Joanna Kolodziej - Academia.edu (original) (raw)

Papers by Joanna Kolodziej

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)

Looking at the rapid development of computer networks, it can be said that the transmission quali... more Looking at the rapid development of computer networks, it can be said that the transmission quality assurance is very important issue. In the past there were attempts to implement Quality of Service (QoS) techniques when using various network technologies. However QoS parameters are not always assured. This paper presents a novel concept of transmission quality determination based on Machine Learning (ML) methods. Transmission quality is determined by four parameters-delay, jitter, bandwidth and packet loss ratio. The concept of transmission quality assured network proposed by Pay&Require was presented as a novel multi-agent approach for QoS based computer networks. In this concept the essential part is transmission quality rating which is done based on transmission parameters by ML techniques. Data set was obtained based on the experience of the users test group. For our research we designed a machine learning system for transmission quality assessment. We obtained promising results using four classifiers: Nu-Support Vector Classifier (Nu-SVC), C-Support Vector Classifier (C-SVC), Random Forest Classifier, and K-Nearest Neighbors (kNN) algorithm. Classification results for different methods are presented together with confusion matrices. The best result, 87% sensitivity (overall accuracy), for the test set of data, was achieved by Nu-SVC and Random Forest (13/100 incorrect classifications).

Studies in Big Data

In this chapter, the k-anonymity algorithm is used for anonymization of sensitive data sending vi... more In this chapter, the k-anonymity algorithm is used for anonymization of sensitive data sending via network and analyzed by experts. Anonymization is a technique used to generalize sensitive data to block the possibility of assigning them to specific individuals or entities. In our proposed model, we have developed a layer that enables virtualization of sensitive data, ensuring that they are transmitted safely over the network and analyzed with respects the protection of personal data. Solution has been verified in real use case for transmission sports data to the experts who send the diagnosis as a response.

Journal of telecommunications and information technology, 2016

Stackelberg games are non-symmetric games where one player or specified group of players have the... more Stackelberg games are non-symmetric games where one player or specified group of players have the privilege position and make decision before the other players. Such games are used in telecommunication and computational systems for supporting administrative decisions. Recently Stackleberg games became useful also in the systems where security issues are the crucial decision criteria. In this paper authors briefly survey the most popular Stackelberg security game models and provide the analysis of the model properties illustrated in the realistic use cases. Keywords—Bayesian games, game theory, leadership, Nash equilibrium, normal form games, security games, Stackelberg equilibrium, Stackelberg games.

The intensive development and growth in the popularity of mobile cloud computing services bring a... more The intensive development and growth in the popularity of mobile cloud computing services bring a critical need to introduce new solutions that increase the level of cloud and users security. One of the critical issues in highly distributed computational systems is a task scheduling process. This process may be exposed to many external and internal security threats, like task injection, machine failure or generation of incorrect schedule. These problems are especially important in mobile environments. It can be even more complicated if we take into consideration the personalization of the services offered. Recently, blockchain has been gaining rapidly in popularity, combining high efficiency with applications in distributed and highly personalized computational environments. In this paper, we developed and described a novel model for security-aware task scheduling in cloud computing based on blockchain technology. Unlike other blockchain-based solutions, the proposed model uses Proo...

This chapter summarizes our recent research on game-theoretical models of grid users’ behavior in... more This chapter summarizes our recent research on game-theoretical models of grid users’ behavior in security aware scheduling. The main scheduling attributes, generic model of security aware management in local grid clusters and several scenarios of users’ games are presented. Four GA-based hybrid schedulers have been implemented for the approximation of the equilibrium states of exemplary simple symmetric game of the grid end users. The proposed hybrid resolution methLarge Scale Network-Centric Computing Systems, 1st edition. By Albert Y. Zomaya and Hamid Sarbazi-Azad Copyright c ⃝ 2012 John Wiley & Sons, Inc. 1 2 GAME-BASED MODELS OF GRID USERS’ DECISIONS... ods are empirically evaluated through the grid simulator under the heterogeneity, security, large-scale and dynamics conditions.

