The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured to analytical approaches for providing autonomous CAC. This paper is an effort to compare the performance of two such approaches - Neural Networks (NN) and Bayesian Networks (BN), to model the network behaviour and to estimate QoS metrics to be used in the CAC algorithm. It provides a way to find the optimum model training size for accurate predictions. Performance comparison is based on a wide range of experiments through a simulated network in Opnet. The outcome of this comparative study provides some interesting insights into the behaviour of NN and BN models and how they can be utilised for better CAC implementations.

Machine learning based Call Admission Control approaches: A comparative study

Intestinal inflammation has become a threatening concern in chicken production worldwide and is closely associated with Th17/Treg cell imbalance. Several studies described that gut microbiota is significantly implicated in chicken growth by modulating intestinal immune homeostasis and immune cell differentiation. Whether reshaping gut microbiota by fecal microbiota transplantation (FMT) could improve chicken growth by balancing Th17/Treg cells is an interesting question.Here, the chickens with significantly different body weight from three different breeds (Turpan cockfighting × White Leghorn chickens, white feather chickens, and yellow feather chickens) were used to compare Th17 and Treg cells. qPCR and IHC staining results indicated that Th17 cell-associated transcriptional factors Stat3 and rorγt and cytokines IL-6, IL-17A, and IL-21 were significantly (P < 0.05) higher in the jejunum of low body weight chickens, while Treg cell-associated transcriptional factor foxp3 and cytokines TGF-β and IL-10 were significantly (P < 0.05) lower in the jejunum of low body weight chickens, indicating imbalanced Th17/Treg cells were closely related to chicken growth performance. Transferring fecal microbiota from the healthy donor with better growth performance and abundant Lactobacillus in feces to 1-day-old chicks markedly increased growth performance (P < 0.001), significantly decreased Th17 cell-associated transcriptional factors and cytokines, and increased Treg cell-associated transcriptional factors and cytokines in the jejunum (P < 0.05). Furthermore, FMT increased the abundance of Lactobacillus (FMT vs Con; 84.98% vs 66.94%). Besides, the metabolites of tryptophan including serotonin, indole, and 5-methoxyindoleacetate were increased as well, which activated their receptor aryl-hydrocarbon-receptor (AhR) and expressed more CYP1A2 and IL-22 to maintain Th17/Treg cell balance and immune homeostasis.These findings suggested that imbalanced Th17/Treg cells decreased chicken growth performance, while FMT-reshaped gut microbiota, i.e., higher Lactobacilli, increased chicken growth performance by balancing Th17/Treg cells. Video Abstract. 

/pdf/fecal-microbiota-transplantation-improves-chicken-growth-jipwy598.pdf

Fecal microbiota transplantation improves chicken growth performance by balancing jejunal Th17/Treg cells

The recent focus on monitoring and managing telecommunication networks in a more efficient and autonomic way has led to the widespread application of machine learning (ML) approaches for network management tasks. In order to study the behaviour and evaluate the performance of such network management systems, it is a requirement that a suitable modelling framework exists. The work presented here addresses this need by comparing existing ML-based approaches and proposing a solution which employs the prediction capabilities of the Bayesian networks (BN) approach. It also formulates a BN-based decision support system for providing real-time call admission control (CAC) decisions in the next generation network (NGN) environment. In order to provide a realistic simulation environment, it surveys the existing computer network simulators and BN modelling simulators to choose the most suitable simulators to test the proposed models. The novelty of this research is validated through offline modelling and online performance evaluation of Bayesian networks-based admission control (BNAC) in terms of the metrics of packet delay, packet loss, queue size and blocking probability. This paper concludes that BNAC approach is appropriate choice for implementing a CAC solution which is efficient and autonomic.

Graphical modelling approach for monitoring and management of telecommunication networks

Existing virtual network admission control algorithms targeting high utilization of data center infrastructure are computationally expensive or provide poor performance. In particular, existing algorithms have in common that they are oblivious to the past, i.e., requests are handled in a fire-and-forget manner, not taking into account information from previously solved instances. This can be inefficient and misses out on a basic optimization opportunity: as for any network optimization algorithm that faces repeating problem instances, it may be beneficial to learn from network states and the outcome of acceptance decisions of the past. In this paper, we propose Ismael, a machine learning framework for predicting the acceptance of virtual clusters, one of the most common virtual network abstractions in data centers. Ismael can be configured with, and learn from, different existing algorithms by combining fixed-size feature representations for graphs with a convolutional neural network or a fully connected deep neural network. We report on extensive simulations, which demonstrate that it is possible to mimic existing, computationally intensive admission control algorithms with an accuracy of up to 94 %, while significantly reducing runtime.

/pdf/ismael-using-machine-learning-to-predict-acceptance-of-3ifxv3qq5k.pdf

Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers

The advent of IP-based Next Generation Network (NGN) and its guaranteed QoS promise has attracted significant attention from both service providers and subscribers. However, to fulfil the said promise, there is a need to provide effective Call Admission Control (CAC) based QoS provisioning solutions which are autonomous, intelligent and scalable.

Novel distributed call admission control solution based on machine learning approach

The anti-inflammatory effect of lutein in broilers is mediated by regulating Toll-like receptor 4/myeloid-differentiation-factor 88 signaling pathway.

A call admission control algorithm using support vector machine (SVM) (SVM-CAC) is analyzed. SVM-CAC uses a service vector and a network vector to predict admission state. QoS metric function compares with some thresholds to determine the admission state. The threshold value can reveal biases of services. SVM-CAC combines policy and SVM's advantages when making admission decisions, so it can take into account the business requirement, external network QoS resource and can reduce algorithm complexity. The simulation results show that this scheme accelerates calculation speed, has lower call delay, achieves better performance in terms of the call blocking probability and the call dropping probability than other machine learning admission control.

Policy-based QoS Control Using Call Admission Control and SVM

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The anti-inflammatory effect of lutein in broilers is mediated by regulating Toll-like receptor 4/myeloid-differentiation-factor 88 signaling pathway.

Policy-based QoS Control Using Call Admission Control and SVM