An investigation into intelligent network congestion control strategies

This thesis examines the congestion control issues that arise in Intelligent Networks, when it is necessary to support multiple service types with different load requirements and priorities. The area of Intelligent Network (IN) congestion control has been under investigation for over a decade, but in general, the models used in this research were over-simplified and all service types were assumed to have the same priority levels and load requirements at the various IN physical elements. However, as the IN is a dynamic network that must process many different service types that have radically different call load profiles and are based on different service level agreements and charging schemes, the validity of the above assumptions is questionable. The aim of this work, therefore, is to remove a number of the classic assumptions made in IN congestion control research, by: • developing a detailed model of an IN, catering for multiple traffic types, • using this model to establish the shortcomings of classic congestion control strategies, • devising a new IN congestion control strategy and verifying its superiority on the model. To achieve these aims, an IN model (both simulation and analytic) is developed to reflect the physical and functional architecture of the network and model the information flows required between network entities in order to execute services. The effectiveness of various classic active and reactive congestion control strategies are then investigated using this model and it is established that none of these strategies are capable of protecting both the Service Control Point and Service Switching Points under all possible traffic mixes and loads. This is partially due to the fact that all of these strategies are based on the use of fixed parameters (and are therefore not flexible enough to deal with IN traffic) and partially because none of these strategies take into account the different load requirements of the different service types. A new, flexible strategy is then devised to facilitate global IN congestion control and cater for service types with different characteristics. This strategy maximises IN performance by protecting all network elements from overload while maximising network revenue and preserving fairness between service types during overload. A number of factors determining the relative importance or weight of different traffic types are also identified and used by the strategy to maintain call importance during overload. The efficiency of this strategy is demonstrated by comparing its operation to that of the best classic IN overload controls and also to a new strategy, which has scalable and dynamic behaviour (and which was devised for the purpose of providing a fair comparison to the optimisation strategy). The optimisation-based strategy and dynamic strategy are found to be equally effective and far superior to the classic strategies. However, the optimisation algorithm also preserves relative importance and fairness, while maximising network revenue - but at the cost of a not insignificant processing overhead

Project FATIMA Final Report: Part 2

The final report of project FATIMA is presented in two parts. Part 1 contains a summary of the FATIMA method and sets out the key recommendations in terms of policies and optimisation methodology from both project OPTIMA and project FATIMA. Part 1 is thus directed particularly towards policy makers. Part 2 contains the details of the methodology, including the formulation of the objective functions, the optimisation process, the resulting optimal strategies under the various objective function regimes and a summary of the feasibility and acceptability of the optimal strategies based on consultations with the city authorities. This part is thus mainly aimed at the professional in transport planning and modelling

Performance controls for distributed telecommunication services

As the Internet and Telecommunications domains merge, open telecommunication service architectures such as TINA, PARLAY and PINT are becoming prevalent. Distributed Computing is a common engineering component in these technologies and promises to bring improvements to the scalability, reliability and flexibility of telecommunications service delivery systems. This distributed approach to service delivery introduces new performance concerns. As service logic is decomposed into software components and distnbuted across network resources, significant additional resource loading is incurred due to inter-node communications. This fact makes the choice of distribution of components in the network and the distribution of load between these components critical design and operational issues which must be resolved to guarantee a high level of service for the customer and a profitable network for the service operator. Previous research in the computer science domain has addressed optimal placement of components from the perspectives of minimising run time, minimising communications costs or balancing of load between network resources. This thesis proposes a more extensive optimisation model, which we argue, is more useful for addressing concerns pertinent to the telecommunications domain. The model focuses on providing optimal throughput and profitability of network resources and on overload protection whilst allowing flexibility in terms of the cost of installation of component copies and differentiation in the treatment of service types, in terms of fairness to the customer and profitability to the operator. Both static (design-time) component distribution and dynamic (run-time) load distribution algorithms are developed using Linear and Mixed Integer Programming techniques. An efficient, but sub-optimal, run-time solution, employing Market-based control, is also proposed. The performance of these algorithms is investigated using a simulation model of a distributed service platform, which is based on TINA service components interacting with the Intelligent Network through gateways. Simulation results are verified using Layered Queuing Network analytic modelling Results show significant performance gains over simpler methods of performance control and demonstrate how trade-offs in network profitability, fairness and network cost are possible

