Jorge Mario Cortés-Mendoza | South Ural State University (original) (raw)

Papers by Jorge Mario Cortés-Mendoza

Research paper thumbnail of Towards Mitigating Uncertainty of Data Security Breaches and Collusion in Cloud Computing

2017 28th International Workshop on Database and Expert Systems Applications (DEXA)

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Research paper thumbnail of RoC Prediction for Bi-Objective Cost-QoS Optimization of Cloud VoIP Call Allocations

2017 IVth International Conference on Engineering and Telecommunication (EnT)

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Research paper thumbnail of Analysis of secured distributed cloud data storage based on multilevel RNS

2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2018

Cloud data storages are functioning in the presence of the risks of confidentiality, integrity, a... more Cloud data storages are functioning in the presence of the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this paper, we provide analysis of reliable, scalable, and confidential distributed data storage based on Multilevel Residue Number System (RNS) and Mignotte secret sharing scheme. We use real cloud providers and estimate characteristics such as the data redundancy, speed of data encoding, and decoding to cope with different user preferences. The analysis shows that the proposed storage scheme increases safety and reliability of traditional approaches and reduces data storage overheads by appropriate selection of RNS parameters.

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Research paper thumbnail of Load-Aware Strategies for Cloud-Based VoIP Optimization with VM Startup Prediction

2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017

In this paper, we address cloud VoIP scheduling strategies to provide appropriate levels of quali... more In this paper, we address cloud VoIP scheduling strategies to provide appropriate levels of quality of service to users, and cost to VoIP service providers. This bi-objective focus is reasonable and representative for real installations and applications. We conduct comprehensive simulation on real data of twenty three on-line non-clairvoyant scheduling strategies with fixed threshold of utilization to request VMs, and twenty strategies with dynamic prediction of the load. We show that our load-aware with predictions strategies outperform the known ones providing suitable quality of service and lower cost. The robustness of these strategies is also analyzed varying VM startup time delays to deal with realistic VoIP cloud environments.

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Research paper thumbnail of Distributed Adaptive VoIP Load Balancing in Hybrid Clouds

Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, ... more Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems. They require resource optimization at multiple layers of the infrastructure and applications. The complexity of cloud computing systems makes infeasible the optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem of load balancing in distributed computer environments and review several algorithms. The goal is to understand the main characteristics of dynamic load balancing algorithms and how they can be adapted for the domain of VoIP computations on hybrid clouds. We conclude by showing how none of these works directly addresses the problem space of the c...

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Research paper thumbnail of Configurable cost-quality optimization of cloud-based VoIP

Journal of Parallel and Distributed Computing

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Research paper thumbnail of AC-RRNS: Anti-collusion secured data sharing scheme for cloud storage

International Journal of Approximate Reasoning

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Research paper thumbnail of AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security

Future Generation Computer Systems

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Research paper thumbnail of Min_c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention

Programming and Computer Software

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Research paper thumbnail of Robust cloud VoIP scheduling under VMs startup time delay uncertainty

Proceedings of the 9th International Conference on Utility and Cloud Computing - UCC '16, 2016

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Research paper thumbnail of Min_с: стратегия неоднородной концентрации задач для энергосберегающих компьютерных расписаний

Proceedings of the Institute for System Programming of the RAS, 2015

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Research paper thumbnail of VoIP Traffic Modelling Using Gaussian Mixture Models, Gaussian Processes and Interactive Particle Algorithms

2015 IEEE Globecom Workshops (GC Wkshps), 2015

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Research paper thumbnail of Distributed VoIP Load Balancing in Cloud Computing

Cloud computing is widely being adopted by many companies because it allows to maximize the utili... more Cloud computing is widely being adopted by many companies because it allows to maximize the utilization of their resources. Unfortunately, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems, they require resource optimization at multiple layers of the infrastructure and applications. The complexity of cloud computing systems makes infeasible the optimal or near-optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem and describe several load balancing algorithms in distributed computer environments. The goal is to understand the main characteristics of load balancing algorithms and how they can be adapted for the domain of VoIP computations on federated clouds. We conclude by showing how none of these...

