can be regenerated. We describe data distribution planning approaches for both single customer and multiple customer scenarios. We describe a fault-tolerant multi-cloud data backup scheme using erasure coding.The data is distributed using a plan driven by a multi-criteria optimization.The plan uses parameters like cost, replication level, recoverability Objective etc.Both single customer and multiple customer cases are tackled.Simulation results for the plans and sensitivity analyses are discussed.

  • GECON - Planning for optimal multi-site data distribution for disaster Recovery
    Economics of Grids Clouds Systems and Services, 2011
    Co-Authors: Shubhashis Sengupta, K. M. Annervaz
    Abstract:

    In this paper, we present DDP-DR: a Data Distribution Planner for Disaster Recovery. DDP-DR provides an optimal way of backing-up critical business data into data centres (DCs) across several Geographic locations. DDP-DR provides a plan for replication of backup data across potentially large number of data centres so that (i) the client data is recoverable in the event of catastrophic failure at one or more data centres (disaster Recovery) and, (ii) the client data is replicated and distributed in an optimal way taking into consideration major business criteria such as cost of storage, protection level against site failures, and other business and operational parameters like Recovery Point Objective (RPO), and Recovery time Objective (RTO). The planner uses Erasure Coding (EC) to divide and codify data chunks into fragments and distribute the fragments across DR sites or storage zones so that failure of one or more site /zone can be tolerated and data can be regenerated.

  • planning for optimal multi site data distribution for disaster Recovery
    Grid Economics and Business Models, 2011
    Co-Authors: Shubhashis Sengupta, K. M. Annervaz
    Abstract:

    In this paper, we present DDP-DR: a Data Distribution Planner for Disaster Recovery. DDP-DR provides an optimal way of backing-up critical business data into data centres (DCs) across several Geographic locations. DDP-DR provides a plan for replication of backup data across potentially large number of data centres so that (i) the client data is recoverable in the event of catastrophic failure at one or more data centres (disaster Recovery) and, (ii) the client data is replicated and distributed in an optimal way taking into consideration major business criteria such as cost of storage, protection level against site failures, and other business and operational parameters like Recovery Point Objective (RPO), and Recovery time Objective (RTO). The planner uses Erasure Coding (EC) to divide and codify data chunks into fragments and distribute the fragments across DR sites or storage zones so that failure of one or more site /zone can be tolerated and data can be regenerated.

Shubhashis Sengupta - One of the best experts on this subject based on the ideXlab platform.

  • Multi-site data distribution for disaster Recovery-A planning framework
    Future Generation Computer Systems, 2014
    Co-Authors: Shubhashis Sengupta, K. M. Annervaz
    Abstract:

    In this paper, we present DDP-DR: a Data Distribution Planner for Disaster Recovery. DDP-DR provides an optimal way of backing-up critical business data into data centers (DCs) across several Geographic locations. DDP-DR provides a plan for replication of backup data across potentially large number of data centers so that (i) the client data is recoverable in the event of catastrophic failure at one or more data centers (disaster Recovery) and, (ii) the client data is replicated and distributed in an optimal way taking into consideration major business criteria such as cost of storage, protection level against site failures, and other business and operational parameters like Recovery Point Objective (RPO), and Recovery time Objective (RTO). The planner uses Erasure Coding (EC) to divide and codify data chunks into fragments and distribute the fragments across DR sites or storage zones so that failure of one or more site / zone can be tolerated and data can be regenerated. We describe data distribution planning approaches for both single customer and multiple customer scenarios. We describe a fault-tolerant multi-cloud data backup scheme using erasure coding.The data is distributed using a plan driven by a multi-criteria optimization.The plan uses parameters like cost, replication level, recoverability Objective etc.Both single customer and multiple customer cases are tackled.Simulation results for the plans and sensitivity analyses are discussed.