Admission Control Optimisation for QoS and QoE Enhancement in Future Networks

Recent exponential growth in demand for traffic heterogeneity support and the number of associated devices has considerably increased demand for network resources and induced numerous challenges for the networks, such as bottleneck congestion, and inefficient admission control and resource allocation. Challenges such as these degrade network Quality of Service (QoS) and user-perceived Quality of Experience (QoE). This work studies admission control from various perspectives. For example, two novel single-objective optimisation-based admission control models, Dynamica Slice Allocation and Admission Control (DSAAC) and Signalling and Admission Control (SAC), are presented to enhance future limited-capacity network Grade of Service (GoS), and for control signalling optimisation, respectively. DSAAC is an integrated model whereby a cost-estimation function based on user demand and network capacity quantifies resource allocation among users. Moreover, to maximise resource utility, adjustable minimum and maximum slice resource bounds have also been derived. In the case of user blocking from the primary slice due to congestion or resource scarcity, a set of optimisation algorithms on inter-slice admission control and resource allocation and adaptability of slice elasticity have been proposed. A novel SAC model uses an unsupervised learning technique (i.e. Ranking-based clustering) for optimal clustering based on users’ homogeneous demand characteristics to minimise signalling redundancy in the access network. The redundant signalling reduction reduces the additional burden on the network in terms of unnecessary resource utilisation and computational time. Moreover, dynamically reconfigurable QoE-based slice performance bounds are also derived in the SAC model from multiple demand characteristics for clustered user admission to the optimal network. A set of optimisation algorithms are also proposed to attain efficient slice allocation and users’ QoE enhancement via assessing the capability of slice QoE elasticity. An enhancement of the SAC model is proposed through a novel multi-objective optimisation model named Edge Redundancy Minimisation and Admission Control (E-RMAC). A novel E-RMAC model for the first time considers the issue of redundant signalling between the edge and core networks. This model minimises redundant signalling using two classical unsupervised learning algorithms, K-mean and Ranking-based clustering, and maximises the efficiency of the link (bandwidth resources) between the edge and core networks. For multi-operator environments such as Open-RAN, a novel Forecasting and Admission Control (FAC) model for tenant-aware network selection and configuration is proposed. The model features a dynamic demand-estimation scheme embedded with fuzzy-logic-based optimisation for optimal network selection and admission control. FAC for the first time considers the coexistence of the various heterogeneous cellular technologies (2G, 3G,4G, and 5G) and their integration to enhance overall network throughput by efficient resource allocation and utilisation within a multi-operator environment. A QoS/QoE-based service monitoring feature is also presented to update the demand estimates with the support of a forecasting modifier. he provided service monitoring feature helps resource allocation to tenants, approximately closer to the actual demand of the tenants, to improve tenant-acquired QoE and overall network performance. Foremost, a novel and dynamic admission control model named Slice Congestion and Admission Control (SCAC) is also presented in this thesis. SCAC employs machine learning (i.e. unsupervised, reinforcement, and transfer learning) and multi-objective optimisation techniques (i.e. Non-dominated Sorting Genetic Algorithm II ) to minimise bottleneck and intra-slice congestion. Knowledge transfer among requests in form of coefficients has been employed for the first time for optimal slice requests queuing. A unified cost estimation function is also derived in this model for slice selection to ensure fairness among slice request admission. In view of instantaneous network circumstances and load, a reinforcement learning-based admission control policy is established for taking appropriate action on guaranteed soft and best-effort slice requests admissions. Intra-slice, as well as inter-slice resource allocation, along with the adaptability of slice elasticity, are also proposed for maximising slice acceptance ratio and resource utilisation. Extensive simulation results are obtained and compared with similar models found in the literature. The proposed E-RMAC model is 35% superior at reducing redundant signalling between the edge and core networks compared to recent work. The E-RMAC model reduces the complexity from O(U) to O(R) for service signalling and O(N) for resource signalling. This represents a significant saving in the uplink control plane signalling and link capacity compared to the results found in the existing literature. Similarly, the SCAC model reduces bottleneck congestion by approximately 56% over the entire load compared to ground truth and increases the slice acceptance ratio. Inter-slice admission and resource allocation offer admission gain of 25% and 51% over cooperative slice- and intra-slice-based admission control and resource allocation, respectively. Detailed analysis of the results obtained suggests that the proposed models can efficiently manage future heterogeneous traffic flow in terms of enhanced throughput, maximum network resources utilisation, better admission gain, and congestion control

A practical assessment of network orientated load control for the intelligent network

The purpose of this thesis is to assess a new method of controlling load in Intelligent Networks (INs). This will be done through the analysis of experimentation results and comparison with existing methods of IN load control. This exercise will result in the investigation and validation of the proposed benefits being offered by this new methodology and the unveiling of its disadvantages. The methodology is known as network-orientated load control for the IN. Network-orientated load control is demonstrated using the MARINER Service Traffic Load Control System developed by the European Commission’s Advanced Communication, Technologies and Services (ACTS) Multi-Agent Architecture for Distributed Intelligent Network Load Control and Overload Protection (MARINER) Project. This system is shown to be a network-orientated load control application operating at the service level, built specifically for Intelligent Networks. Network-orientated load control is then assessed by deploying the MARINER System on a model of the IN, and running an exhaustive series of experiments. These experiments are structured to test the proposed benefits, limitations and disadvantages of networkorientated load control. The conclusions drawn from the results of these trials are then compared with existing IN load control characteristics, and used to make an assessment of network-orientated load control for the Intelligent Network