Journal of Parallel and Distributed Computing

Abstract Cloud computing (CC) systems are the most popular computational environments for providi... more Abstract Cloud computing (CC) systems are the most popular computational environments for providing elastic and scalable services on a massive scale. The nature of such systems often results in energy-related problems that have to be solved for sustainability, cost reduction, and environment protection. In this paper we defined and developed a set of performance and energy-aware strategies for resource allocation, task scheduling, and for the hibernation of virtual machines. The idea behind this model is to combine energy and performance-aware scheduling policies in order to hibernate those virtual machines that operate in idle state. The efficiency achieved by applying the proposed models has been tested using a realistic large-scale CC system simulator. Obtained results show that a balance between low energy consumption and short makespan can be achieved. Several security constraints may be considered in this model. Each security constraint is characterized by: (a) Security Demands (SD) of tasks; and (b) Trust Levels (TL) provided by virtual machines. SD and TL are computed during the scheduling process in order to provide proper security services. Experimental results show that the proposed solution reduces up to 45% of the energy consumption of the CC system. Such significant improvement was achieved by the combination of an energy-aware scheduler with energy-efficiency policies focused on the hibernation of VMs.

Evolutionary Hierachical Multi Criteria Metaheuristics For Schheduling in Large Scale Grid System Joanna Kolodziej, 2012

Concurrency and Computation: Practice and Experience, 2016

Journal for his support and advises, as well as to the editorial and managerial journal team from... more Journal for his support and advises, as well as to the editorial and managerial journal team from Wiley for their assistance and excellent cooperative collaboration to produce this valuable scientific work.

International Journal of Applied Mathematics and Computer Science, 2015

When there is a mismatch between the cardinality of a periodic task set and the priority levels s... more When there is a mismatch between the cardinality of a periodic task set and the priority levels supported by the underlying hardware systems, multiple tasks are grouped into one class so as to maintain a specific level of confidence in their accuracy. However, such a transformation is achieved at the expense of the loss of schedulability of the original task set. We further investigate the aforementioned problem and report the following contributions: (i) a novel technique for mapping unlimited priority tasks into a reduced number of classes that do not violate the schedulability of the original task set and (ii) an efficient feasibility test that eliminates insufficient points during the feasibility analysis. The theoretical correctness of both contributions is checked through formal verifications. Moreover, the experimental results reveal the superiority of our work over the existing feasibility tests by reducing the number of scheduling points that are needed otherwise.

IEEE Cloud Computing, 2015

he emergence of cloud computing has facilitated resource sharing beyond organizational boundaries... more he emergence of cloud computing has facilitated resource sharing beyond organizational boundaries and among various applications. This cloud resource sharing is primarily driven by resource virtualization and utility computing (the pay-as-you-go pricing model). The generic multilayered cloud service model is appealing to many parties-from small businesses looking for a low upfront infrastructure investment, to enterprises wanting to cut the cost of managing infrastructures, to research communities requiring large-scale data processing and computing power. In a cloud environment, computing resources (processors, storage devices, network bandwidth, and so on) and applications are provided as services over the Internet. Fueled by an insatiable demand for new Internet services and a shift to cloud computing services that are largely hosted in commercial datacenters and in the large data farms operated by companies like Amazon, Apple, Google, Microsoft, and Facebook, discussions increasingly focus on the need to ensure application performance under various uncertainties. Through the infrastructureas-a-service (IaaS) and platform-as-a-service (PaaS) concepts, datacenters virtualize their hardware and software resources and rent it on demand. In the cloud computing approach, multiple datacen

