The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform
Goran Velkoski - One of the best experts on this subject based on the ideXlab platform.
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windows azure resource organization performance analysis
European Conference on Service-Oriented and Cloud Computing, 2014Co-Authors: Marjan Gusev, Sashko Ristov, Bojana Koteska, Goran VelkoskiAbstract:Cloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing Virtual Machine Instance with additional resources, or by adding an additional Virtual Machine Instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” Instances or less ”large” Instances. The first hypothesis states that better performance is obtained by using more and smaller Instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger Instances results with better performance and that the user gets more performances than expected by scaling the resources.
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ESOCC - Windows Azure: Resource Organization Performance Analysis
Advanced Information Systems Engineering, 2014Co-Authors: Marjan Gusev, Sashko Ristov, Bojana Koteska, Goran VelkoskiAbstract:Cloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing Virtual Machine Instance with additional resources, or by adding an additional Virtual Machine Instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” Instances or less ”large” Instances. The first hypothesis states that better performance is obtained by using more and smaller Instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger Instances results with better performance and that the user gets more performances than expected by scaling the resources.
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EDUCON - Cloud E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course
2014 IEEE Global Engineering Education Conference (EDUCON), 2014Co-Authors: Sashko Ristov, Marjan Gusev, Goran VelkoskiAbstract:We have recently developed and implemented an E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course. Although this platform reduced the students' problems, we faced an additional problem with the lack of resources expressed in a specific timeframe just before homework deadline. In this paper we propose using a cloud based architecture of an e-Elearning system. It is intended to upgrade the e-Learning and benchmarking platform prototype into a scalable and elastic platform, where the system will send the execution on the Virtual Machine Instance hosted on the cloud node with available resources. Additionally, we propose a strategy for efficient utilisation of cloud resources is proposed.
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Cloud E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course
2014 Ieee Global Engineering Education Conference (Educon), 2014Co-Authors: Sashko Ristov, Marjan Gusev, Goran VelkoskiAbstract:We have recently developed and implemented an E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course. Although this platform reduced the students' problems, we faced an additional problem with the lack of resources expressed in a specific timeframe just before homework deadline. In this paper we propose using a cloud based architecture of an e-Elearning system. It is intended to upgrade the e-Learning and benchmarking platform prototype into a scalable and elastic platform, where the system will send the execution on the Virtual Machine Instance hosted on the cloud node with available resources. Additionally, we propose a strategy for efficient utilisation of cloud resources is proposed.
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IEEE CLOUD - Optimal Resource Allocation to Host Web Services in Cloud
2013 IEEE Sixth International Conference on Cloud Computing, 2013Co-Authors: Marjan Gusev, Sashko Ristov, Goran Velkoski, Monika SimjanoskaAbstract:In this paper, we analyze the performance of computation intensive and memory demanding web services hosted in different environments with the same amount of resources, but orchestrated differently. A single-VM addresses the environment where all the resources are allocated in one huge Virtual Machine Instance (VMI), while a multi-VM environment uses several smaller VMIs, each allocated with only one CPU core, and the load is balanced among them. We realize series of experiments with different server loads by changing the message size and the number of concurrent messages to analyze the optimal resource allocation to host web services in order to achieve maximum performance from the same resources in the cloud, i.e., for the same price. Despite the hypothesis that the single-VM environment provides better performance than the multi-VM environment, the results show totally opposite for almost all test cases. We achieve maximal relative speedup of 9.83 comparing the multi-VM environment to the single-VM.
Marjan Gusev - One of the best experts on this subject based on the ideXlab platform.
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windows azure resource organization performance analysis
European Conference on Service-Oriented and Cloud Computing, 2014Co-Authors: Marjan Gusev, Sashko Ristov, Bojana Koteska, Goran VelkoskiAbstract:Cloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing Virtual Machine Instance with additional resources, or by adding an additional Virtual Machine Instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” Instances or less ”large” Instances. The first hypothesis states that better performance is obtained by using more and smaller Instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger Instances results with better performance and that the user gets more performances than expected by scaling the resources.
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ESOCC - Windows Azure: Resource Organization Performance Analysis
Advanced Information Systems Engineering, 2014Co-Authors: Marjan Gusev, Sashko Ristov, Bojana Koteska, Goran VelkoskiAbstract:Cloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing Virtual Machine Instance with additional resources, or by adding an additional Virtual Machine Instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” Instances or less ”large” Instances. The first hypothesis states that better performance is obtained by using more and smaller Instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger Instances results with better performance and that the user gets more performances than expected by scaling the resources.
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EDUCON - Cloud E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course
2014 IEEE Global Engineering Education Conference (EDUCON), 2014Co-Authors: Sashko Ristov, Marjan Gusev, Goran VelkoskiAbstract:We have recently developed and implemented an E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course. Although this platform reduced the students' problems, we faced an additional problem with the lack of resources expressed in a specific timeframe just before homework deadline. In this paper we propose using a cloud based architecture of an e-Elearning system. It is intended to upgrade the e-Learning and benchmarking platform prototype into a scalable and elastic platform, where the system will send the execution on the Virtual Machine Instance hosted on the cloud node with available resources. Additionally, we propose a strategy for efficient utilisation of cloud resources is proposed.
