Admission Control Mechanism Research Articles

Joint downlink user association and interference avoidance with a load balancing approach in backhaul-constrained HetNets.

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets. It shows that it is necessary to associate users with cells using a load balancing approach in order to fully leverage the addition of small cells. The cell association needs to be done jointly with an interference management technique that protects offloaded users and those prone to harmful interference. After modeling the system and describing the interference model, the problem of cell and subband allocation is formulated. We first examine the problem in a time-sharing mode and present a centralized heuristic solution to the cell and subband allocation problem. This is accomplished by solving the convex problem using the gradual removal method. The importance of providing distributed algorithms for HetNets leads to the development of a new algorithm through the application of the dual decomposition method to a reformulated problem and the use of an admission control mechanism. In the achieved algorithm, all computations are performed locally, with each user and base station relying only on local information. This algorithm obtains near-optimal answers, as confirmed by the simulation results. Compared with conventional cell allocation methods, our distributed algorithm prevents intensive interference for all users and achieves better load balance between network tiers, resulting in improved network utility.

Analytical Model of the Connection Handoff in 5G Mobile Networks with Call Admission Control Mechanisms.

Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this article is to analyze traffic levels, aiming to optimize traffic management and handling. This article considers the two most popular CAC mechanisms: the resource reservation mechanism and the threshold mechanism. It presents an analytical approach to occupancy distribution and blocking probability calculation in 5G mobile networks, incorporating connection handoff and CAC mechanisms for managing multiple traffic streams generated by multi-service sources. Due to the fact that the developed analytical model is an approximate model, its accuracy was also examined. For this purpose, the results of analytical calculations of the blocking probability in a group of 5G cells are compared with the simulation data. This paper is an extended version of our paper published in 17th ConTEL 2023.

Advanced Considerations Concerning Impact of Applied Call Admission Control Mechanisms on Traffic Characteristics in Elastic Optical Network Nodes

Over the past few years, a significant increase in network traffic volumes has been observed. The ever growing bandwidth demands mean that a reliable and optimum service level needs to be ensured in communication systems for specific traffic categories. Optimal allocation and use of resources may be achieved through the use of call admission control (CAC) mechanisms implemented in network systems. The resource reservation mechanism and the threshold mechanism are two of the most popular CAC methods. In the reservation mechanisms, a certain number of resources is reserved for selected (predefined) services only. In the case of threshold mechanisms, the number of resources allocated to individual traffic classes depends on the network load. This article discusses the results of simulations verifying the impact of applied CAC mechanisms on the traffic characteristics in elastic optical network (EON) nodes with a Clos structure. Loss probability results obtained with the use of the simulator are presented as well.

Cloud or In-House Service? Strategic Joining and Social Optimality in Hybrid Service Systems With Retrial Orbit

Cloud computing provides people with fast services, web service companies that need computing resources often submit some of their incoming jobs to the cloud and the rest to their in-house subsystem to seek services. This forms a hybrid service system with cloud service and an in-house server. Taking into account customers' strategic behavior, this article studies the optimal decision making problems in the hybrid service system. A queue-length-based admission control mechanism is adopted in this article to regulate whether the cloud server is open to customers. When the cloud server cannot be accessed immediately, some customers send their jobs to the in-house subsystem, while others (called opportunists) try to send their jobs to the cloud server again. We derive the conditional equilibrium joining probability of entering the in-house subsystem when the cloud is not open. Based on the cooperative and noncooperative game-theoretic analyses, we determine the cooperatively optimal retrial rate and the noncooperatively optimal retrial rate for a given queue-length information. Numerical experiments show that the two optimal retrial rates are equivalent. Finally, it is found that the existence of the opportunists significantly harms social interests, regardless of whether these opportunists are cooperative or noncooperative.

CO-CAC: A new approach to Call Admission Control for VoIP in 5G/WiFi UAV-based relay networks

Voice over IP (VoIP) requires a Call Admission Control (CAC) mechanism in WiFi networks to preserve VoIP packet flows from excessive network delay or packet loss. Ideally, this mechanism should be integrated with the operational scenario, guarantee the quality of service of active calls, and maximize the number of concurrent calls. This paper presents a novel CAC scheme for VoIP in the context of a WiFi access network deployed with Unmanned Aerial Vehicles (UAVs) that relay to a backhaul 5G network. Our system, named Codec-Optimization CAC (CO-CAC), is integrated into each drone. It intercepts VoIP call control messages and decides on the admission of every new call based on a prediction of the WiFi network’s congestion level and the minimum quality of service desired for VoIP calls. To maximize the number of concurrent calls, CO-CAC proactively optimizes the codec settings of active calls by exchanging signaling with VoIP users.We have simulated CO-CAC in a 50m × 50m scenario with four UAVs providing VoIP service to up to 200 ground users with IEEE 802.11ac WiFi terminals. Our results show that without CAC, the number of calls that did not meet a minimum quality level during the simulation was 10% and 90%, for 50 and 200 users, respectively. However, when CO-CAC was in place, all calls achieved minimum quality for up to 90 users without rejecting any call. For 200 users, only 25% of call attempts were rejected by the admission control scheme. These results were narrowly worse when the ground users moved randomly in the scenario.