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Research paper thumbnail of Adaptive energy efficient distributed VoIP load balancing in federated cloud infrastructure

2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), 2014

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Research paper thumbnail of Generalized extremal optimization for parallel job scheduling in two levels hierarchical Grid systems

Grids are becoming almost commonplace nowadays because of the benefits shown when it is necessary... more Grids are becoming almost commonplace nowadays because of the benefits shown when it is necessary to work with a large amount of data or computing power. The initial challenges of Grid computing have been overcome to first order (running a job, transferring files, managing multiple user accounts, etc.). Researchers can now address the issues involved to improve the uses of Grid computing, one of the most studied field is job scheduling. Several techniques have been used to address the problem of scheduling. In recent years the use of heuristics inspired by nature has boomed owing the effectiveness to solve hard problems. This work presents the generalized extremal optimization heuristic (GEO) to solve parallel job scheduling problem in two levels hierarchical Grid systems. GEO heuristic is based on the extremal optimization observed in complex systems, where generational avalanches evolve an ecosystem to a critical state. The abstraction of this model has been successfully implement...

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Research paper thumbnail of Distributed Adaptive VoIP Load Balancing in Hybrid Clouds

Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, ... more Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems. They
require resource optimization at multiple layers of the infrastructure and applications. The complexity of cloud computing systems makes unfeasible the optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem of load balancing in distributed computer environments and review several algorithms. The goal is to understand the main characteristics of dynamic load balancing algorithms and how they can be adapted for the domain of VoIP computations on hybrid clouds. We conclude by showing how none of these works directly addresses the problem space of the considered problem, but do provide a valuable basis for our work.

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Research paper thumbnail of Heterogeneous Job Consolidation for Power Aware Scheduling with Quality of Service

In this paper, we present an energy optimization model of Cloud computing, and formulate novel en... more In this paper, we present an energy optimization model of Cloud computing, and formulate novel energy-aware resource allocation problem that provides energy-efficiency by heterogeneous job consolidation taking into account types of applications. Data centers process heterogeneous workloads that include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other types of applications. When one type of applications creates a bottleneck and resource contention either in CPU, disk or network, it may result in degradation of the system performance and increasing energy consumption. We discuss energy characteristics of applications, and how an awareness of their types can help in intelligent allocation strategy to improve energy consumption.

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Research paper thumbnail of Distributed Adaptive VoIP Load Balancing in Hybrid Clouds

NC&SC’2015 - Network Computing & Supercomputing workshop. In conjunction with RuSCDays'15 - The Russian Supercomputing Days, September 28-29, 2015, Moscow

Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, ... more Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems. They require
resource optimization at multiple layers of the infrastructure and applications. The complexity
of cloud computing systems makes infeasible the optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem of load balancing in distributed computer environments and review several algorithms. The goal is to understand the main characteristics of dynamic load balancing algorithms and how they can be adapted for the domain of VoIP computations on hybrid clouds. We conclude by showing how none of these works directly addresses the problem space of the considered problem, but do provide a valuable basis for our work

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Research paper thumbnail of Heterogeneous Job Consolidation for Power Aware Scheduling with Quality of Service

NC&SC’2015 - Network Computing & Supercomputing workshop. In conjunction with RuSCDays'15 - The Russian Supercomputing Days, September 28-29, 2015, Moscow

In this paper, we present an energy optimization model of Cloud computing, and formulate novel en... more In this paper, we present an energy optimization model of Cloud computing, and formulate novel energy-aware resource allocation problem that provides energy-efficiency by heterogeneous job consolidation taking into account types of applications. Data centers process heterogeneous workloads that include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other types of applications. When one type of applications creates a bottleneck and resource contention either in CPU, disk or network, it may result in degradation of the system performance and increasing energy consumption. We discuss energy characteristics of applications, and how an awareness of their types can help in intelligent allocation strategy to improve energy consumption.