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Cloud E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course
2014 Ieee Global Engineering Education Conference (Educon), 2014Co-Authors: Sashko Ristov, Marjan Gusev, Goran VelkoskiAbstract:We have recently developed and implemented an E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course. Although this platform reduced the students' problems, we faced an additional problem with the lack of resources expressed in a specific timeframe just before homework deadline. In this paper we propose using a cloud based architecture of an e-Elearning system. It is intended to upgrade the e-Learning and benchmarking platform prototype into a scalable and elastic platform, where the system will send the execution on the Virtual Machine Instance hosted on the cloud node with available resources. Additionally, we propose a strategy for efficient utilisation of cloud resources is proposed.
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IEEE CLOUD - Optimal Resource Allocation to Host Web Services in Cloud
2013 IEEE Sixth International Conference on Cloud Computing, 2013Co-Authors: Marjan Gusev, Sashko Ristov, Goran Velkoski, Monika SimjanoskaAbstract:In this paper, we analyze the performance of computation intensive and memory demanding web services hosted in different environments with the same amount of resources, but orchestrated differently. A single-VM addresses the environment where all the resources are allocated in one huge Virtual Machine Instance (VMI), while a multi-VM environment uses several smaller VMIs, each allocated with only one CPU core, and the load is balanced among them. We realize series of experiments with different server loads by changing the message size and the number of concurrent messages to analyze the optimal resource allocation to host web services in order to achieve maximum performance from the same resources in the cloud, i.e., for the same price. Despite the hypothesis that the single-VM environment provides better performance than the multi-VM environment, the results show totally opposite for almost all test cases. We achieve maximal relative speedup of 9.83 comparing the multi-VM environment to the single-VM.
Sashko Ristov - One of the best experts on this subject based on the ideXlab platform.
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windows azure resource organization performance analysis
European Conference on Service-Oriented and Cloud Computing, 2014Co-Authors: Marjan Gusev, Sashko Ristov, Bojana Koteska, Goran VelkoskiAbstract:Cloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing Virtual Machine Instance with additional resources, or by adding an additional Virtual Machine Instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” Instances or less ”large” Instances. The first hypothesis states that better performance is obtained by using more and smaller Instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger Instances results with better performance and that the user gets more performances than expected by scaling the resources.
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ESOCC - Windows Azure: Resource Organization Performance Analysis
Advanced Information Systems Engineering, 2014Co-Authors: Marjan Gusev, Sashko Ristov, Bojana Koteska, Goran VelkoskiAbstract:Cloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing Virtual Machine Instance with additional resources, or by adding an additional Virtual Machine Instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” Instances or less ”large” Instances. The first hypothesis states that better performance is obtained by using more and smaller Instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger Instances results with better performance and that the user gets more performances than expected by scaling the resources.
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EDUCON - Cloud E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course
2014 IEEE Global Engineering Education Conference (EDUCON), 2014Co-Authors: Sashko Ristov, Marjan Gusev, Goran VelkoskiAbstract:We have recently developed and implemented an E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course. Although this platform reduced the students' problems, we faced an additional problem with the lack of resources expressed in a specific timeframe just before homework deadline. In this paper we propose using a cloud based architecture of an e-Elearning system. It is intended to upgrade the e-Learning and benchmarking platform prototype into a scalable and elastic platform, where the system will send the execution on the Virtual Machine Instance hosted on the cloud node with available resources. Additionally, we propose a strategy for efficient utilisation of cloud resources is proposed.
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Cloud E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course
2014 Ieee Global Engineering Education Conference (Educon), 2014Co-Authors: Sashko Ristov, Marjan Gusev, Goran VelkoskiAbstract:We have recently developed and implemented an E-learning and Benchmarking Platform for the Parallel and Distributed Computing Course. Although this platform reduced the students' problems, we faced an additional problem with the lack of resources expressed in a specific timeframe just before homework deadline. In this paper we propose using a cloud based architecture of an e-Elearning system. It is intended to upgrade the e-Learning and benchmarking platform prototype into a scalable and elastic platform, where the system will send the execution on the Virtual Machine Instance hosted on the cloud node with available resources. Additionally, we propose a strategy for efficient utilisation of cloud resources is proposed.
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IEEE CLOUD - Optimal Resource Allocation to Host Web Services in Cloud
2013 IEEE Sixth International Conference on Cloud Computing, 2013Co-Authors: Marjan Gusev, Sashko Ristov, Goran Velkoski, Monika SimjanoskaAbstract:In this paper, we analyze the performance of computation intensive and memory demanding web services hosted in different environments with the same amount of resources, but orchestrated differently. A single-VM addresses the environment where all the resources are allocated in one huge Virtual Machine Instance (VMI), while a multi-VM environment uses several smaller VMIs, each allocated with only one CPU core, and the load is balanced among them. We realize series of experiments with different server loads by changing the message size and the number of concurrent messages to analyze the optimal resource allocation to host web services in order to achieve maximum performance from the same resources in the cloud, i.e., for the same price. Despite the hypothesis that the single-VM environment provides better performance than the multi-VM environment, the results show totally opposite for almost all test cases. We achieve maximal relative speedup of 9.83 comparing the multi-VM environment to the single-VM.