Admission Control for 5G Core Network Slicing Based on Deep Reinforcement Learning

Network slicing is a promising technology for providing customized logical and virtualized networks for the fifth-generation (5G) use-cases (enhanced mobile broadband, ultrareliable low-latency communications, and massive machine-type communications), which pose distinct quality of service (QoS) requirements. Admission control and resource allocation mechanisms are pivotal for realizing network slicing efficiently, but existing mechanisms focus on slicing the radio access network. This article proposes an approach encompassing intelligent and efficient mechanisms for admission control and resource allocation for network slicing in the 5G core network. The admission control mechanism introduces two solutions, one based on reinforcement learning (called SARA) and the other based on deep reinforcement learning (called DSARA). SARA and DSARA consider the QoS requirements of 5G use-cases, differentiate network core nodes from edge nodes, and process slice requests in time windows to favor the service provider’s profit and resource utilization. Results show that SARA and DSARA overcome existing mechanisms for managing admission control and resource allocation in 5G core network slicing.

Admission Control and Resource Reservation for Prioritized Slice Requests With Guaranteed SLA Under Uncertainties

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request.

Probabilistic-Forecasting-Based Admission Control for Network Slicing in Software-Defined Networks

Network slicing is one the key features of software-defined networks (SDNs) and can be used in next-generation communication networks. Admission control of network slices is the basis of providing the heterogeneous quality-of-service performance guarantee and maximizing the optimization objectives of the network operator. Various admission control mechanisms have been proposed in the literature, including those based on traffic forecasting. However, recurrent neural network-based probabilistic forecasting models have not been given thorough consideration for slice admission control. In this study, the network slicing scheme design problem is formulated mathematically, with an equivalent formulation of the constrained bandwidth-sharing scheme. Then, a DeepAR-based slice admission control mechanism is proposed for sequential decision making for network slice requests in SDN, with the support of the SDN controller. An improved variant is further proposed with a closed-loop parameter update mechanism. The experiments based on real-world historical traffic data validate the effectiveness of the proposed mechanisms, with metrics, including revenue, resource reservation and utilization ratios, and service admission ratio.

Quality enhancement in a mm-wave multi-hop, multi-tier heterogeneous 5G network architecture

Millimetre-wave ultra-dense high capacity networks are an important component of future 5G and 6G cellular systems since they are providing extremely high network capacity and speed to the end users. However, disparate types of users coexist in such scenarios, which can make the heterogeneous network unfair in terms of allocation of resources to various users based on their requirements. Therefore, a mechanism is required for effective spectrum sharing and to achieve overall system fairness. In this paper, an analytical model is suggested, which is based on a two-dimensional Markov state-transition diagram, to help set the parameter values to control the issuance of resources in coexistence layouts. A restriction approach is further implemented to gain a fair balance of the Grade-of-Service (GoS) for both user groups using the User Admission Control (UAC) mechanism. The developed mechanism restricts access to various channel resources for users with complete choice to give a greater probability of access to different users with limited resource options. Various levels of restriction are investigated in order to offer a balanced low-blocking probability performance to both user groups in order to improve the overall network fairness. Also, the proposed approach could provide a precise level of Grade-of-Service guarantee for both the user groups if sufficient flexibility is available within the whole network. Our simulations results along with the analytical model demonstrate that approximately 30% to 45% enhancement, in terms of grade of service (GoS), could be achieved in high to medium loads by restricting some of the users’ flexibility. From the analytical model, it is concluded that the blocking of shadowed users are significantly reduced from 7% to 4.5% at high traffic loads. Moreover, the obtained results and findings are verified using a number of case studies and large-scale simulations.

Improved Throughput for All-or-Nothing Multicommodity Flows with Arbitrary Demands

Throughput is a main performance objective in communication networks. This paper considers a fundamental maximum throughput routing problem-the all-or-nothing multicommodity flow (ANF) problem - in arbitrary directed graphs and in the practically relevant but challenging setting where demands can be (much) larger than the edge capacities. Hence, in addition to assigning requests to valid flows for each routed commodity, an admission control mechanism is required which prevents overloading the network when routing commodities.