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Research paper thumbnail of Towards Mitigating Uncertainty of Data Security Breaches and Collusion in Cloud Computing

2017 28th International Workshop on Database and Expert Systems Applications (DEXA)

Bookmarks Related papers MentionsView impact

Research paper thumbnail of RoC Prediction for Bi-Objective Cost-QoS Optimization of Cloud VoIP Call Allocations

2017 IVth International Conference on Engineering and Telecommunication (EnT)

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Analysis of secured distributed cloud data storage based on multilevel RNS

2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2018

Cloud data storages are functioning in the presence of the risks of confidentiality, integrity, a... more Cloud data storages are functioning in the presence of the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this paper, we provide analysis of reliable, scalable, and confidential distributed data storage based on Multilevel Residue Number System (RNS) and Mignotte secret sharing scheme. We use real cloud providers and estimate characteristics such as the data redundancy, speed of data encoding, and decoding to cope with different user preferences. The analysis shows that the proposed storage scheme increases safety and reliability of traditional approaches and reduces data storage overheads by appropriate selection of RNS parameters.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Load-Aware Strategies for Cloud-Based VoIP Optimization with VM Startup Prediction

2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017

In this paper, we address cloud VoIP scheduling strategies to provide appropriate levels of quali... more In this paper, we address cloud VoIP scheduling strategies to provide appropriate levels of quality of service to users, and cost to VoIP service providers. This bi-objective focus is reasonable and representative for real installations and applications. We conduct comprehensive simulation on real data of twenty three on-line non-clairvoyant scheduling strategies with fixed threshold of utilization to request VMs, and twenty strategies with dynamic prediction of the load. We show that our load-aware with predictions strategies outperform the known ones providing suitable quality of service and lower cost. The robustness of these strategies is also analyzed varying VM startup time delays to deal with realistic VoIP cloud environments.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Distributed Adaptive VoIP Load Balancing in Hybrid Clouds

Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, ... more Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems. They require resource optimization at multiple layers of the infrastructure and applications. The complexity of cloud computing systems makes infeasible the optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem of load balancing in distributed computer environments and review several algorithms. The goal is to understand the main characteristics of dynamic load balancing algorithms and how they can be adapted for the domain of VoIP computations on hybrid clouds. We conclude by showing how none of these works directly addresses the problem space of the c...

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Research paper thumbnail of Configurable cost-quality optimization of cloud-based VoIP

Journal of Parallel and Distributed Computing

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Research paper thumbnail of AC-RRNS: Anti-collusion secured data sharing scheme for cloud storage

International Journal of Approximate Reasoning

Bookmarks Related papers MentionsView impact

Research paper thumbnail of AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security

Future Generation Computer Systems

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Min_c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention

Programming and Computer Software

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Robust cloud VoIP scheduling under VMs startup time delay uncertainty

Proceedings of the 9th International Conference on Utility and Cloud Computing - UCC '16, 2016

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Min_с: стратегия неоднородной концентрации задач для энергосберегающих компьютерных расписаний

Proceedings of the Institute for System Programming of the RAS, 2015

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Research paper thumbnail of VoIP Traffic Modelling Using Gaussian Mixture Models, Gaussian Processes and Interactive Particle Algorithms

2015 IEEE Globecom Workshops (GC Wkshps), 2015

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Distributed VoIP Load Balancing in Cloud Computing

Cloud computing is widely being adopted by many companies because it allows to maximize the utili... more Cloud computing is widely being adopted by many companies because it allows to maximize the utilization of their resources. Unfortunately, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems, they require resource optimization at multiple layers of the infrastructure and applications. The complexity of cloud computing systems makes infeasible the optimal or near-optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem and describe several load balancing algorithms in distributed computer environments. The goal is to understand the main characteristics of load balancing algorithms and how they can be adapted for the domain of VoIP computations on federated clouds. We conclude by showing how none of these...

