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

Papers by Joanna Kolodziej

ECMS 2018 Proceedings edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen, 2018

Assuring the security of services in Computational Clouds (CC) is one of the most critical factor... more Assuring the security of services in Computational Clouds (CC) is one of the most critical factors in cloud computing. However, it can complicate an already complex environment due to the complexity of the system architecture, the huge number of services, and the required storage management. In real systems, some security parameters of CC are manually set, which can be very time-consuming and requires security expertise. This paper proposes an intelligent system to support decisions regarding security and tasks scheduling in cloud services, which aims at automating these processes. This system comprises two different kinds of Artificial Neural Networks (ANN) and an evolutionary algorithm, and has as main goal sorting tasks incoming into CC according to their security demands. Trust levels of virtual machines (VMs) in the environment are automatically set to meet the tasks security demands. Tasks are then scheduled on VMs optimizing the makespan and ensuring that their security requirements are fulfilled. The paper also describes tests assessing the best configurations for the system components, using randomly generated batches of tasks. Results are presented and discussed. The proposed system may be used by CC service providers and CC consumers using Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) Cloud Computing models.

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

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).

Journal of telecommunications and information technology, 2017

This paper presents an overview of techniques developed to improve energy efficiency of grid and ... more This paper presents an overview of techniques developed to improve energy efficiency of grid and cloud computing. Power consumption models and energy usage profiles are presented together with energy efficiency measuring methods. Modeling of computing dynamics is discussed from the viewpoint of system identification theory, indicating basic experiment design problems and challenges. Novel approaches to cluster and network-wide energy usage optimization are surveyed, including multi-level power and software control systems, energy-aware task scheduling, resource allocation algorithms and frameworks for backbone networks management. Software-development techniques and tools are also presented as a new promising way to reduce power consumption at the computing node level. Finally, energy-aware control mechanisms are presented. In addition, this paper introduces the example of batch scheduler based on ETC matrix approach.

ECMS 2018 Proceedings edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen, 2018

Energy-awareness remians the important problem in today's cloud computing (CC). Optimization of t... more Energy-awareness remians the important problem in today's cloud computing (CC). Optimization of the energy consumed in cloud data centers and computing servers is usually related to the scheduling problems. It is very difficult to define an optimal scheduling policy without negoative influence into the system performance and task completion time. In this work, we define a general cloud scheduling model based on a Stackelberg game with the workload scheduler and energy-efficiency agent as the main players. In this game, the aim of the scheduler is the minimization of the makespan of the workload, which is achieved by the employ of a genetic scheduling algorithm that maps the workload tasks into the computational nodes. The energy-efficiency agent selects the energy-optimization techniques based on the idea of switchin-off of the idle machines, in response to the scheduler decisions. The efficiency of the proposed model has been tested using a SCORE cloud simmulator. Obtained results show that the proposed model performs better than static energy-optimization strategies, achieving a fair balance between low energy consumption and short queue times and makespan.

Proceedings of the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, 2017

Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comp... more Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comprise general purpose CPUs and accelerators (such as, GPU, or Intel Xeon Phi) that provide high performance with suitable energy-consumption characteristics. However, exploiting the available performance of heterogeneous architectures may be challenging. There are various parallel programming frameworks (such as, OpenMP, OpenCL, OpenACC, CUDA) and selecting the one that is suitable for a target context is not straightforward. In this paper, we study empirically the characteristics of OpenMP, OpenACC, OpenCL, and CUDA with respect to programming productivity, performance, and energy. To evaluate the programming productivity we use our homegrown tool CodeStat, which enables us to determine the percentage of code lines that was required to parallelize the code using a specific framework. We use our tool x-MeterPU to evaluate the energy consumption and the performance. Experiments are conducted using the industry-standard SPEC benchmark suite and the Rodinia benchmark suite for accelerated computing on heterogeneous systems that combine Intel Xeon E5 Processors with a GPU accelerator or an Intel Xeon Phi co-processor.

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

In this paper we present an original adaptive task scheduling system, which optimizes the energy ... more In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that due to the experience gathered and the learning process performed, the decision module has consequently become more efficient in assigning the task to either the mobile device or cloud resources. In face of presented improvements, the security issues inherent within the context of mobile application and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often preventing the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering the performance.

