Control Plane Optimisation for an SDN-Based WBAN Framework to Support Healthcare Applications

Software-Defined Networking (SDN) offers an abstract view of the network and assists network operators to control the network traffic and the associated network resources more effectively. For the past few years, SDN has shown a lot of merits in diverse fields of applications, an important one being the Wireless Body Area Network (WBAN) for healthcare services. With the amalgamation of SDN with WBAN (SDWBAN), the patient monitoring and management system has gained much more flexibility and scalability compared to the conventional WBAN. However, the performance of the SDWBAN framework largely depends on the controller which is a core element of the control plane. The reason is that an optimal number of controllers assures the satisfactory level of performance and control of the network traffic originating from the underlying data plane devices. This paper proposes a mathematical model to determine the optimal number of controllers for the SDWBAN framework in healthcare applications. To achieve this goal, the proposed mathematical model adopts the convex optimization method and incorporates three critical SDWBAN factors in the design process: number of controllers, latency and number of SDN-enabled switches (SDESW). The proposed analytical model is validated by means of simulations in Castalia 3.2 and the outcomes indicate that the network achieves high level of Packet Delivery Ratio (PDR) and low latency for optimal number of controllers as derived in the mathematical model

A Survey on Data Plane Programming with P4: Fundamentals, Advances, and Applied Research

With traditional networking, users can configure control plane protocols to match the specific network configuration, but without the ability to fundamentally change the underlying algorithms. With SDN, the users may provide their own control plane, that can control network devices through their data plane APIs. Programmable data planes allow users to define their own data plane algorithms for network devices including appropriate data plane APIs which may be leveraged by user-defined SDN control. Thus, programmable data planes and SDN offer great flexibility for network customization, be it for specialized, commercial appliances, e.g., in 5G or data center networks, or for rapid prototyping in industrial and academic research. Programming protocol-independent packet processors (P4) has emerged as the currently most widespread abstraction, programming language, and concept for data plane programming. It is developed and standardized by an open community and it is supported by various software and hardware platforms. In this paper, we survey the literature from 2015 to 2020 on data plane programming with P4. Our survey covers 497 references of which 367 are scientific publications. We organize our work into two parts. In the first part, we give an overview of data plane programming models, the programming language, architectures, compilers, targets, and data plane APIs. We also consider research efforts to advance P4 technology. In the second part, we analyze a large body of literature considering P4-based applied research. We categorize 241 research papers into different application domains, summarize their contributions, and extract prototypes, target platforms, and source code availability.Comment: Submitted to IEEE Communications Surveys and Tutorials (COMS) on 2021-01-2

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications

Assurance, Provision, Management and Enhancement of QoS in 5G Communication Networks