  • GECON - Planning for optimal multi-site data distribution for disaster Recovery
    Economics of Grids Clouds Systems and Services, 2011
    Co-Authors: Shubhashis Sengupta, K. M. Annervaz
    Abstract:

    In this paper, we present DDP-DR: a Data Distribution Planner for Disaster Recovery. DDP-DR provides an optimal way of backing-up critical business data into data centres (DCs) across several Geographic locations. DDP-DR provides a plan for replication of backup data across potentially large number of data centres so that (i) the client data is recoverable in the event of catastrophic failure at one or more data centres (disaster Recovery) and, (ii) the client data is replicated and distributed in an optimal way taking into consideration major business criteria such as cost of storage, protection level against site failures, and other business and operational parameters like Recovery Point Objective (RPO), and Recovery time Objective (RTO). The planner uses Erasure Coding (EC) to divide and codify data chunks into fragments and distribute the fragments across DR sites or storage zones so that failure of one or more site /zone can be tolerated and data can be regenerated.

  • planning for optimal multi site data distribution for disaster Recovery
    Grid Economics and Business Models, 2011
    Co-Authors: Shubhashis Sengupta, K. M. Annervaz
    Abstract:

    In this paper, we present DDP-DR: a Data Distribution Planner for Disaster Recovery. DDP-DR provides an optimal way of backing-up critical business data into data centres (DCs) across several Geographic locations. DDP-DR provides a plan for replication of backup data across potentially large number of data centres so that (i) the client data is recoverable in the event of catastrophic failure at one or more data centres (disaster Recovery) and, (ii) the client data is replicated and distributed in an optimal way taking into consideration major business criteria such as cost of storage, protection level against site failures, and other business and operational parameters like Recovery Point Objective (RPO), and Recovery time Objective (RTO). The planner uses Erasure Coding (EC) to divide and codify data chunks into fragments and distribute the fragments across DR sites or storage zones so that failure of one or more site /zone can be tolerated and data can be regenerated.

Júlio Mendonça - One of the best experts on this subject based on the ideXlab platform.

  • Multiple-criteria Evaluation of Disaster Recovery Strategies Based on Stochastic Models
    2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020, 2020
    Co-Authors: Júlio Mendonça, Ermeson Andrade, Ricardo Lima, Julian Araujo
    Abstract:

    The consequences for a company losing its data or having its IT system disrupted are severe and can impact negatively on business operations. It can also cause customer dissatisfaction and subsequent revenue loss. In a competitive global market, companies have been adopting disaster Recovery (DR) strategies as an attempt to keep IT systems operational, prevent data loss, and ensure business continuity. However, there is not a single DR strategy that meets the requirements of every business (e.g., availability and cost). Besides, most of the time, these requirements are conflicting. Therefore, efficient and accurate analysis of DR strategies before its deployment is crucial to choose the best strategy that suits companies’ needs and budget. In this paper, we propose the adoption of a multiple-criteria decision-making (MCDM) method and stochastic models to evaluate and rank DR strategies for IT infrastructures. The stochastic models are used for quantitative assessing distinct DR strategies regarding five DR key-metrics: availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO), and cost. We also use an MCDM method to rank the strategies according to multiple criteria (e.g., availability maximization and costs minimization). A case study demonstrates the feasibility and usefulness of the proposed approach for finding the best DR strategies according to multiple criteria.

  • Evaluating and modelling solutions for disaster Recovery
    International Journal of Grid and Utility Computing, 2020
    Co-Authors: Júlio Mendonça, Ricardo Lima, Ermeson Andrade
    Abstract:

    Systems outages can have disastrous effects on businesses such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster Recovery (DR) solutions have been adopted by companies to minimise the effects of these outages. However, the selection of an optimal DR solution is difficult since there does not exist a single solution that suits the requirement of every company (e.g., availability and costs). In this paper, we propose an integrated model-experiment approach to evaluate DR solutions. We perform experiments in different real-world DR solutions and propose analytic models to evaluate these solutions regarding DR key-metrics: steady-state availability, Recovery time Objective (RTO), Recovery Point Objective (RPO), downtime, and costs. The results reveal that DR solutions can significantly improve availability and minimise costs. Also, a sensitivity analysis identifies the parameters that most affect the RPO and RTO of the DR adopted solutions.

