The Experts below are selected from a list of 213 Experts worldwide ranked by ideXlab platform

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.

  • Dependability evaluation of a disaster recovery solution for IoT infrastructures
    The Journal of Supercomputing, 2020
    Co-Authors: Ermeson Andrade, Bruno Nogueira
    Abstract:

    The ongoing miniaturization and cost reduction of electronic devices (microprocessors, sensors, batteries, and wireless communication units) have allowed the proliferation of the Internet of Things (IoT)-based applications. Many of these applications are mission-critical (e.g., healthcare and traffic road management), in the sense that the IoT system needs to take critical decisions in real-time. Hence, these IoT systems need to be designed using effective fault-tolerant techniques like disaster recovery (DR) solutions. This work proposes a Petri net-based approach for modeling and analysis of DR solutions for IoT infrastructures. The proposed models allow assessing important DR measures, such as system availability, cost, and recovery time objective. To demonstrate the feasibility of our approach, we present a case study in which a real-world healthcare IoT system is modeled and analyzed. Besides, sensitivity analysis is carried out to assess the effects of model parameters on the system availability.

  • 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.

  • 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, Ricardo Lima, Ewerton Queiroz, 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.

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

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.

  • 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.

  • 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, Ricardo Lima, Ewerton Queiroz, 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.

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

  • 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.

  • 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.

  • 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, Ricardo Lima, Ewerton Queiroz, 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.

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

  • 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.

Yashwant K. Malaiya - One of the best experts on this subject based on the ideXlab platform.

  • ISSRE Workshops - Are the Classical Disaster recovery Tiers Still Applicable Today
    2014 IEEE International Symposium on Software Reliability Engineering Workshops, 2014
    Co-Authors: Omar H. Alhami, Yashwant K. Malaiya
    Abstract:

    As disaster recovery plans (DRPs) for IT systems have been improving over the past decades, some metrics became widely accepted such as recovery time objective (RTO) and recovery point objective (RPO). However, disaster recovery plans and solutions vary in their design, sophistication and their required RTO/RTO. Therefore, a need to categorize disaster recovery plans into tiers has become necessary. Fortunately, a number of classifications exist but sometimes they are not fully explained, so, independent researchers may find the classification confusing or inappropriate for the current state of technology with significant overlap among tiers. Moreover, advances in communication and technology and the introduction of disaster recovery as a service (DRaaS) by several cloud service providers (CSPs) has reshaped the area of disaster recovery and development of DRPs. Therefore, one can argue that the old classification of 7-tiers of DRPs is obsolete and a new classification is needed. Here, we try to survey these classifications, understand the common grounds and the differences and try to suggest some improvements to gap them.

  • Are the Classical Disaster recovery Tiers Still Applicable Today?
    2014 IEEE International Symposium on Software Reliability Engineering Workshops, 2014
    Co-Authors: Omar H. Alhami, Yashwant K. Malaiya
    Abstract:

    As disaster recovery plans (DRPs) for IT systems have been improving over the past decades, some metrics became widely accepted such as recovery time objective (RTO) and recovery point objective (RPO). However, disaster recovery plans and solutions vary in their design, sophistication and their required RTO/RTO. Therefore, a need to categorize disaster recovery plans into tiers has become necessary. Fortunately, a number of classifications exist but sometimes they are not fully explained, so, independent researchers may find the classification confusing or inappropriate for the current state of technology with significant overlap among tiers. Moreover, advances in communication and technology and the introduction of disaster recovery as a service (DRaaS) by several cloud service providers (CSPs) has reshaped the area of disaster recovery and development of DRPs. Therefore, one can argue that the old classification of 7-tiers of DRPs is obsolete and a new classification is needed. Here, we try to survey these classifications, understand the common grounds and the differences and try to suggest some improvements to gap them.

