References of "Guzek, Mateusz 50001911"
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See detailService Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers
Wagle, Shyam Sharan UL; Guzek, Mateusz UL; Bouvry, Pascal UL

in Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers (in press)

The knowledge of service performance of cloud providers is essential for cloud service users to choose the cloud services that meet their requirements. Instantaneous performance readings are accessible ... [more ▼]

The knowledge of service performance of cloud providers is essential for cloud service users to choose the cloud services that meet their requirements. Instantaneous performance readings are accessible, but prolonged observations provide more reliable information. However, due to technical complexities and costs of monitoring services, it may not be possible to access the service performance of cloud provider for longer time durations. The extended observation periods are also a necessity for prediction of future behavior of services. These predictions have very high value for decision making both for private and corporate cloud users, as the uncertainty about the future performance of purchased cloud services is an important risk factor. Predictions can be used by specialized entities, such as cloud service brokers (CSBs) to optimally recommend cloud services to the cloud users. In this paper, we address the challenge of prediction. To achieve this, the current service performance patterns of cloud providers are analyzed and future performance of cloud providers are predicted using to the observed service performance data. It is done using two automatic predicting approaches: ARIMA and ETS. Error measures of entire service performance prediction of cloud providers are evaluated against the actual performance of the cloud providers computed over a period of one month. Results obtained in the performance prediction show that the methodology is applicable for both short- term and long-term performance prediction. [less ▲]

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See detailComparisons of Heat Map and IFL Technique to Evaluate the Performance of Commercially Available Cloud Providers
Wagle, Shyam Sharan UL; Guzek, Mateusz UL; Bouvry, Pascal UL et al

in Comparisons of Heat Map and IFL Technique to Evaluate the Performance of Commercially Available Cloud Providers (in press)

Cloud service providers (CSPs) offer different Ser- vice Level Agreements (SLAs) to the cloud users. Cloud Service Brokers (CSBs) provide multiple sets of alternatives to the cloud users according to ... [more ▼]

Cloud service providers (CSPs) offer different Ser- vice Level Agreements (SLAs) to the cloud users. Cloud Service Brokers (CSBs) provide multiple sets of alternatives to the cloud users according to users requirements. Generally, a CSB considers the service commitments of CSPs rather than the actual quality of CSPs services. To overcome this issue, the broker should verify the service performances while recommending cloud services to the cloud users, using all available data. In this paper, we compare our two approaches to do so: a min-max-min decomposition based on Intuitionistic Fuzzy Logic (IFL) and a Performance Heat Map technique, to evaluate the performance of commercially available cloud providers. While the IFL technique provides simple, total order of the evaluated CSPs, Performance Heat Map provides transparent and explanatory, yet consistent evaluation of service performance of commercially available CSPs. The identified drawbacks of the IFL technique are: 1) It does not return the accurate performance evaluation over multiple decision alternatives due to highly influenced by critical feedback of the evaluators; 2) Overall ranking of the CSPs is not as expected according to the performance measurement. As a result, we recommend to use performance Heat Map for this problem. [less ▲]

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See detailA Novel Co-evolutionary Approach for Constrained Genetic Algorithms
Kieffer, Emmanuel UL; Guzek, Mateusz UL; Danoy, Grégoire UL et al

in Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion (2016)

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See detailMinimum Dependencies Energy-Efficient Scheduling in Data Centers
Zotkiewicz, Mateusz UL; Guzek, Mateusz UL; Kliazovich, Dzmitry UL et al

in IEEE Transactions on Parallel & Distributed Systems (2016)

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See detailAn Evaluation Model for Selecting Cloud Services from Commercially Available Cloud Providers
Wagle, Shyam Sharan UL; Guzek, Mateusz UL; Bouvry, Pascal UL et al

in 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom) (2015, December)

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See detailCloud Service Providers Ranking Based on Service Delivery and Consumer Experience
Wagle, Shyam Sharan UL; Guzek, Mateusz UL; Bouvry, Pascal UL

in 2015 IEEE 4th International Conference on Cloud Networking (CloudNet) (2015, October)

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See detailA Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing
Guzek, Mateusz UL; Bouvry, Pascal UL; Talbi, El-Ghazali

in IEEE Computational Intelligence Magazine (2015), 10(2), 53-67

Cloud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are ... [more ▼]

Cloud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are attracted by the flexibility and the speed with which they can obtain the services. Service providers compete to offer the most attractive conditions at the lowest prices. However, the environmental impact and legal aspects of cloud solutions pose additional challenges. Indeed, the new cloud-related techniques for resource virtualization and sharing and the corresponding service level agreements call for new optimization models and solutions. It is important for computational intelligence researchers to understand the novelties introduced by cloud computing. The current survey highlights and classifies key research questions, the current state of the art, and open problems. [less ▲]

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See detailEvalix: Classification and Prediction of Job Resource Consumption on HPC Platforms
Emeras, Joseph UL; Varrette, Sébastien UL; Guzek, Mateusz UL et al

in Proc. of the 19th Intl. Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP'15), part of the 29th IEEE/ACM Intl. Parallel and Distributed Processing Symposium (IPDPS 2015) (2015, May)

At the advent of a wished (or forced) convergence between High Performance Computing (HPC) platforms, stand-alone accelerators and virtualized resources from Cloud Computing (CC) systems, this ar- ticle ... [more ▼]