Information Sciences, 2015

IEEE Cloud Computing, 2014

IEEE Systems Journal, 2015

With the increasing complexity of both data structures and computer architectures, the performanc... more With the increasing complexity of both data structures and computer architectures, the performance of applications needs fine tuning in order to achieve the expected runtime execution time. Performance tuning is traditionally based on the analysis of performance data. The analysis results may not be accurate, depending on the quality of the data and the applied analysis approaches. Therefore, application developers may ask: Can we trust the analysis results? This paper introduces our research work in performance optimization of the memory system, with a focus on the cache locality of a shared memory and the memory locality of a distributed shared memory. The quality of the data analysis is guaranteed by using both real performance data acquired at the runtime while the application is running and well-established data analysis algorithms in the field of bioinformatics and data mining. We verified the quality of the proposed approaches by optimizing a set of benchmark applications. The experimental results show a significant performance gain.

The Knowledge Engineering Review, 2015

Intelligent computing in large-scale systems provides systematic methodologies and tools for buil... more Intelligent computing in large-scale systems provides systematic methodologies and tools for building complex inferential systems, which are able to adapt, mine data sets, evolve, and act in a nimble manner within major distributed environments with diverse architectures featuring multiple cores, accelerators, and high-speed networks.We believe that the papers presented in this special issue ought to serve as a reference for students, researchers, and industry practitioners interested in the evolving, interdisciplinary area of intelligent computing in large-scale systems. We very much hope that readers will find in this compendium new inspiration and ideas to enhance their own research.

Distributed and Parallel Databases, 2015

ABSTRACT As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume,... more ABSTRACT As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

Studies in Computational Intelligence, 2012

This chapter presents the results of comprehensive empirical evaluation of hierarchical, hybrid, ... more This chapter presents the results of comprehensive empirical evaluation of hierarchical, hybrid, single-and multi-population genetic metaheuristics in static and dynamic versions of the scheduling problem in grid. All metaheuristics have been integrated with the Sim-G-Batch grid simulator. The results of the analysis show the high effectiveness of HGS-Sched in exploration of the bi-objective dynamic optimization landscapes in highly-parametrized grids.

Studies in Computational Intelligence, 2012

This chapter introduces the application of the Hierarchical Genetic Strategy-based Grid scheduler... more This chapter introduces the application of the Hierarchical Genetic Strategy-based Grid scheduler (HGS-Sched) to the energy-aware independent batch scheduling problem in Computational Grids (CGs). The Dynamic Voltage Scaling (DVS) methodology is used for both scaling the power supply of the grid resources and reducing the cumulative power energy utilized by the grid computing machines. Two implementations of HGS-Sched-with elitist and struggle replacement mechanisms respectively-are defined and empirically evaluated. The effectiveness of the hierarchical schedulers are compared with the quality of single-population Genetic Algorithms (GAs) and Island GA models for four CG significant scenarios in static and dynamic modes. The simulation results show that meta-heuristic grid schedulers can significantly reduce the energy consumption in the system as well as be easily adapted to various scheduling scenarios.

Studies in Computational Intelligence, 2012

This chapter presents two non-cooperative game approaches, namely the symmetric non-zero sum game... more This chapter presents two non-cooperative game approaches, namely the symmetric non-zero sum game and asymmetric Stackelberg game, for modelling the grid users' behavior. These models allow to illustrate new scenarios in scheduling and resource allocation problems, such as asymmetric users' relations, security and reliability restrictions in computational grids (CGs). Four GA-based hybrid schedulers are implemented for the approximation of the equilibrium states of both games. The proposed hybrid resolution methods are empirically evaluated through the grid simulator under the heterogeneity, security, large-scale and dynamics conditions.

Lecture Notes in Computer Science, 2002

We present Hierarchical Genetic Strategy (HGS) as a family of Markov chains applying Vose's mathe... more We present Hierarchical Genetic Strategy (HGS) as a family of Markov chains applying Vose's mathematical model for Simple Genetic Algorithm. Studying its asymptotic properties and performing simply experiments we try to compare efficiency of HGS and sequential genetic algorithms.