  • DRCN - Multiple-criteria Evaluation of Disaster Recovery Strategies Based on Stochastic Models
    2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020, 2020
    Co-Authors: Júlio Mendonça, Julian Araujo, Ermeson Andrade, Ricardo Lima, Dong Seong Kim
    Abstract:

    The consequences for a company losing its data or having its IT system disrupted are severe and can impact negatively on business operations. It can also cause customer dissatisfaction and subsequent revenue loss. In a competitive global market, companies have been adopting disaster Recovery (DR) strategies as an attempt to keep IT systems operational, prevent data loss, and ensure business continuity. However, there is not a single DR strategy that meets the requirements of every business (e.g., availability and cost). Besides, most of the time, these requirements are conflicting. Therefore, efficient and accurate analysis of DR strategies before its deployment is crucial to choose the best strategy that suits companies’ needs and budget. In this paper, we propose the adoption of a multiple-criteria decision-making (MCDM) method and stochastic models to evaluate and rank DR strategies for IT infrastructures. The stochastic models are used for quantitative assessing distinct DR strategies regarding five DR key-metrics: availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO), and cost. We also use an MCDM method to rank the strategies according to multiple criteria (e.g., availability maximization and costs minimization). A case study demonstrates the feasibility and usefulness of the proposed approach for finding the best DR strategies according to multiple criteria.

  • ISCC - Evaluation of a Backup-as-a-Service Environment for Disaster Recovery
    2019 IEEE Symposium on Computers and Communications (ISCC), 2019
    Co-Authors: Júlio Mendonça, Ewerton Queiroz, Ricardo Lima, Ermeson Andrade
    Abstract:

    Systems unavailability may produce severe consequences for modern business such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster Recovery (DR) solutions have been adopted by many organizations as an attempt to prevent data loss and ensure business continuity. With the cloud computing expansion, different cloud providers have been offering low-cost solutions for DR purposes such as the Backup-as-a-service (BaaS) for consumers. Therefore, in this paper, we present an integrated model-experiment approach to evaluate a BaaS environment for DR purposes. We use analytic models and fault-injection experiments to evaluate DR keymetrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO) in a real-world BaaS environment. The results revealed that the environment availability can vary according to the amount of data to backed up and restored. Besides, a sensitivity analysis shows that the RTO and RPO are mainly influenced by the the mean time to recover from a disaster and the backup interval, respectively.

  • SMC - Evaluating Database Replication Mechanisms for Disaster Recovery in Cloud Environments
    2019 IEEE International Conference on Systems Man and Cybernetics (SMC), 2019
    Co-Authors: Júlio Mendonça, Ermeson Andrade, Wilson Medeiros, Ronierison Maciel, Paulo Maciel, Ricardo Lima
    Abstract:

    Relational databases are the most popular database system worldwide. The occurrence of failures in these systems may produce severe consequences for the business, such as data loss, customer dissatisfaction, and subsequent revenue loss. Consequently, many organizations have adopted disaster Recovery (DR) solutions as an attempt to prevent data loss and ensure business continuity. Data replication for databases is one of the most used DR solution employed to guarantee data safety and availability. However, the analysis regarding DR aspects has been less explored. Therefore, in this paper, we present an integrated model-experiment approach to evaluate replication mechanisms in relational databases for DR purposes. We performed experiments in a geo-distributed cloud environment and developed analytic models to evaluate DR key-metrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO). The results revealed that the adoption of replication mechanisms could increase the system’s availability significantly. It also revealed that the replication mechanisms can guarantee RPO and RTO within seconds.

Ricardo Lima - One of the best experts on this subject based on the ideXlab platform.