Simulation Modelling Practice and Theory, 2019

This paper presents a generic model of the energy aware, secure sensing and computing system comp... more This paper presents a generic model of the energy aware, secure sensing and computing system composed of clusters comprised of static and mobile wireless sensors. The architecture of the modelled system is based on the paradigms of edge and fog computing. The data collected by all sensing devices (edge devices) located in each cluster is preprocessed and stored at the edge closures and routed over the wireless network to a base station, i.e. a gateway of a cluster. The local aggregation, analysis and essential computing tasks are performed by the clusters' gateways. Finally, the results of these operations are sent through the backbone network to the cloud data center for data fusion, correlation and further computing based on the data gathered from the all sensing clusters. The proposed edge and fog implementation can significantly offload of the cloud data centers and improve the security aspects in data processing. We point out that due to limited computing and energy resources of edge devices effective deployment of sensors and power management are vital design issues that need to be boosted in order to carry out a substantial amount of computation, increase the lifespan of a sensing system and ensure high quality monitoring. We overview various approaches to deployment of sensing devices in a workspace and discuss the issues related to the energy aware and secure communication. The results of the evaluation of the performance of the selected energy conservation techniques through simulation and experiments conducted in testbed networks are presented and discussed.

Journal of Parallel and Distributed Computing, 2018

h i g h l i g h t s • We propose energy-efficiency strategies for task scheduling and hibernating... more h i g h l i g h t s • We propose energy-efficiency strategies for task scheduling and hibernating VMs. • We combine energy and time-based criteria in order to sleep idle resources. • We take into account several security constraints in our model. • The effectiveness of the proposed model has been confirmed by simulation experiments.

International Journal of Applied Mathematics and Computer Science, 2017

With the rapid evolution of the distributed computing world in the last few years, the amount of ... more With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.

Simulation Modelling Practice and Theory, 2017

Acknowledgement and Disclaimer This publication is based upon work from the COST Action IC1406 Hi... more Acknowledgement and Disclaimer This publication is based upon work from the COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), supported by COST (European Cooperation in Science and Technology). The book reflects only the authors' views. Neither the COST Association nor any person acting on its behalf is responsible for the use, which might be made of the information contained in this publication. The COST Association is not responsible for external websites or sources referred to in this publication.

Distributed and Parallel Databases, 2015

As we delve deeper into the 'Digital Age', we witness an explosive growth in the volume, velocity... more 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 mobiledevices, sensors, individual

Devices that form a wireless sensor network (WSN) system are usually remotely deployed in large n... more Devices that form a wireless sensor network (WSN) system are usually remotely deployed in large numbers in a sensing field. WSNs have enabled numerous applications, in which location awareness is usually required. Therefore, numerous localization systems are provided to assign geographic coordinates to each node in a network. In this paper, we describe and evaluate an integrated software framework WSNLS (Wireless Sensor Network Localization System) that provides tools for network nodes localization and the environment for tuning and testing various localization schemes. Simulation experiments can be performed on parallel and multi-core computers or computer clusters. The main component of the WSNLS framework is the library of solvers for calculating the geographic coordinates of nodes in a network. Our original solution implemented in WSNLS is the localization system that combines simple geometry of triangles and stochastic opti-M. Marks, E. Niewiadomska-Szynkiewicz, J. Ko lodziej mization to determine the position of nodes with unknown location in the sensing field. We describe and discuss the performance of our system due to the accuracy of location estimation and computation time. Numerical results presented in the paper confirm that our hybrid scheme gives accurate location estimates of network nodes in sensible computing time, and the WSNLS framework can be successfully used for efficient tuning and verification of different localization techniques.

ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann, 2012

Global optimization of the energy consumption in heterogeneous environments has been recently an ... more Global optimization of the energy consumption in heterogeneous environments has been recently an important research issue in wired and wireless networks. This paper presents a general framework for flexible and cognitive backbone network management which leads to the minimization of the energy utilized by the network. The policy for activity control of all the modules and elements that form a network is introduced and discussed. The idea of the system is to achieve the desired trade-off between energy consumption and network performance according to the traffic load.

ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann, 2012

Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregat... more Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregate bandwidth requirement is a major bottleneck to data center performance. New Data Center Network (DCN) architectures are proposed to handle different challenges faced by current DCN architecture. In this paper we have implemented and simulated two promising DCN architectural models, namely switch-based and hybrid models, and compared their effectiveness by monitoring the network throughputs and average packet latencies. The presented analysis may be a background for the further studies on the simulation and implementation of the DCN customized topologies, and customized addressing protocols in the large-scale data centers.

International Journal of Ad Hoc and Ubiquitous Computing, 2014

In this paper we summarize the results of our research concerned with the development, implementa... more In this paper we summarize the results of our research concerned with the development, implementation and evaluation of a software framework for wireless sensor networks localization-High Performance Localization System (HPLS). The system can be used to calculate positions of sensing devices (network nodes) in the deployment area, and to tune and verify various localization schemes through simulation. It provides tools for data acquisition from a workspace, estimation of inter-node distances, calculation of geographical coordinates of all nodes with unknown position and results evaluation. Received Signal Strength measurements are utilized to support the localization process. Trilateration, simulated annealing and genetic algorithm are applied to calculate the geographical coordinates of network nodes. The utility, efficiency and scalability of the proposed localization system HPLS have been justified through simulation and testbed implementation. The calculations have been done in parallel using the map-reduce paradigm and the HPC environment formed by a cluster of servers. The testbed networks were formed by sensor devices manufactured by Advantic Technology (clones of TelosB platform). A provided case study demonstrates the localization accuracy obtained for small-, medium-and large-size multihop networks.

Data-aware scheduling in today's large-scale heterogeneous environments has become a major resear... more Data-aware scheduling in today's large-scale heterogeneous environments has become a major research issue. Data Grids (DGs) and Data Centers arise quite naturally to support needs of scientific communities to share, access, process, and manage large data collections geographically distributed. Data scheduling, although similar in nature with grid scheduling, is given rise to the definition of a new family of optimization problems. New requirements such as data transmission, decoupling of data from processing, data replication, data access and security are to be added to the scheduling problem are the basis for the definition of a whole taxonomy of data scheduling problems. In this paper we briefly survey the state-of-the-art in the domain. We exemplify the model and methodology for the case of data-aware independent job scheduling in computational grid and present several heuristic resolution methods for the problem.

The Information and Communication Technology sector is considered to be a major consumer of energ... more The Information and Communication Technology sector is considered to be a major consumer of energy and has become an active participant in Green House Gas

2013 11th International Conference on Frontiers of Information Technology, 2013

Data-aware scheduling in today's large-scale computing systems has become a major complex researc... more Data-aware scheduling in today's large-scale computing systems has become a major complex research issue. This problem becomes even more challenging when data is stored and accessed from many highly distributed servers and energy-efficiency is treated as a main scheduling objective. In this paper we approach the independent batch scheduling in grid environment as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We used the Dynamic Voltage and Frequency Scaling (DVFS) model for reducing the cumulative power energy utilized by the system resources for tasks executions. We developed for data transmission a general logical network topology and policy based on the sleep link-based Adaptive Link Rate (ALR) on/off technique. Two developed energy-aware grid schedulers are based on genetic algorithms (GAs) frameworks with elitist and struggle replacement mechanisms and were empirically evaluated for four grid size scenarios in static and dynamic modes. The simulation results show that the proposed schedulers perform to a level that is sufficient to maintain the desired quality levels.

Computing, 2014

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources... more In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious

ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho, 2011

We present a simulation model designed for evaluation of dependability in distributed systems. Th... more We present a simulation model designed for evaluation of dependability in distributed systems. The model is a modification of the MONARC simulation model by adding new capabilities for capturing the reliability, safety, availability, security, and maintainability requirements. It includes components for failures injection, and it provides evaluation mechanisms for different replication strategies, redundancy procedures, and security enforcement mechanisms. The model is implemented as an extension of the multi-threaded, process oriented simulator MONARC, which allows the realistic simulation of a wide-range of distributed system technologies, with respect to their specific components and characteristics. The experimental results show that the application of the discrete-event simulators in the design and development of the dependable distributed systems is appealing due to their efficiency and scalability