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)

Looking at the rapid development of computer networks, it can be said that the transmission quali... more Looking at the rapid development of computer networks, it can be said that the transmission quality assurance is very important issue. In the past there were attempts to implement Quality of Service (QoS) techniques when using various network technologies. However QoS parameters are not always assured. This paper presents a novel concept of transmission quality determination based on Machine Learning (ML) methods. Transmission quality is determined by four parameters-delay, jitter, bandwidth and packet loss ratio. The concept of transmission quality assured network proposed by Pay&Require was presented as a novel multi-agent approach for QoS based computer networks. In this concept the essential part is transmission quality rating which is done based on transmission parameters by ML techniques. Data set was obtained based on the experience of the users test group. For our research we designed a machine learning system for transmission quality assessment. We obtained promising results using four classifiers: Nu-Support Vector Classifier (Nu-SVC), C-Support Vector Classifier (C-SVC), Random Forest Classifier, and K-Nearest Neighbors (kNN) algorithm. Classification results for different methods are presented together with confusion matrices. The best result, 87% sensitivity (overall accuracy), for the test set of data, was achieved by Nu-SVC and Random Forest (13/100 incorrect classifications).

Studies in Big Data

In this chapter, the k-anonymity algorithm is used for anonymization of sensitive data sending vi... more In this chapter, the k-anonymity algorithm is used for anonymization of sensitive data sending via network and analyzed by experts. Anonymization is a technique used to generalize sensitive data to block the possibility of assigning them to specific individuals or entities. In our proposed model, we have developed a layer that enables virtualization of sensitive data, ensuring that they are transmitted safely over the network and analyzed with respects the protection of personal data. Solution has been verified in real use case for transmission sports data to the experts who send the diagnosis as a response.

Journal of telecommunications and information technology, 2016

Stackelberg games are non-symmetric games where one player or specified group of players have the... more Stackelberg games are non-symmetric games where one player or specified group of players have the privilege position and make decision before the other players. Such games are used in telecommunication and computational systems for supporting administrative decisions. Recently Stackleberg games became useful also in the systems where security issues are the crucial decision criteria. In this paper authors briefly survey the most popular Stackelberg security game models and provide the analysis of the model properties illustrated in the realistic use cases. Keywords—Bayesian games, game theory, leadership, Nash equilibrium, normal form games, security games, Stackelberg equilibrium, Stackelberg games.

The intensive development and growth in the popularity of mobile cloud computing services bring a... more The intensive development and growth in the popularity of mobile cloud computing services bring a critical need to introduce new solutions that increase the level of cloud and users security. One of the critical issues in highly distributed computational systems is a task scheduling process. This process may be exposed to many external and internal security threats, like task injection, machine failure or generation of incorrect schedule. These problems are especially important in mobile environments. It can be even more complicated if we take into consideration the personalization of the services offered. Recently, blockchain has been gaining rapidly in popularity, combining high efficiency with applications in distributed and highly personalized computational environments. In this paper, we developed and described a novel model for security-aware task scheduling in cloud computing based on blockchain technology. Unlike other blockchain-based solutions, the proposed model uses Proo...

This chapter summarizes our recent research on game-theoretical models of grid users’ behavior in... more This chapter summarizes our recent research on game-theoretical models of grid users’ behavior in security aware scheduling. The main scheduling attributes, generic model of security aware management in local grid clusters and several scenarios of users’ games are presented. Four GA-based hybrid schedulers have been implemented for the approximation of the equilibrium states of exemplary simple symmetric game of the grid end users. The proposed hybrid resolution methLarge Scale Network-Centric Computing Systems, 1st edition. By Albert Y. Zomaya and Hamid Sarbazi-Azad Copyright c ⃝ 2012 John Wiley & Sons, Inc. 1 2 GAME-BASED MODELS OF GRID USERS’ DECISIONS... ods are empirically evaluated through the grid simulator under the heterogeneity, security, large-scale and dynamics conditions.