  • Multiple-criteria Evaluation of Disaster Recovery Strategies Based on Stochastic Models
    2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020, 2020
    Co-Authors: Júlio Mendonça, Ermeson Andrade, Ricardo Lima, Julian Araujo
    Abstract:

    The consequences for a company losing its data or having its IT system disrupted are severe and can impact negatively on business operations. It can also cause customer dissatisfaction and subsequent revenue loss. In a competitive global market, companies have been adopting disaster Recovery (DR) strategies as an attempt to keep IT systems operational, prevent data loss, and ensure business continuity. However, there is not a single DR strategy that meets the requirements of every business (e.g., availability and cost). Besides, most of the time, these requirements are conflicting. Therefore, efficient and accurate analysis of DR strategies before its deployment is crucial to choose the best strategy that suits companies’ needs and budget. In this paper, we propose the adoption of a multiple-criteria decision-making (MCDM) method and stochastic models to evaluate and rank DR strategies for IT infrastructures. The stochastic models are used for quantitative assessing distinct DR strategies regarding five DR key-metrics: availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO), and cost. We also use an MCDM method to rank the strategies according to multiple criteria (e.g., availability maximization and costs minimization). A case study demonstrates the feasibility and usefulness of the proposed approach for finding the best DR strategies according to multiple criteria.

  • Evaluating and modelling solutions for disaster Recovery
    International Journal of Grid and Utility Computing, 2020
    Co-Authors: Júlio Mendonça, Ricardo Lima, Ermeson Andrade
    Abstract:

    Systems outages can have disastrous effects on businesses such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster Recovery (DR) solutions have been adopted by companies to minimise the effects of these outages. However, the selection of an optimal DR solution is difficult since there does not exist a single solution that suits the requirement of every company (e.g., availability and costs). In this paper, we propose an integrated model-experiment approach to evaluate DR solutions. We perform experiments in different real-world DR solutions and propose analytic models to evaluate these solutions regarding DR key-metrics: steady-state availability, Recovery time Objective (RTO), Recovery Point Objective (RPO), downtime, and costs. The results reveal that DR solutions can significantly improve availability and minimise costs. Also, a sensitivity analysis identifies the parameters that most affect the RPO and RTO of the DR adopted solutions.

  • DRCN - Multiple-criteria Evaluation of Disaster Recovery Strategies Based on Stochastic Models
    2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020, 2020
    Co-Authors: Júlio Mendonça, Julian Araujo, Ermeson Andrade, Ricardo Lima, Dong Seong Kim
    Abstract:

    The consequences for a company losing its data or having its IT system disrupted are severe and can impact negatively on business operations. It can also cause customer dissatisfaction and subsequent revenue loss. In a competitive global market, companies have been adopting disaster Recovery (DR) strategies as an attempt to keep IT systems operational, prevent data loss, and ensure business continuity. However, there is not a single DR strategy that meets the requirements of every business (e.g., availability and cost). Besides, most of the time, these requirements are conflicting. Therefore, efficient and accurate analysis of DR strategies before its deployment is crucial to choose the best strategy that suits companies’ needs and budget. In this paper, we propose the adoption of a multiple-criteria decision-making (MCDM) method and stochastic models to evaluate and rank DR strategies for IT infrastructures. The stochastic models are used for quantitative assessing distinct DR strategies regarding five DR key-metrics: availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO), and cost. We also use an MCDM method to rank the strategies according to multiple criteria (e.g., availability maximization and costs minimization). A case study demonstrates the feasibility and usefulness of the proposed approach for finding the best DR strategies according to multiple criteria.