ECMS 2018 Proceedings edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen, 2018

Assuring the security of services in Computational Clouds (CC) is one of the most critical factor... more Assuring the security of services in Computational Clouds (CC) is one of the most critical factors in cloud computing. However, it can complicate an already complex environment due to the complexity of the system architecture, the huge number of services, and the required storage management. In real systems, some security parameters of CC are manually set, which can be very time-consuming and requires security expertise. This paper proposes an intelligent system to support decisions regarding security and tasks scheduling in cloud services, which aims at automating these processes. This system comprises two different kinds of Artificial Neural Networks (ANN) and an evolutionary algorithm, and has as main goal sorting tasks incoming into CC according to their security demands. Trust levels of virtual machines (VMs) in the environment are automatically set to meet the tasks security demands. Tasks are then scheduled on VMs optimizing the makespan and ensuring that their security requirements are fulfilled. The paper also describes tests assessing the best configurations for the system components, using randomly generated batches of tasks. Results are presented and discussed. The proposed system may be used by CC service providers and CC consumers using Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) Cloud Computing models.

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

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).

Journal of telecommunications and information technology, 2017

This paper presents an overview of techniques developed to improve energy efficiency of grid and ... more This paper presents an overview of techniques developed to improve energy efficiency of grid and cloud computing. Power consumption models and energy usage profiles are presented together with energy efficiency measuring methods. Modeling of computing dynamics is discussed from the viewpoint of system identification theory, indicating basic experiment design problems and challenges. Novel approaches to cluster and network-wide energy usage optimization are surveyed, including multi-level power and software control systems, energy-aware task scheduling, resource allocation algorithms and frameworks for backbone networks management. Software-development techniques and tools are also presented as a new promising way to reduce power consumption at the computing node level. Finally, energy-aware control mechanisms are presented. In addition, this paper introduces the example of batch scheduler based on ETC matrix approach.

ECMS 2018 Proceedings edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen, 2018

Energy-awareness remians the important problem in today's cloud computing (CC). Optimization of t... more Energy-awareness remians the important problem in today's cloud computing (CC). Optimization of the energy consumed in cloud data centers and computing servers is usually related to the scheduling problems. It is very difficult to define an optimal scheduling policy without negoative influence into the system performance and task completion time. In this work, we define a general cloud scheduling model based on a Stackelberg game with the workload scheduler and energy-efficiency agent as the main players. In this game, the aim of the scheduler is the minimization of the makespan of the workload, which is achieved by the employ of a genetic scheduling algorithm that maps the workload tasks into the computational nodes. The energy-efficiency agent selects the energy-optimization techniques based on the idea of switchin-off of the idle machines, in response to the scheduler decisions. The efficiency of the proposed model has been tested using a SCORE cloud simmulator. Obtained results show that the proposed model performs better than static energy-optimization strategies, achieving a fair balance between low energy consumption and short queue times and makespan.

Proceedings of the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, 2017

Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comp... more Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comprise general purpose CPUs and accelerators (such as, GPU, or Intel Xeon Phi) that provide high performance with suitable energy-consumption characteristics. However, exploiting the available performance of heterogeneous architectures may be challenging. There are various parallel programming frameworks (such as, OpenMP, OpenCL, OpenACC, CUDA) and selecting the one that is suitable for a target context is not straightforward. In this paper, we study empirically the characteristics of OpenMP, OpenACC, OpenCL, and CUDA with respect to programming productivity, performance, and energy. To evaluate the programming productivity we use our homegrown tool CodeStat, which enables us to determine the percentage of code lines that was required to parallelize the code using a specific framework. We use our tool x-MeterPU to evaluate the energy consumption and the performance. Experiments are conducted using the industry-standard SPEC benchmark suite and the Rodinia benchmark suite for accelerated computing on heterogeneous systems that combine Intel Xeon E5 Processors with a GPU accelerator or an Intel Xeon Phi co-processor.

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

In this paper we present an original adaptive task scheduling system, which optimizes the energy ... more In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that due to the experience gathered and the learning process performed, the decision module has consequently become more efficient in assigning the task to either the mobile device or cloud resources. In face of presented improvements, the security issues inherent within the context of mobile application and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often preventing the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering the performance.

