INF - Instituto de Informática
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Navegando INF - Instituto de Informática por Por Orientador "Corrêa, Sand Luz"
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Item Um modelo analítico para estimar o consumo de energia de sistemas multi-camadas no nível de transação(Universidade Federal de Goiás, 2017-04-25) Ferreira, Alex Rabelo; Reis, Valéria Quadros dos; http://lattes.cnpq.br/7372808441783055; Corrêa, Sand Luz; http://lattes.cnpq.br/3386409577930822; Corrêa, Sand Luz; http://lattes.cnpq.br/3386409577930822; Reis, Valéria Quadros dos; http://lattes.cnpq.br/7372808441783055; Martins, Wellington Santos; Petrucci, Vinicius TavaresIn large-scale data centers, power management techniques such as Dynamic Voltage/Frequency Scaling (DVFS), virtual machine consolidation, and power-capping mechanisms promise impressive energy savings compared to traditional resource management strategies. Most of these techniques rely on coarse-grained monitoring of the workload behavior to apply their optimizations. However, coarse-grained monitoring and black box observations are not satisfactory for predicting the behavior of bursty workloads such as those observed in enterprise, Web servers. In this work, we propose an analytical model to estimate the energy consumption of multi-tier Web Systems. Differently from previous works, our model captures the consumption pattern at the level of fine-grained transactions and for each tier of the system. In addition, our model is based only on CPU utilization and server architectural parameters, which can be easily obtained in today’s production environments. We demonstrate the effectiveness of our model in a real-world experimentation environment, based on the TPC-W benchmark. Results show that our model is able to estimate the energy consumption for a Web system with an average relative error of 6.5% in the worst-case scenario, whereas more complex models of the literature present errors within the same order of magnitude.Item Controle de admissão para network slicing considerando recursos de comunicação e computação(Universidade Federal de Goiás, 2023-05-10) Lima, Henrique Valle de; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Corrêa, Sand Luz; http://lattes.cnpq.br/3386409577930822; Corrêa, Sand Luz; Cardoso, Kleber Vieira; Oliveira Júnior, Antônio Carlos de; Costa, Ronaldo Martins da; Both, Cristiano BonatoThe 5G networks have enabled the application of various innovative and disruptive technologies such as Network Function Virtualization (NFV) and Software-Defined Networking (SDN). Together, these technologies act as enablers of Network Slicing (NS), transforming the way networks are operated, managed, and monetized. Through the concept of Slice-as-a-Service (SlaaS), telecommunications operators can monetize the physical and logical infrastructure by offering network slices to new customers, such as vertical industries. This thesis addresses the problem of tenant admission control using NS. We propose three admission control models for NS (MONETS-OBD, MONETS-OBS, and CAONS) that consider both communication and computation resources. To evaluate the proposed models, we compare the results with different classical algorithms from the literature, such as eUCB, e-greedy, and ONETS. We use data from different applications to enrich the analysis. The results indicate that the MONETS-OBD, MONETS-OBS, and CAONS heuristics perform admission control that approaches the set of ideal solutions. We achieve high efficiency with the MONETS-OBD and MONETS-OBS heuristics in controlling tenant admission, reaching acceptance rates of up to 99% in some cases. Furthermore, the CAONS heuristic, which employs penalties, not only achieves acceptance and reward rates close to the optimal solution but also significantly reduces the number of capacity violations. Lastly, the results highlight that the process of slice admission control should consider both communication and computation resources, which are scarce at the network edge. A solution that considers only communication resources can lead to incorrect and unfeasible interpretations, overestimating the capacity of computation resources.