  • ISCC - Evaluation of a Backup-as-a-Service Environment for Disaster Recovery
    2019 IEEE Symposium on Computers and Communications (ISCC), 2019
    Co-Authors: Júlio Mendonça, Ewerton Queiroz, Ricardo Lima, Ermeson Andrade
    Abstract:

    Systems unavailability may produce severe consequences for modern business such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster Recovery (DR) solutions have been adopted by many organizations as an attempt to prevent data loss and ensure business continuity. With the cloud computing expansion, different cloud providers have been offering low-cost solutions for DR purposes such as the Backup-as-a-service (BaaS) for consumers. Therefore, in this paper, we present an integrated model-experiment approach to evaluate a BaaS environment for DR purposes. We use analytic models and fault-injection experiments to evaluate DR keymetrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO) in a real-world BaaS environment. The results revealed that the environment availability can vary according to the amount of data to backed up and restored. Besides, a sensitivity analysis shows that the RTO and RPO are mainly influenced by the the mean time to recover from a disaster and the backup interval, respectively.

  • SMC - Evaluating Database Replication Mechanisms for Disaster Recovery in Cloud Environments
    2019 IEEE International Conference on Systems Man and Cybernetics (SMC), 2019
    Co-Authors: Júlio Mendonça, Ermeson Andrade, Wilson Medeiros, Ronierison Maciel, Paulo Maciel, Ricardo Lima
    Abstract:

    Relational databases are the most popular database system worldwide. The occurrence of failures in these systems may produce severe consequences for the business, such as data loss, customer dissatisfaction, and subsequent revenue loss. Consequently, many organizations have adopted disaster Recovery (DR) solutions as an attempt to prevent data loss and ensure business continuity. Data replication for databases is one of the most used DR solution employed to guarantee data safety and availability. However, the analysis regarding DR aspects has been less explored. Therefore, in this paper, we present an integrated model-experiment approach to evaluate replication mechanisms in relational databases for DR purposes. We performed experiments in a geo-distributed cloud environment and developed analytic models to evaluate DR key-metrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO). The results revealed that the adoption of replication mechanisms could increase the system’s availability significantly. It also revealed that the replication mechanisms can guarantee RPO and RTO within seconds.

Ermeson Andrade - One of the best experts on this subject based on the ideXlab platform.

  • Multiple-criteria Evaluation of Disaster Recovery Strategies Based on Stochastic Models
    2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020, 2020
    Co-Authors: Júlio Mendonça, Ermeson Andrade, Ricardo Lima, Julian Araujo
    Abstract:

    The consequences for a company losing its data or having its IT system disrupted are severe and can impact negatively on business operations. It can also cause customer dissatisfaction and subsequent revenue loss. In a competitive global market, companies have been adopting disaster Recovery (DR) strategies as an attempt to keep IT systems operational, prevent data loss, and ensure business continuity. However, there is not a single DR strategy that meets the requirements of every business (e.g., availability and cost). Besides, most of the time, these requirements are conflicting. Therefore, efficient and accurate analysis of DR strategies before its deployment is crucial to choose the best strategy that suits companies’ needs and budget. In this paper, we propose the adoption of a multiple-criteria decision-making (MCDM) method and stochastic models to evaluate and rank DR strategies for IT infrastructures. The stochastic models are used for quantitative assessing distinct DR strategies regarding five DR key-metrics: availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO), and cost. We also use an MCDM method to rank the strategies according to multiple criteria (e.g., availability maximization and costs minimization). A case study demonstrates the feasibility and usefulness of the proposed approach for finding the best DR strategies according to multiple criteria.

  • Evaluating and modelling solutions for disaster Recovery
    International Journal of Grid and Utility Computing, 2020
    Co-Authors: Júlio Mendonça, Ricardo Lima, Ermeson Andrade
    Abstract:

    Systems outages can have disastrous effects on businesses such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster Recovery (DR) solutions have been adopted by companies to minimise the effects of these outages. However, the selection of an optimal DR solution is difficult since there does not exist a single solution that suits the requirement of every company (e.g., availability and costs). In this paper, we propose an integrated model-experiment approach to evaluate DR solutions. We perform experiments in different real-world DR solutions and propose analytic models to evaluate these solutions regarding DR key-metrics: steady-state availability, Recovery time Objective (RTO), Recovery Point Objective (RPO), downtime, and costs. The results reveal that DR solutions can significantly improve availability and minimise costs. Also, a sensitivity analysis identifies the parameters that most affect the RPO and RTO of the DR adopted solutions.