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Research paper thumbnail of Adaptive energy efficient distributed VoIP load balancing in federated cloud infrastructure

2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), 2014

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Generalized extremal optimization for parallel job scheduling in two levels hierarchical Grid systems

Grids are becoming almost commonplace nowadays because of the benefits shown when it is necessary... more Grids are becoming almost commonplace nowadays because of the benefits shown when it is necessary to work with a large amount of data or computing power. The initial challenges of Grid computing have been overcome to first order (running a job, transferring files, managing multiple user accounts, etc.). Researchers can now address the issues involved to improve the uses of Grid computing, one of the most studied field is job scheduling. Several techniques have been used to address the problem of scheduling. In recent years the use of heuristics inspired by nature has boomed owing the effectiveness to solve hard problems. This work presents the generalized extremal optimization heuristic (GEO) to solve parallel job scheduling problem in two levels hierarchical Grid systems. GEO heuristic is based on the extremal optimization observed in complex systems, where generational avalanches evolve an ecosystem to a critical state. The abstraction of this model has been successfully implement...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Distributed Adaptive VoIP Load Balancing in Hybrid Clouds

Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, ... more Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems. They
require resource optimization at multiple layers of the infrastructure and applications. The complexity of cloud computing systems makes unfeasible the optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem of load balancing in distributed computer environments and review several algorithms. The goal is to understand the main characteristics of dynamic load balancing algorithms and how they can be adapted for the domain of VoIP computations on hybrid clouds. We conclude by showing how none of these works directly addresses the problem space of the considered problem, but do provide a valuable basis for our work.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Heterogeneous Job Consolidation for Power Aware Scheduling with Quality of Service

In this paper, we present an energy optimization model of Cloud computing, and formulate novel en... more In this paper, we present an energy optimization model of Cloud computing, and formulate novel energy-aware resource allocation problem that provides energy-efficiency by heterogeneous job consolidation taking into account types of applications. Data centers process heterogeneous workloads that include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other types of applications. When one type of applications creates a bottleneck and resource contention either in CPU, disk or network, it may result in degradation of the system performance and increasing energy consumption. We discuss energy characteristics of applications, and how an awareness of their types can help in intelligent allocation strategy to improve energy consumption.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Distributed Adaptive VoIP Load Balancing in Hybrid Clouds

NC&SC’2015 - Network Computing & Supercomputing workshop. In conjunction with RuSCDays'15 - The Russian Supercomputing Days, September 28-29, 2015, Moscow

Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, ... more Cloud computing as a powerful economic stimulus widely being adopted by many companies. However, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems. They require
resource optimization at multiple layers of the infrastructure and applications. The complexity
of cloud computing systems makes infeasible the optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem of load balancing in distributed computer environments and review several algorithms. The goal is to understand the main characteristics of dynamic load balancing algorithms and how they can be adapted for the domain of VoIP computations on hybrid clouds. We conclude by showing how none of these works directly addresses the problem space of the considered problem, but do provide a valuable basis for our work

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Heterogeneous Job Consolidation for Power Aware Scheduling with Quality of Service

NC&SC’2015 - Network Computing & Supercomputing workshop. In conjunction with RuSCDays'15 - The Russian Supercomputing Days, September 28-29, 2015, Moscow

In this paper, we present an energy optimization model of Cloud computing, and formulate novel en... more In this paper, we present an energy optimization model of Cloud computing, and formulate novel energy-aware resource allocation problem that provides energy-efficiency by heterogeneous job consolidation taking into account types of applications. Data centers process heterogeneous workloads that include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other types of applications. When one type of applications creates a bottleneck and resource contention either in CPU, disk or network, it may result in degradation of the system performance and increasing energy consumption. We discuss energy characteristics of applications, and how an awareness of their types can help in intelligent allocation strategy to improve energy consumption.

Bookmarks Related papers MentionsView impact