  • Evaluating disaster recovery plans using the cloud
    2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS), 2013
    Co-Authors: Omar H. Alhazmi, Yashwant K. Malaiya
    Abstract:

    Every organization requires a business continuity plan (BCP) or disaster recovery plan (DRP) which falls within cost constraints while achieving the target recovery requirements in terms of recovery time objective (RTO) and recovery point objective (RPO). The organizations must identify the likely events that can cause disasters and evaluate their impact. They need to set the objectives clearly, evaluate feasible disaster recovery plans to choose the DRP that would be optimal. The paper examines tradeoffs involved and presents guidelines for choosing among the disaster recovery options. The optimal disaster recovery planning should take into consideration the key parameters including the initial cost, the cost of data transfers, and the cost of data storage. The organization data needs and its disaster recovery objectives need to be considered. To evaluate the risk, the types of disaster (natural or human-caused) need to be identified. The probability of a disaster occurrence needs to be assessed along with the costs of corresponding failures. An appropriate approach for the cost evaluation needs to be determined to allow a quantitative assessment of currently active disaster recovery plans (DRP) in terms of the time need to restore the service (associated with RTO) and possible loss of data (associated with RPO). This can guide future development of the plan and maintenance of the DRP. Such a quantitative approach would also allow CIOs to compare applicable DRP solutions.

  • ISSRE Workshops - Assessing Disaster recovery Alternatives: On-Site, Colocation or Cloud
    2012 IEEE 23rd International Symposium on Software Reliability Engineering Workshops, 2012
    Co-Authors: Omar H. Alhazmi, Yashwant K. Malaiya
    Abstract:

    Every organization requires a business continuity plan (BCP) or disaster recovery plan (DRP) which falls within the cost constraints while achieving the target recovery requirement's in terms of recovery time objective (RTO) and recovery point objective (RPO). The organizations must identify the likely events that can cause disasters and evaluate their impact. They need to set the objectives clearly, evaluate feasible /DRPs to choose the one that would be optimal. Here we examine tradeoffs involved in choosing among the disaster recovery options. The optimal disaster recovery planning should take into consideration the key parameters including the initial cost, the cost of data transfers, and the cost of data storage. To evaluate the risk, the types of disaster (natural or human-caused) need to be identified along with the probability of a disaster occurrence and the costs of corresponding failures needs to be evaluated. An appropriate approach for the cost evaluation needs to be determined to allow a quantitative assessment of currently active disaster recovery plans (DRP) in terms of the time need to restore the service (associated with RTO) and possible loss of data (associated with RPO).

  • Assessing Disaster recovery Alternatives: On-Site, Colocation or Cloud
    2012 IEEE 23rd International Symposium on Software Reliability Engineering Workshops, 2012
    Co-Authors: Omar H. Alhazmi, Yashwant K. Malaiya
    Abstract:

    Every organization requires a business continuity plan (BCP) or disaster recovery plan (DRP) which falls within the cost constraints while achieving the target recovery requirement's in terms of recovery time objective (RTO) and recovery point objective (RPO). The organizations must identify the likely events that can cause disasters and evaluate their impact. They need to set the objectives clearly, evaluate feasible /DRPs to choose the one that would be optimal. Here we examine tradeoffs involved in choosing among the disaster recovery options. The optimal disaster recovery planning should take into consideration the key parameters including the initial cost, the cost of data transfers, and the cost of data storage. To evaluate the risk, the types of disaster (natural or human-caused) need to be identified along with the probability of a disaster occurrence and the costs of corresponding failures needs to be evaluated. An appropriate approach for the cost evaluation needs to be determined to allow a quantitative assessment of currently active disaster recovery plans (DRP) in terms of the time need to restore the service (associated with RTO) and possible loss of data (associated with RPO).

H. Sunahara - One of the best experts on this subject based on the ideXlab platform.

  • A Flexible Replication Mechanism with Extended Database Connection Layers
    Fifth IEEE International Symposium on Network Computing and Applications (NCA'06), 2006
    Co-Authors: N. Nakamura, K. Fujiyama, E. Kawai, H. Sunahara
    Abstract:

    It is vital to achieve a disaster recovery system that allows a backup site to take over a primary site's IT services while the primary site is down. We propose a flexible replication mechanism based on service requirements such as system performance, recovery time objective (RTO), and recovery point objective (RPO). For high flexibility, the mechanism controls the replication schedule by monitoring the application's database accesses in the database connection library and matching the accesses with previously registered access patterns. In our experiments, we confirmed that the proposed mechanism outperforms other existing mechanisms, especially in situations with network delays and packet losses