Simulation Modelling Practice and Theory, 2019

This paper presents a generic model of the energy aware, secure sensing and computing system comp... more This paper presents a generic model of the energy aware, secure sensing and computing system composed of clusters comprised of static and mobile wireless sensors. The architecture of the modelled system is based on the paradigms of edge and fog computing. The data collected by all sensing devices (edge devices) located in each cluster is preprocessed and stored at the edge closures and routed over the wireless network to a base station, i.e. a gateway of a cluster. The local aggregation, analysis and essential computing tasks are performed by the clusters' gateways. Finally, the results of these operations are sent through the backbone network to the cloud data center for data fusion, correlation and further computing based on the data gathered from the all sensing clusters. The proposed edge and fog implementation can significantly offload of the cloud data centers and improve the security aspects in data processing. We point out that due to limited computing and energy resources of edge devices effective deployment of sensors and power management are vital design issues that need to be boosted in order to carry out a substantial amount of computation, increase the lifespan of a sensing system and ensure high quality monitoring. We overview various approaches to deployment of sensing devices in a workspace and discuss the issues related to the energy aware and secure communication. The results of the evaluation of the performance of the selected energy conservation techniques through simulation and experiments conducted in testbed networks are presented and discussed.

Journal of Parallel and Distributed Computing, 2018

h i g h l i g h t s • We propose energy-efficiency strategies for task scheduling and hibernating... more h i g h l i g h t s • We propose energy-efficiency strategies for task scheduling and hibernating VMs. • We combine energy and time-based criteria in order to sleep idle resources. • We take into account several security constraints in our model. • The effectiveness of the proposed model has been confirmed by simulation experiments.

International Journal of Applied Mathematics and Computer Science, 2017

With the rapid evolution of the distributed computing world in the last few years, the amount of ... more With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.

Simulation Modelling Practice and Theory, 2017

Acknowledgement and Disclaimer This publication is based upon work from the COST Action IC1406 Hi... more Acknowledgement and Disclaimer This publication is based upon work from the COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), supported by COST (European Cooperation in Science and Technology). The book reflects only the authors' views. Neither the COST Association nor any person acting on its behalf is responsible for the use, which might be made of the information contained in this publication. The COST Association is not responsible for external websites or sources referred to in this publication.

Distributed and Parallel Databases, 2015

As we delve deeper into the 'Digital Age', we witness an explosive growth in the volume, velocity... more 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 mobiledevices, sensors, individual

Devices that form a wireless sensor network (WSN) system are usually remotely deployed in large n... more Devices that form a wireless sensor network (WSN) system are usually remotely deployed in large numbers in a sensing field. WSNs have enabled numerous applications, in which location awareness is usually required. Therefore, numerous localization systems are provided to assign geographic coordinates to each node in a network. In this paper, we describe and evaluate an integrated software framework WSNLS (Wireless Sensor Network Localization System) that provides tools for network nodes localization and the environment for tuning and testing various localization schemes. Simulation experiments can be performed on parallel and multi-core computers or computer clusters. The main component of the WSNLS framework is the library of solvers for calculating the geographic coordinates of nodes in a network. Our original solution implemented in WSNLS is the localization system that combines simple geometry of triangles and stochastic opti-M. Marks, E. Niewiadomska-Szynkiewicz, J. Ko lodziej mization to determine the position of nodes with unknown location in the sensing field. We describe and discuss the performance of our system due to the accuracy of location estimation and computation time. Numerical results presented in the paper confirm that our hybrid scheme gives accurate location estimates of network nodes in sensible computing time, and the WSNLS framework can be successfully used for efficient tuning and verification of different localization techniques.

ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann, 2012

Global optimization of the energy consumption in heterogeneous environments has been recently an ... more Global optimization of the energy consumption in heterogeneous environments has been recently an important research issue in wired and wireless networks. This paper presents a general framework for flexible and cognitive backbone network management which leads to the minimization of the energy utilized by the network. The policy for activity control of all the modules and elements that form a network is introduced and discussed. The idea of the system is to achieve the desired trade-off between energy consumption and network performance according to the traffic load.

ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann, 2012

Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregat... more Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregate bandwidth requirement is a major bottleneck to data center performance. New Data Center Network (DCN) architectures are proposed to handle different challenges faced by current DCN architecture. In this paper we have implemented and simulated two promising DCN architectural models, namely switch-based and hybrid models, and compared their effectiveness by monitoring the network throughputs and average packet latencies. The presented analysis may be a background for the further studies on the simulation and implementation of the DCN customized topologies, and customized addressing protocols in the large-scale data centers.

International Journal of Ad Hoc and Ubiquitous Computing, 2014

In this paper we summarize the results of our research concerned with the development, implementa... more In this paper we summarize the results of our research concerned with the development, implementation and evaluation of a software framework for wireless sensor networks localization-High Performance Localization System (HPLS). The system can be used to calculate positions of sensing devices (network nodes) in the deployment area, and to tune and verify various localization schemes through simulation. It provides tools for data acquisition from a workspace, estimation of inter-node distances, calculation of geographical coordinates of all nodes with unknown position and results evaluation. Received Signal Strength measurements are utilized to support the localization process. Trilateration, simulated annealing and genetic algorithm are applied to calculate the geographical coordinates of network nodes. The utility, efficiency and scalability of the proposed localization system HPLS have been justified through simulation and testbed implementation. The calculations have been done in parallel using the map-reduce paradigm and the HPC environment formed by a cluster of servers. The testbed networks were formed by sensor devices manufactured by Advantic Technology (clones of TelosB platform). A provided case study demonstrates the localization accuracy obtained for small-, medium-and large-size multihop networks.

Data-aware scheduling in today's large-scale heterogeneous environments has become a major resear... more Data-aware scheduling in today's large-scale heterogeneous environments has become a major research issue. Data Grids (DGs) and Data Centers arise quite naturally to support needs of scientific communities to share, access, process, and manage large data collections geographically distributed. Data scheduling, although similar in nature with grid scheduling, is given rise to the definition of a new family of optimization problems. New requirements such as data transmission, decoupling of data from processing, data replication, data access and security are to be added to the scheduling problem are the basis for the definition of a whole taxonomy of data scheduling problems. In this paper we briefly survey the state-of-the-art in the domain. We exemplify the model and methodology for the case of data-aware independent job scheduling in computational grid and present several heuristic resolution methods for the problem.

The Information and Communication Technology sector is considered to be a major consumer of energ... more The Information and Communication Technology sector is considered to be a major consumer of energy and has become an active participant in Green House Gas

2013 11th International Conference on Frontiers of Information Technology, 2013

Data-aware scheduling in today's large-scale computing systems has become a major complex researc... more Data-aware scheduling in today's large-scale computing systems has become a major complex research issue. This problem becomes even more challenging when data is stored and accessed from many highly distributed servers and energy-efficiency is treated as a main scheduling objective. In this paper we approach the independent batch scheduling in grid environment as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We used the Dynamic Voltage and Frequency Scaling (DVFS) model for reducing the cumulative power energy utilized by the system resources for tasks executions. We developed for data transmission a general logical network topology and policy based on the sleep link-based Adaptive Link Rate (ALR) on/off technique. Two developed energy-aware grid schedulers are based on genetic algorithms (GAs) frameworks with elitist and struggle replacement mechanisms and were empirically evaluated for four grid size scenarios in static and dynamic modes. The simulation results show that the proposed schedulers perform to a level that is sufficient to maintain the desired quality levels.

Computing, 2014

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources... more In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious

ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho, 2011

We present a simulation model designed for evaluation of dependability in distributed systems. Th... more We present a simulation model designed for evaluation of dependability in distributed systems. The model is a modification of the MONARC simulation model by adding new capabilities for capturing the reliability, safety, availability, security, and maintainability requirements. It includes components for failures injection, and it provides evaluation mechanisms for different replication strategies, redundancy procedures, and security enforcement mechanisms. The model is implemented as an extension of the multi-threaded, process oriented simulator MONARC, which allows the realistic simulation of a wide-range of distributed system technologies, with respect to their specific components and characteristics. The experimental results show that the application of the discrete-event simulators in the design and development of the dependable distributed systems is appealing due to their efficiency and scalability