Enhancement of QoS in PS network as 5G communication network is non trivial endeavour which faces a host of new challenges beyond 3G and 4G communication networks. The number of nodes, the homogeneity of the access technologies, the conflicting network management objectives, resource usage minimization, and the division between limited physical resources and elastic virtual resources is driving a complete change in the vision and methodologies for efficient management of the available network resources. QoS is the measure of the reliability and performance of the networks’ nodes and links, particularly as perceivedbytheendusersoftheservicesandapplicationthataretransportedviaPSnetwork. Furthermore, QoS is a composite metric as it based on a number of multiple factors, which indicate the E2E characteristics and performance of the network condition, applications and services. Hence, reductions or improvements in the QoS level can brought about through a number of combined factors. This thesis tries to introduce a vision of Quality of Service (QoS) enhancement and management based on the 5th generation network requirements and solutions by: Firstly: Proposing a traffic flow management policy, which allocates and organises Machine Type Communication (MTC) traffic flow’s network resources sharing within Evolved Packet System (EPS), with an access element as a Wireless Sensor Network (WSN) gateway for providing an overlaying access channel between the Machine Type Devices (MTDs) and EPS. This proposal addresses the effect and interaction in the heterogeneity of applications, services and terminal devices and the related QoS issues among them. The introduced work inthisproposalovercomestheproblemsofnetworkresourcestarvationbypreventingdeterioration of network performance. The scheme is validated through simulation, which indicates the proposed traffic flow management policy outperforms the current traffic management policy. Specifically, simulation results show that the proposed model achieves an enhancement in QoS performance for the MTC traffic flows, including a decrease of 99.45% in Packet Loss Rate (PLR), a decrease of 99.89% in packet End to End (E2E) delay, a decrease of 99.21% in Packet Delay Variation (PDV). Furthermore, it retains the perceived Quality of Experience (QoE) of the real time application users within high satisfaction levels, such as the Voice over Long Term Evolution (VoLTE) service possessing a Mean Opinion Score (MOS)of4.349andenhancingtheQoSofavideoconferenceservicewithinthestandardised values of a 3GPP body, with a decrease of 85.28% in PLR, a decrease of 85% in packet E2E delay and a decrease of 88.5% in PDV. Secondly: Proposing an approach for allocating existing 4G installed network radio access nodes to multiple Base Band Unit (BBU) pools, which is proposed to deploy 5G Cloud-Radio Access Network (C-RAN) and improve the offered Network QoS (NQoS). The proposed approach involves performing radio access nodes clustering based on the Particle Swarm Optimization (PSO) algorithm, model selection Bayesian Information Criterion (BIC), Measure of spread technique and Voronoi tessellation. The proposed scheme is used to consider a Dynamic C-RAN (DC-RAN) operation, that adaptively adjusts the main Radio Remote Head (RRH) coverage range according to the traffic load requirement as well as considering energy saving. The numerical results of the approach show that the optimized partition of the proposed network model is 41 BBU pools, with an average density of RRHs per pool area, which matches the primary average density of the radio access nodes per network area. Thirdly: Developing mathematical framework that investigates the Power Consumption (PC) profile for the interaction of Internet of Thing (IoT) Application QoS (AQoS) with NQoS in wireless Software Defined Network (SDN) as SDN for WIreless SEnsor network (SDN-WISE). This profile model offers flexibility for managing the structure of the Machine to Machine (M2M) system in IoT. It enables controlling the provided NQoS, precisely the achieved PHY layer transmission link throughput, combined with the AQoS, represented by IoT data stream payload size. The investigation is composed of two essential SDN traffic parts, they are control plane signalling and data plane traffic PCs and their relevance with QoS. The results show that 98% PC in data plane companion with a control plane PC of 2% in overall of the proposed system power, these figures were achieved with control plane signalling Transmission Time Interval (TTI) of 5 sec and a maximum data plane payload size of 92 Bytes as a worst case scenario.Ministry of Higher Education and Scientific Research (MOHESR), Cultural Attache and University of Wasit in Ira

A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing

With the rapid growth of Internet of Things (IoT), cloud-centric application management raises questions related to quality of service for real-time applications. Fog and edge computing (FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource management on multiple resources from distributed and administrative FEC nodes is a key challenge to ensure the quality of end-user’s experience. To improve resource utilisation and system performance, researchers have been proposed many fair allocation mechanisms for resource management. Dominant Resource Fairness (DRF), a resource allocation policy for multiple resource types, meets most of the required fair allocation characteristics. However, DRF is suitable for centralised resource allocation without considering the effects (or feedbacks) of large-scale distributed environments like multi-controller software defined networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to ‘proportionately’ share resources among distributed participants. Although CEEI’s decentralised policy guarantees load balancing for performance isolation, they are not faultproof for computation offloading. The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of decentralised SDN controller deployment. We apply multi-agent reinforcement learning (MARL) with robustness against adversarial controllers to enable efficient priority scheduling for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by applying the principles of feedback (positive or/and negative network effects) in reverse game theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask offloading/forwarding in FEC environments. In the first piece of study, monotonic scheduling for joint offloading at the federated edge is addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL approach applying partition form game (PFG) to guarantee second-best Pareto optimality viii | P a g e (SBPO) in allocation of multi-resources from deterministic policy in both population and resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical networks by applying constrained coalition formation (CCF) games to implement MARL. The multi-objective optimisation problem for fog throughput maximisation is solved via a constraint dimensionality reduction methodology using fairness constraints for efficient gateway and low-level controller’s placement. For evaluation, we develop an agent-based framework to implement fair allocation policies in distributed data centre environments. In empirical results, the deterministic policy of IP-DRF scheme provides SBPO and reduces the average execution and turnaround time by 19% and 11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets in population non-monotonic settings. The processing cost of tasks shows significant improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair (MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria