Enhanced Lagrange Decomposition for multi-objective scalable TE in SDN

Jaglarz, Piotr; Boryło, Piotr; Szymanski, Andrzej; Chołda, Piotr

doi:10.1016/j.comnet.2019.106992

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…In the experiment, we use two non-overlapping sets of samples: 600 samples taken every 30 min (fine-grained) and 600 samples taken every 2h (coarsegrained), so in this case r = 4. While the model can use all four aggregation levels in the data, we focus here on only two of them because the practical solutions proposed in the literature are focused on a time scale of about 1h Jaglarz et al (2020) and because we could substantially simplify our implementation for r being a power of two. The overall time span 2 0 1 9 -1 1 -0 5 2 0 1 9 -1 1 -0 7 2 0 1 9 -1 1 -0 9 2 0 1 9 -1 1 -1 1 2 0 1 9 -1 1 -1 3 2 0 1 9 -1 1 -1 (a) time series 1 1 -1 4 0 6 1 1 -1 4 1 2 1 1 -1 4 1 8 1 1 -1 5 0 0 1 1 -1 5 0 6 1 1 -1 5 1 2 1 1 -1 5 1 8 1 1 -1 6 0 0 1 1 -1 6 0 then covers around 62 days, from which the last 2 days are used only for forecast validation.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Fine-grained Network Traffic Prediction from Coarse Data

Rusek

Drton

2023

AJS

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ICT systems provide detailed information on computer network traffic. However, due to storage limitations, some of the information on past traffic is often only retained in an aggregated form. In this paper we show that Linear Gaussian State Space Models yield simple yet effective methods to make predictions based on time series at different aggregation levels. The models link coarse-grained and fine-grained time series to a single model that is able to provide fine-grained predictions. Our numerical experiments show up to 3.7 times improvement in expected mean absolute forecast error when forecasts are made using, instead of ignoring, additional coarse-grained observations. The forecasts are obtained in a Bayesian formulation of the model, which allows for provisioning of a traffic prediction service with highly informative priors obtained from coarse-grained historical data.

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Section: Numerical Experimentsmentioning

confidence: 99%

Fine-grained Network Traffic Prediction from Coarse Data

Rusek

Drton

2023

AJS

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“…Networks are made up of many nodes that have millions of streams of dynamic nature. Jaglarz et al (2020) have addressed this issue. A novel linear programming problem of energy awareness, multi-objective, complex and integer formulation is modeled and solved using the Lagrange decomposition algorithm.…”

Section: B) Lagrange and Benders Algorithms In Comparisonmentioning

confidence: 99%

Designing a new mathematical model based on ABC analysis for inventory control problem: A real case study

et al. 2021

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In modern business today, organizations that hold large numbers of inventory items, do not find it economical to make policies for the management of individual inventory items. Managers, thus, need to classify these items according to their importance and fit each item to a certain asset class. The method of grouping and inventory control available in traditional ABC has several disadvantages. These shortcomings have led to the development of an optimization model in the present study to improve the grouping and inventory control decisions in ABC. Moreover, it simultaneously optimizes the existing business relationships among revenue, investment in inventory and customer satisfaction (through service levels) as well as a company's budget for inventory costs. In this paper, a mathematical model is presented to classify inventory items, taking into account significant profit and cost reduction indices. The model has an objective function to maximize the net profit of items in stock. Limitations such as budget even inventory shortages are taken into account too. The mathematical model is solved by the Benders decomposition and the Lagrange relaxation algorithms. Then, the results of the two solutions are compared. The TOPSIS technique and statistical tests are used to evaluate and compare the proposed solutions with one another and to choose the best one. Subsequently, several sensitivity analyses are performed on the model, which helps inventory control managers determine the effect of inventory management costs on optimal decision making and item grouping. Finally, according to the results of evaluating the efficiency of the proposed model and the solution method, a real-world case study is conducted on the ceramic tile industry. Based on the proposed approach, several managerial perspectives are gained on optimal inventory grouping and item control strategies.

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“…Em [8], foi apresentada uma estratégia de roteamento baseada no SPEA2, com fim de otimizar a eficiência energética da rede considerando também as restrições de QoS. Em [11], foi proposto um método de otimização de múltiplos critérios com foco em escalabilidade, utilizando decomposição de Lagrange aprimorado por sequências ergódicas, onde soluções são derivadas com base em pesos atribuídos para os critérios.…”

Section: Trabalhos Relacionadosunclassified

Uma abordagem multiobjetivo para definição de rotas em Redes Definidas por Software (SDN)

Assis¹,

Errico²

2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Enhanced Lagrange Decomposition for multi-objective scalable TE in SDN

Cited by 3 publications

References 25 publications

Fine-grained Network Traffic Prediction from Coarse Data

Fine-grained Network Traffic Prediction from Coarse Data

Designing a new mathematical model based on ABC analysis for inventory control problem: A real case study

Uma abordagem multiobjetivo para definição de rotas em Redes Definidas por Software (SDN)

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