Journal of Parallel and Distributed Computing

Abstract Cloud computing (CC) systems are the most popular computational environments for providi... more Abstract Cloud computing (CC) systems are the most popular computational environments for providing elastic and scalable services on a massive scale. The nature of such systems often results in energy-related problems that have to be solved for sustainability, cost reduction, and environment protection. In this paper we defined and developed a set of performance and energy-aware strategies for resource allocation, task scheduling, and for the hibernation of virtual machines. The idea behind this model is to combine energy and performance-aware scheduling policies in order to hibernate those virtual machines that operate in idle state. The efficiency achieved by applying the proposed models has been tested using a realistic large-scale CC system simulator. Obtained results show that a balance between low energy consumption and short makespan can be achieved. Several security constraints may be considered in this model. Each security constraint is characterized by: (a) Security Demands (SD) of tasks; and (b) Trust Levels (TL) provided by virtual machines. SD and TL are computed during the scheduling process in order to provide proper security services. Experimental results show that the proposed solution reduces up to 45% of the energy consumption of the CC system. Such significant improvement was achieved by the combination of an energy-aware scheduler with energy-efficiency policies focused on the hibernation of VMs.

Evolutionary Hierachical Multi Criteria Metaheuristics For Schheduling in Large Scale Grid System Joanna Kolodziej, 2012

Concurrency and Computation: Practice and Experience, 2016

Journal for his support and advises, as well as to the editorial and managerial journal team from... more Journal for his support and advises, as well as to the editorial and managerial journal team from Wiley for their assistance and excellent cooperative collaboration to produce this valuable scientific work.

International Journal of Applied Mathematics and Computer Science, 2015

When there is a mismatch between the cardinality of a periodic task set and the priority levels s... more When there is a mismatch between the cardinality of a periodic task set and the priority levels supported by the underlying hardware systems, multiple tasks are grouped into one class so as to maintain a specific level of confidence in their accuracy. However, such a transformation is achieved at the expense of the loss of schedulability of the original task set. We further investigate the aforementioned problem and report the following contributions: (i) a novel technique for mapping unlimited priority tasks into a reduced number of classes that do not violate the schedulability of the original task set and (ii) an efficient feasibility test that eliminates insufficient points during the feasibility analysis. The theoretical correctness of both contributions is checked through formal verifications. Moreover, the experimental results reveal the superiority of our work over the existing feasibility tests by reducing the number of scheduling points that are needed otherwise.

IEEE Cloud Computing, 2015

he emergence of cloud computing has facilitated resource sharing beyond organizational boundaries... more he emergence of cloud computing has facilitated resource sharing beyond organizational boundaries and among various applications. This cloud resource sharing is primarily driven by resource virtualization and utility computing (the pay-as-you-go pricing model). The generic multilayered cloud service model is appealing to many parties-from small businesses looking for a low upfront infrastructure investment, to enterprises wanting to cut the cost of managing infrastructures, to research communities requiring large-scale data processing and computing power. In a cloud environment, computing resources (processors, storage devices, network bandwidth, and so on) and applications are provided as services over the Internet. Fueled by an insatiable demand for new Internet services and a shift to cloud computing services that are largely hosted in commercial datacenters and in the large data farms operated by companies like Amazon, Apple, Google, Microsoft, and Facebook, discussions increasingly focus on the need to ensure application performance under various uncertainties. Through the infrastructureas-a-service (IaaS) and platform-as-a-service (PaaS) concepts, datacenters virtualize their hardware and software resources and rent it on demand. In the cloud computing approach, multiple datacen

