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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 detailCollaborative Data Delivery for Smart City-oriented Mobile Crowdsensing Systems
Vitello, Piergiorgio; Capponi, Andrea UL; Fiandrino, Claudio UL et al

in IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE, 2018 (2018, December)

The huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing ... [more ▼]

The huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing infrastructures. In typical MCS systems, sensing tasks are allocated and reported on individual-basis. In this paper, we investigate on collaboration among users for data delivery as it brings a number of benefits for both users and sensing campaign organizers and leads to better coordination and use of resources. By taking advantage from proximity, users can employ device-to-device (D2D) communications like Wi-Fi Direct that are more energy efficient than 3G/4G technology. In such scenario, once a group is set, one of its member is elected to be the owner and perform data forwarding to the collector. The efficiency of forming groups and electing suitable owners defines the efficiency of the whole collaborative-based system. This paper proposes three policies optimized for MCS that are compliant with current Android implementation of Wi-Fi Direct. The evaluation results, obtained using CrowdSenSim simulator, demonstrate that collaborative-based approaches outperform significantly individual-based approaches. [less ▲]

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See detailSecurity, reliability and regulation compliance in Ultrascale Computing System
Bouvry, Pascal UL; Varrette, Sébastien UL; Wasim, Muhammad Umer UL et al

in Carretero, J.; Jeannot, E. (Eds.) Ultrascale Computing Systems (2018)

Ultrascale Computing Systems (UCSs) are envisioned as large-scale complex systems joining parallel and distributed computing systems that will be two to three orders of magnitude larger than today’s ... [more ▼]

Ultrascale Computing Systems (UCSs) are envisioned as large-scale complex systems joining parallel and distributed computing systems that will be two to three orders of magnitude larger than today’s systems (considering the number of Central Process Unit (CPU) cores). It is very challenging to find sustainable solutions for UCSs due to their scale and a wide range of possible applications and involved technologies. For example, we need to deal with heterogeneity and cross fertilization among HPC, large-scale distributed systems, and big data management. One of the challenges regarding sustainable UCSs is resilience. Another one, which attracted less interest in the literature but becomes more and more crucial with the expected convergence with the Cloud computing paradigm, is the notion of regulation in such system to assess the Quality of Service (QoS) and Service Level Agreement (SLA) proposed for the use of these platforms. This chapter covers both aspects through the reproduction of two articles: [1] and [2]. [less ▲]

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See detailWhy Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collection Frameworks
Tomasoni, Mattia; Capponi, Andrea UL; Fiandrino, Claudio UL et al

in Pervasive and Mobile Computing (2018)

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data ... [more ▼]

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCF) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly. [less ▲]

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See detailPRESENCE: Monitoring and Modelling the Performance Metrics of Mobile Cloud SaaS Web Services
Ibrahim, Abdallah Ali Zainelabden Abdallah UL; Wasim, Muhammad Umer UL; Varrette, Sébastien UL et al

in J. Mobile Information Systems (2018), 2018(1351386),

Service Level Agreements (SLAs) are defining the quality of the services delivered from the Cloud Services Providers (CSPs) to the cloud customers. The services are delivered on a pay-per-use model. The ... [more ▼]

Service Level Agreements (SLAs) are defining the quality of the services delivered from the Cloud Services Providers (CSPs) to the cloud customers. The services are delivered on a pay-per-use model. The quality of the provided services is not guaranteed by the SLA because it is just a contract. The developments around mobile cloud computing and the advent of edge computing technologies are contributing to the diffusion of the cloud services and the multiplication of offers. Although the cloud services market is growing for the coming years, unfortunately, there is no standard mechanism which exists to verify and assure that delivered services satisfy the signed SLA agreement in an automatic way. The accurate monitoring and modelling of the provided Quality of Service (QoS) is also missing. In this context, we aim at offering an automatic framework named PRESENCE, to evaluate the QoS and SLA compliance of Web Services (WSs) offered across several CSPs. Yet unlike other approaches, PRESENCE aims at quantifying in a fair and by stealth way the performance and scalability of the delivered WS. This article focuses on the first experimental results obtained on the accurate modelisation of each individual performance metrics. Indeed, 19 generated models are provided, out of which 78.9% accurately represent the WS performance metrics for two representative SaaS web services used for the validation of the PRESENCE approach. This opens novel perspectives for assessing the SLA compliance of Cloud providers using the PRESENCE framework. [less ▲]

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See detailPRESENCE: Performance Metrics Models for Cloud SaaS Web Services
Ibrahim, Abdallah Ali Zainelabden Abdallah UL; Wasim, Umer; Varrette, Sébastien UL et al

in Proc. of the 11th IEEE Intl. Conf. on Cloud Computing (CLOUD 2018) (2018, July)

Detailed reference viewed: 16 (4 UL)
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See detailOn Verifying and Assuring the Cloud SLA by Evaluating the Performance of SaaS Web Services Across Multi-cloud Providers
Ibrahim, Abdallah Ali Zainelabden Abdallah UL; Varrette, Sébastien UL; Bouvry, Pascal UL

in 48th Annual IEEE/IFIP Intl. Conf. on Dependable Systems and Networks Workshops (DNS'18) (2018, June)

Detailed reference viewed: 12 (5 UL)
See detailUL HPC Tutorial: Bio-informatics workflows and applications
Plugaru, Valentin UL; Diehl, Sarah UL; Varrette, Sébastien UL et al

Presentation (2018, June)

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See detailUL HPC Tutorial: (Advanced) Prototyping with Python
Parisot, Clément UL; Diehl, Sarah UL; Varrette, Sébastien UL et al

Presentation (2018, June)

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See detailUL HPC Tutorial: HPC Containers with Singularity
Plugaru, Valentin UL; Varrette, Sébastien UL; Diehl, Sarah UL et al

Presentation (2018, June)

Detailed reference viewed: 8 (1 UL)
See detailUL HPC Tutorial: HPC workflow with sequential jobs
Cartiaux, Hyacinthe UL; Varrette, Sébastien UL; Plugaru, Valentin UL et al

Presentation (2018, June)

Detailed reference viewed: 5 (2 UL)
See detailUL HPC Tutorial: Advanced Job scheduling with SLURM and OAR
Plugaru, Valentin UL; Varrette, Sébastien UL; Diehl, Sarah UL et al

Presentation (2018, June)

Detailed reference viewed: 6 (2 UL)