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Abstract: Capacity planning of IP-based networks is a difficult task. Ideally, in order to estimate the maximum amount of traffic that can be carried by the network, without violating QoS requirements such as end-to-end delay and packet loss, it is necessary to determine the queue length distribution of the network nodes under different traffic conditions. When per-flow guarantees are required (e.g., VoIP traffic), it is also necessary to determine the impact of the queue behavior on the performance of individual flows. Analytical models for queue length distribution are available only for relatively simple traffic patterns. This paper proposes a generic method for building a fuzzy predictor for modeling the behavior of a DiffServ node with multiple queues. The method combines nonlinear programming (NLP) and simulation to build a fuzzy predictor capable of determining the performance of a DiffServ node subjected to both per-flow and aggregated performance guarantees. This approach does not require deriving an analytical model, and can be applied to any type of traffic. In this paper, we employ the fuzzy approach to model the behavior of a multi-queue node where (aggregated ON–OFF) VoIP traffic and (self-similar) data traffic compete for the network resources. [Copyright &y& Elsevier]

The placement of routers and gateways in wireless communication networks for Smart Grid Advanced Metering Infrastructure (AMI) is a complex problem often addressed in the literature through heuristics, clustering, or linear programming approaches. In large-scale scenarios, the complexity is directly related to the region's number of smart meters and poles. The terrain profile can introduce signal quality degradation between a smart meter and the routers and gateways, further complicating the problem. This study presents the AIDA-ML method, representing a preliminary step toward developing an Intelligent Decision Support System (IDSS) for an effective positioning strategy. AIDA-ML incorporates a feature engineering-based strategy and utilizes machine learning algorithms to learn from the results computed by a heuristic method called AIDA. The heuristic approach is too time-consuming, relying on external resources like numerous terrain profile API calls and a deep understanding of wireless communication loss models. To implement a machine learning-based and more straightforward approach, a feature engineering process is employed to capture the operational characteristics and results of the analytical method while requiring less processing time. Through experiments conducted with real-world data from 26 cities in the state of Paraná, Brazil, comprising 466,237 smart meters and 352,867 poles, the results obtained using machine learning algorithms suggest that AIDA-ML can ensure the connection coverage of smart meters while meeting the same minimum requirements established for the analytical method. Moreover, AIDA-ML offers the advantage of reducing the processing time by 87.60% and by 96.86% the overall search space size compared to the heuristic approach. [ABSTRACT FROM AUTHOR]