Information Sciences, 2015

IEEE Cloud Computing, 2014

IEEE Systems Journal, 2015

With the increasing complexity of both data structures and computer architectures, the performanc... more With the increasing complexity of both data structures and computer architectures, the performance of applications needs fine tuning in order to achieve the expected runtime execution time. Performance tuning is traditionally based on the analysis of performance data. The analysis results may not be accurate, depending on the quality of the data and the applied analysis approaches. Therefore, application developers may ask: Can we trust the analysis results? This paper introduces our research work in performance optimization of the memory system, with a focus on the cache locality of a shared memory and the memory locality of a distributed shared memory. The quality of the data analysis is guaranteed by using both real performance data acquired at the runtime while the application is running and well-established data analysis algorithms in the field of bioinformatics and data mining. We verified the quality of the proposed approaches by optimizing a set of benchmark applications. The experimental results show a significant performance gain.

The Knowledge Engineering Review, 2015

Intelligent computing in large-scale systems provides systematic methodologies and tools for buil... more Intelligent computing in large-scale systems provides systematic methodologies and tools for building complex inferential systems, which are able to adapt, mine data sets, evolve, and act in a nimble manner within major distributed environments with diverse architectures featuring multiple cores, accelerators, and high-speed networks.We believe that the papers presented in this special issue ought to serve as a reference for students, researchers, and industry practitioners interested in the evolving, interdisciplinary area of intelligent computing in large-scale systems. We very much hope that readers will find in this compendium new inspiration and ideas to enhance their own research.

Distributed and Parallel Databases, 2015

ABSTRACT As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume,... more ABSTRACT As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

Studies in Computational Intelligence, 2012

This chapter presents the results of comprehensive empirical evaluation of hierarchical, hybrid, ... more This chapter presents the results of comprehensive empirical evaluation of hierarchical, hybrid, single-and multi-population genetic metaheuristics in static and dynamic versions of the scheduling problem in grid. All metaheuristics have been integrated with the Sim-G-Batch grid simulator. The results of the analysis show the high effectiveness of HGS-Sched in exploration of the bi-objective dynamic optimization landscapes in highly-parametrized grids.

Studies in Computational Intelligence, 2012

This chapter introduces the application of the Hierarchical Genetic Strategy-based Grid scheduler... more This chapter introduces the application of the Hierarchical Genetic Strategy-based Grid scheduler (HGS-Sched) to the energy-aware independent batch scheduling problem in Computational Grids (CGs). The Dynamic Voltage Scaling (DVS) methodology is used for both scaling the power supply of the grid resources and reducing the cumulative power energy utilized by the grid computing machines. Two implementations of HGS-Sched-with elitist and struggle replacement mechanisms respectively-are defined and empirically evaluated. The effectiveness of the hierarchical schedulers are compared with the quality of single-population Genetic Algorithms (GAs) and Island GA models for four CG significant scenarios in static and dynamic modes. The simulation results show that meta-heuristic grid schedulers can significantly reduce the energy consumption in the system as well as be easily adapted to various scheduling scenarios.

Studies in Computational Intelligence, 2012

This chapter presents two non-cooperative game approaches, namely the symmetric non-zero sum game... more This chapter presents two non-cooperative game approaches, namely the symmetric non-zero sum game and asymmetric Stackelberg game, for modelling the grid users' behavior. These models allow to illustrate new scenarios in scheduling and resource allocation problems, such as asymmetric users' relations, security and reliability restrictions in computational grids (CGs). Four GA-based hybrid schedulers are implemented for the approximation of the equilibrium states of both games. The proposed hybrid resolution methods are empirically evaluated through the grid simulator under the heterogeneity, security, large-scale and dynamics conditions.

Lecture Notes in Computer Science, 2002

We present Hierarchical Genetic Strategy (HGS) as a family of Markov chains applying Vose's mathe... more We present Hierarchical Genetic Strategy (HGS) as a family of Markov chains applying Vose's mathematical model for Simple Genetic Algorithm. Studying its asymptotic properties and performing simply experiments we try to compare efficiency of HGS and sequential genetic algorithms.