  • DRCN - Multiple-criteria Evaluation of Disaster Recovery Strategies Based on Stochastic Models
    2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020, 2020
    Co-Authors: Júlio Mendonça, Julian Araujo, Ermeson Andrade, Ricardo Lima, Dong Seong Kim
    Abstract:

    The consequences for a company losing its data or having its IT system disrupted are severe and can impact negatively on business operations. It can also cause customer dissatisfaction and subsequent revenue loss. In a competitive global market, companies have been adopting disaster Recovery (DR) strategies as an attempt to keep IT systems operational, prevent data loss, and ensure business continuity. However, there is not a single DR strategy that meets the requirements of every business (e.g., availability and cost). Besides, most of the time, these requirements are conflicting. Therefore, efficient and accurate analysis of DR strategies before its deployment is crucial to choose the best strategy that suits companies’ needs and budget. In this paper, we propose the adoption of a multiple-criteria decision-making (MCDM) method and stochastic models to evaluate and rank DR strategies for IT infrastructures. The stochastic models are used for quantitative assessing distinct DR strategies regarding five DR key-metrics: availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO), and cost. We also use an MCDM method to rank the strategies according to multiple criteria (e.g., availability maximization and costs minimization). A case study demonstrates the feasibility and usefulness of the proposed approach for finding the best DR strategies according to multiple criteria.

  • ISCC - Evaluation of a Backup-as-a-Service Environment for Disaster Recovery
    2019 IEEE Symposium on Computers and Communications (ISCC), 2019
    Co-Authors: Júlio Mendonça, Ewerton Queiroz, Ricardo Lima, Ermeson Andrade
    Abstract:

    Systems unavailability may produce severe consequences for modern business such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster Recovery (DR) solutions have been adopted by many organizations as an attempt to prevent data loss and ensure business continuity. With the cloud computing expansion, different cloud providers have been offering low-cost solutions for DR purposes such as the Backup-as-a-service (BaaS) for consumers. Therefore, in this paper, we present an integrated model-experiment approach to evaluate a BaaS environment for DR purposes. We use analytic models and fault-injection experiments to evaluate DR keymetrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO) in a real-world BaaS environment. The results revealed that the environment availability can vary according to the amount of data to backed up and restored. Besides, a sensitivity analysis shows that the RTO and RPO are mainly influenced by the the mean time to recover from a disaster and the backup interval, respectively.

  • SMC - Evaluating Database Replication Mechanisms for Disaster Recovery in Cloud Environments
    2019 IEEE International Conference on Systems Man and Cybernetics (SMC), 2019
    Co-Authors: Júlio Mendonça, Ermeson Andrade, Wilson Medeiros, Ronierison Maciel, Paulo Maciel, Ricardo Lima
    Abstract:

    Relational databases are the most popular database system worldwide. The occurrence of failures in these systems may produce severe consequences for the business, such as data loss, customer dissatisfaction, and subsequent revenue loss. Consequently, many organizations have adopted disaster Recovery (DR) solutions as an attempt to prevent data loss and ensure business continuity. Data replication for databases is one of the most used DR solution employed to guarantee data safety and availability. However, the analysis regarding DR aspects has been less explored. Therefore, in this paper, we present an integrated model-experiment approach to evaluate replication mechanisms in relational databases for DR purposes. We performed experiments in a geo-distributed cloud environment and developed analytic models to evaluate DR key-metrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO). The results revealed that the adoption of replication mechanisms could increase the system’s availability significantly. It also revealed that the replication mechanisms can guarantee RPO and RTO within seconds.