At the advent of a wished (or forced) convergence between High Performance Computing (HPC) platforms, stand-alone accelerators and virtualized resources from Cloud Computing (CC) systems, this ar- ticle unveils the job prediction component of the Evalix project. This framework aims at an improved efficiency of the underlying Resource and Job Management System (RJMS) within heterogeneous HPC facil- ities by the automatic evaluation and characterization of the submitted workload. The objective is not only to better adapt the scheduled jobs to the available resource capabilities, but also to reduce the energy costs. For that purpose, we collected the resource consumption of all the jobs executed on a production cluster for a period of three months. Based on the analysis then on the classification of the jobs, we computed a resource consumption model. The objective is to train a set of predictors based on the aforementioned model, that will give the estimated CPU, mem- ory and IO used by the jobs. The analysis of the resource consumption highlighted that different classes of jobs have different kinds of resource needs and the classification of the jobs enabled to characterize several application patterns of the users. We also discovered that several users whose resource usage on the cluster is considered as too low, are respon- sible for a loss of CPU time on the order of five years over the considered three month period. The predictors, trained from a supervised learning algorithm, were able to correctly classify a large set of data. We evalu- ated them with three performance indicators that gave an information retrieval rate of 71% to 89% and a probability of accurate prediction be- tween 0.7 and 0.8. The results of this work will be particularly helpful for designing an optimal partitioning of the considered heterogeneous plat- form, taking into consideration the real application needs and thus lead- ing to energy savings and performance improvements. Moreover, apart from the novelty of the contribution, the accurate classification scheme offers new insights of users behavior of interest for the design of future HPC platforms. [less ▲]

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See detailCloud Brokering: Current Practices and Upcoming Challenges
Guzek, Mateusz UL; Gniewek, Alicja UL; Bouvry, Pascal UL et al

in IEEE Cloud Computing (2015), 2(2),

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See detailHEROS: Energy-Efficient Load Balancing for Heterogeneous Data Centers
Guzek, Mateusz UL; Kliazovich, Dzmitry UL; Bouvry, Pascal UL

in 8th IEEE International Conference on Cloud Computing IEEE CLOUD 2015 (2015)

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See detailHolistic, Autonomic, and Energy-aware Resource Allocation in Cloud Computing
Guzek, Mateusz UL

Doctoral thesis (2014)

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See detailParaMASK: a Multi-Agent System for the Efficient and Dynamic Adaptation of HPC Workloads
Guzek, Mateusz UL; Besseron, Xavier UL; Varrette, Sébastien UL et al

in 14th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2014) (2014, December)

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See detailMulti-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems
Guzek, Mateusz UL; Pecero, Johnatan UL; Dorronsoro, Bernabé UL et al

in Applied Soft Computing (2014), 24

The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches ... [more ▼]

The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy. This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms. The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance. [less ▲]

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See detailHPC Performance and Energy-Efficiency of the OpenStack Cloud Middleware
Varrette, Sébastien UL; Plugaru, Valentin UL; Guzek, Mateusz UL et al

in Proc. of the 43rd Intl. Conf. on Parallel Processing (ICPP-2014), Heterogeneous and Unconventional Cluster Architectures and Applications Workshop (HUCAA'14) (2014, September)

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See detailA Holistic Model of the Performance and the Energy-Efficiency of Hypervisors in an HPC Environment
Guzek, Mateusz UL; Varrette, Sébastien UL; Plugaru, Valentin UL et al

in Concurrency & Computation : Practice & Experience (2014)

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See detailHPC Performance and Energy-Efficiency of Xen, KVM and VMware Hypervisors
Varrette, Sébastien UL; Guzek, Mateusz UL; Plugaru, Valentin UL et al

in Proc. of the 25th Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2013) (2013, October)

With a growing concern on the considerable energy consumed by HPC platforms and data centers, research efforts are targeting green approaches with higher energy efficiency. In particular, virtualization ... [more ▼]

With a growing concern on the considerable energy consumed by HPC platforms and data centers, research efforts are targeting green approaches with higher energy efficiency. In particular, virtualization is emerging as the prominent approach to mutualize the energy consumed by a single server running multiple VMs instances. Even today, it remains unclear whether the overhead induced by virtualization and the corresponding hypervisor middleware suits an environment as high-demanding as an HPC platform. In this paper, we analyze from an HPC perspective the three most widespread virtualization frameworks, namely Xen, KVM, and VMware ESXi and compare them with a baseline environment running in native mode. We performed our experiments on the Grid’5000 platform by measuring the results of the reference HPL benchmark. Power measures were also performed in parallel to quantify the potential energy efficiency of the virtualized environments. In general, our study offers novel incentives toward in-house HPC platforms running without any virtualized frameworks. [less ▲]

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See detailSystem Design and Implementation Decisions for ParaMoise Organizational Model
Guzek, Mateusz UL; Danoy, Grégoire UL; Bouvry, Pascal UL

in Proceedings of the 2013 Federated Conference on Computer Science and Information Systems (2013, September)

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See detailParaMoise: Increasing Capabilities of Parallel Execution and Reorganization in an Organizational Model
Guzek, Mateusz UL; Danoy, Grégoire UL; Bouvry, Pascal UL

in Ito, Takayuki; Jonker, Catholijn; Gini, Maria (Eds.) et al Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems, AAMAS'13 (2013, May 06)

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See detailA Holistic Model of the Performance and the Energy-Efficiency of Hypervisors in an HPC Environment
Guzek, Mateusz UL; Varrette, Sébastien UL; Plugaru, Valentin UL et al

in Energy Efficiency in Large Scale Distributed Systems (2013)

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See detailA Holistic Model for Resource Representation in Virtualized Cloud Computing Data Centers
Guzek, Mateusz UL; Kliazovich, Dzmitry UL; Bouvry, Pascal UL

in IEEE International Conference on Cloud Computing Technology, Bristol, UK, 2013 (2013)

Detailed reference viewed: 74 (12 UL)