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anh quan nguyen

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See detailMULTI-OBJECTIVE CLOUD BROKERING OPTIMIZATION TAKING INTO ACCOUNT UNCERTAINTY AND LOAD PREDICTION
Nguyen, Anh Quan UL

Doctoral thesis (2017)

Cloud broker optimization for energy-aware in multi-clouds system is to use a metaheuristic method for this multi-objective optimization problem that focuses on reducing the cost as well as improving the ... [more ▼]

Cloud broker optimization for energy-aware in multi-clouds system is to use a metaheuristic method for this multi-objective optimization problem that focuses on reducing the cost as well as improving the energy efficiency. This broad topic has been motivated by the energy-aware challenge at the level of cloud brokerage service. The cloud broker bases on multi-objectives optimization is characterized by a tightly coupled constraints, a dynamic environment, and changing objectives and priorities. That results in investigating specific aspects of the cloud brokerage service - virtual machine placement problem. [less ▲]

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See detailDesign of an Energy Efficiency Model and Architecture for Cloud Management using Prediction Models
Nguyen, Anh Quan UL; Tantar, Alexandru-Adrian UL; Bouvry, Pascal UL et al

Scientific Conference (2013, December 18)

In this paper, we present a new energy efficiency model and architecture for cloud management based on a prediction model with Gaussian Mixture Models. The methodology relies on a distributed agent model ... [more ▼]

In this paper, we present a new energy efficiency model and architecture for cloud management based on a prediction model with Gaussian Mixture Models. The methodology relies on a distributed agent model and the validation will be performed on OpenStack. This paper intends to be a position paper, the implementation and experimental run will be conducted in future work. The design concept leverages the prediction model by providing a full architecture binding the resource demands, the predictions and the actual cloud environment (Openstack). The prediction analysis feeds the power-aware agents that run on the compute nodes in order to turn the nodes into sleep mode when the load state is low to reduce the energy consumption of the data center. [less ▲]

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See detailEnergy Efficiency Metaheuristic Mechanism for Cloud Broker \\ in Multi-Cloud Computing
Nguyen, Anh Quan UL; Tantar, Alexandru-Adrian UL; Bouvry, Pascal UL et al

Scientific Conference (2013, July 13)

In this paper, we would like to present our view on an energy efficiency mechanism based on a metaheuristic algorithm for a cloud broker in multi-cloud computing. The following study is only a design ... [more ▼]

In this paper, we would like to present our view on an energy efficiency mechanism based on a metaheuristic algorithm for a cloud broker in multi-cloud computing. The following study is only a design concept and therefore this paper does not intend offering some established results. The metaheuristic based algorithm we envisage using needs to deal with the multiple objectives defined by the cloud users and the Cloud Service Providers (CSPs). The goal of the mechanism mainly focuses on energy efficiency while searching for a balance point that satisfies the objectives of both the cloud users and the CSPs. In our proposed concept, the designed mechanism needs to include a component to collect the resources that underutilized by the cloud users (in public or private cloud environment) and offers them back to the cloud broker for re-rent. [less ▲]

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See detailComputational Intelligence for Cloud Management Current Trends and Opportunities
Tantar, Alexandru-Adrian UL; Nguyen, Anh Quan UL; Bouvry, Pascal UL et al

Scientific Conference (2013, June 21)

The development of large scale data center and cloud computing optimization models led to a wide range of complex issues like scaling, operation cost and energy efficiency. Different approaches were ... [more ▼]

The development of large scale data center and cloud computing optimization models led to a wide range of complex issues like scaling, operation cost and energy efficiency. Different approaches were proposed to this end, including classical resource allocation heuristics, machine learning or stochastic optimization. No consensus exists but a trend towards using many-objective stochastic models became apparent over the past years. This work reviews in brief some of the more recent studies on cloud computing modeling and optimization, and points at notions on stability, convergence, definitions or results that could serve to analyze, respectively build accurate cloud computing models. A very brief discussion of simulation frameworks that include support for energy-aware components is also given. [less ▲]

Detailed reference viewed: 163 (2 UL)