Ragib Hasan - One of the best experts on this subject based on the ideXlab platform.
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CyberSecurity - I Have the Proof: Providing Proofs of Past Data Possession in Cloud Forensics
2012 International Conference on Cyber Security, 2012Co-Authors: Shams Zawoad, Ragib HasanAbstract:Cloud computing has emerged as a popular computing paradigm in recent years. However, today's cloud computing architectures often lack support for computer forensic investigations. A key task of digital forensics is to prove the presence of a particular file in a given storage system. Unfortunately, it is very hard to do so in a cloud given the black-box nature of clouds and the multi-tenant cloud models. In clouds, analyzing the data from a Virtual Machine Instance or data stored in a cloud storage only allows us to investigate the current content of the cloud storage, but not the previous contents. In this paper, we introduce the idea of building proofs of past data possession in the context of a cloud storage service. We present a scheme for creating such proofs and evaluate its performance in a real cloud provider. We also discuss how this proof of past data possession can be used effectively in cloud forensics.
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I Have the Proof: Providing Proofs of Past Data Possession in Cloud Forensics
2012 International Conference on Cyber Security, 2012Co-Authors: Shams Zawoad, Ragib HasanAbstract:Cloud computing has emerged as a popular computing paradigm in recent years. However, today's cloud computing architectures often lack support for computer forensic investigations. A key task of digital forensics is to prove the presence of a particular file in a given storage system. Unfortunately, it is very hard to do so in a cloud given the black-box nature of clouds and the multi-tenant cloud models. In clouds, analyzing the data from a Virtual Machine Instance or data stored in a cloud storage only allows us to investigate the current content of the cloud storage, but not the previous contents. In this paper, we introduce the idea of building proofs of past data possession in the context of a cloud storage service. We present a scheme for creating such proofs and evaluate its performance in a real cloud provider. We also discuss how this proof of past data possession can be used effectively in cloud forensics.
Julien Gossa - One of the best experts on this subject based on the ideXlab platform.
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On the efficiency of several VM provisioning strategies for workflows with multi-threaded tasks on clouds
Computing, 2014Co-Authors: Marc Eduard Frincu, Stéphane Genaud, Julien GossaAbstract:Cloud computing promises the delivery of on-demand pay-per-use access to unlimited resources. Using these resources requires more than a simple access to them as most clients have certain constraints in terms of cost and time that need to be fulfilled. Therefore certain scheduling heuristics have been devised to optimize the placement of client tasks on allocated Virtual Machines. The applications can be roughly divided in two categories: independent bag-of-tasks and workflows. In this paper we focus on the latter and investigate a less studied problem, i.e., the effect the Virtual Machine allocation policy has on the scheduling outcome. For this we look at how workflow structure, execution time, Virtual Machine Instance type affect the efficiency of the provisioning method when cost and makespan are considered. To aid our study we devised a mathematical model for cost and makespan in case single or multiple Instance types are used. While the model allows us to determine the boundaries for two of our extreme methods, the complexity of workflow applications calls for a more experimental approach to determine the general relation. For this purpose we considered synthetically generated workflows that cover a wide range of possible cases. Results have shown the need for probabilistic selection methods in case small and heterogeneous execution times are used, while for large homogeneous ones the best algorithm is clearly noticed. Several other conclusions regarding the efficiency of powerful Instance types as compared to weaker ones, and of dynamic methods against static ones are also made.
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On the efficiency of several VM provisioning strategies for workflows with multi-threaded tasks on clouds
Computing, 2014Co-Authors: Marc Eduard Frincu, Stéphane Genaud, Julien GossaAbstract:Cloud computing promises the delivery of on-demand pay-per-use access to unlimited resources. Using these resources requires more than a simple access to them as most clients have certain constraints in terms of cost and time that need to be fulfilled. Therefore certain scheduling heuristics have been devised to optimize the placement of client tasks on allocated Virtual Machines. The applications can be roughly divided in two categories: independent bag-of-tasks and workflows. In this paper we focus ourselves on the latter and investigate a less studied problem, i.e., the effect the Virtual Machine allocation policy has on the scheduling outcome. For this we look at how workflow structure, execution time, Virtual Machine Instance type affect the efficiency of the provisioning method when cost and makespan are considered. To aid our study we devised a mathematical model for cost and makespan in case single or multiple Instance types are used. While the model allows us to determine the boundaries for two of our extreme methods, the complexity of workflow applications requires a more experimental approach to determine the general relation. For this we considered simulations of real application workflows and synthetic ones, covering most of the possible cases. Results have shown the need for probabilistic selection methods in case small and heterogeneous execution times are used, while for large homogeneous ones the best algorithm is clearly noticed. Several other conclusions regarding the efficiency of powerful Instance types as compared to weaker ones, and of dynamic methods against static ones are also made.