Preemptive admission control mechanism for strict QoS guarantee to life-saving emergency traffic in wireless LANs

The increasing usage of distributed emergency services (e.g. natural disaster or disaster caused by humans) in wireless local area networks requires the support of immediate channel access with strict quality of service (QoS) guarantee from a medium access control (MAC) protocol. The IEEE 802.11e/enhanced distributed channel access (EDCA) standard is the MAC enhancement for QoS. Unfortunately, 802.11e (EDCA) neither supports emergency traffic nor provides a strict QoS guarantee especially for a large number of users who report an emergency. To address this problem of achieving a strict QoS guarantee for emergency traffic, we previously developed and reported a multi-preemptive EDCA (MP-EDCA) protocol suitable for operating under low to medium traffic loads. However, MP-EDCA does not provide a strict QoS guarantee to life-saving emergency traffic (e.g. ambulance calls) in highly loaded networks. In this paper, we provide a solution to the problem of achieving a strict QoS guarantee to life-saving emergency traffic under high traffic loads. To this end, we propose a preemptive admission control (PAC) mechanism for MP-EDCA called PAC-MP-EDCA. The proposed PAC-MP-EDCA protects ongoing life-saving emergency traffic flows by giving high priority; thus, assures a QoS guarantee (in terms of lower packet delays) to life-saving emergency traffic when a high number of nodes require immediate channel access during emergency time. The priorities are set by carefully adjusting the short inter-frame space and slot-time in the emergency frames. The performance of PAC-MP-EDCA is evaluated by Riverbed Modeler simulation. Results obtained show that the proposed PAC-MP-EDCA achieved up to 98% lower MAC delays for life-saving emergency nodes and about 15% higher throughput than MP-EDCA under high traffic loads.

Multiservice Function Chain Embedding With Delay Guarantee: A Game-Theoretical Approach

Through network function virtualization (NFV), virtual network functions (VNFs) can be mapped onto substrate networks as service function chains (SFCs) to provide customized services with guaranteed Quality of Service (QoS). In this article, we solve a multi-SFC embedding problem by a game-theoretical approach considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. Specifically, each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all NFV nodes. Due to processing-resource sharing, additional delay is incurred and incorporated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We prove that the formulated resource-constrained multi-SFC embedding game (RC-MSEG) is an exact potential game admitting at least one pure Nash equilibrium (NE) and has the finite improvement property (FIP). Two iterative algorithms are developed, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE. Simulations are conducted to demonstrate the effectiveness of the proposed game-theoretical approach.

Admission control mechanism for quality of service and security in H.323 voice gateway

AbstractThe rapid demand on the VOIP communication lead to the increase of the development of information security and the offered quality. Therefore, information technology has increased the deepening and developing of call admission control (CAC). In which, admission decisions and resource reservations are taken at the edge voice gateway, rather than resources or individual routers within the network. This means that, CAC decision is made before a voice call is established and it depends on the availability of the required call resources to provide suitable quality of service (QoS) for the new call. In this article, a new model for voice network, in order to achieve QoS along with security, is proposed. In the proposed model, the admission control decision is taken place at H.323 voice gateways using resource reservation protocol. It is shown that the admission control decision should be taken at edge H.323 voice gateways. This will lead to provide the accepted security for both, source and destination nodes which were previously authenticated through the identity services engine. It is concluded from the simulation results that the packet loss rate of call flows after implementing the mechanism is almost 0%. In addition, there is no jitter although there is some delay for packet delivery “Latency.” The latency observed by the proposed model is still accepted according to the ITU G.114 standard which recommends a maximum of a 150 ms one‐way latency. All results are tested in GNS3 based simulation to evaluate the performance of voice gateway based upon CAC.

Flat-Rate Pricing and Truthful Offloading Mechanism in Multi-Layer Edge Computing

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of Internet-of-Things (IoT) devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from IoT devices, the queueing delay at the edge nodes not only poses a colossal impediment to achieving satisfactory quality of experience (QoE) for the IoT devices but also to the benefits of the edge nodes owing to escalating energy expenditure. Moreover, since the service providers may differ, computationally competent entities' computing services should entail economic compensation for the incurred energy expenditure and the capital investment. Therefore, the workload allocation mechanism, where we consider flat-rate and dynamic pricing schemes in the multi-layer edge computing structure, is much-needed. We use Stackelberg game to capture the inherent hierarchy and interdependence between the second-layer edge node (SLEN) and first-layer edge nodes (FLENs). A truthful admission control mechanism grounded on the optimal workload allocation is designed for FLENs without violating end-to-end (E2E) latency requirements. We prove that a Stackelberg equilibrium with the E2E latency guarantee and truthfulness exists and can be reached through proposed algorithm. Simulation results confirm the effectiveness of our scheme and illustrate several insights.

On using reinforcement learning for network slice admission control in 5G: Offline vs. online

SummaryAchieving a fair usage of network resources is of vital importance in Slice‐ready 5G network. The dilemma of which network slice to accept or to reject is very challenging for the Infrastructure Provider (InfProv). On one hand, InfProv aims to maximize the network resources usage by accepting as many network slices as possible; on the other hand, the network resources are limited, and the network slice requirements regarding Quality of Service (QoS) need to be fulfilled. In this paper, we devise three admission control mechanisms based on Reinforcement Learning, namely, Q‐Learning, Deep Q‐Learning, and Regret Matching, which allow deriving admission control decisions (policy) to be applied by InfProv to admit or reject network slice requests. We evaluated the three algorithms using computer simulation, showing results on each mechanism's performance in terms of maximizing the InfProv revenue and their ability to learn offline or online.

Fault-Detection Managers: More May Not Be the Merrier.

A fault management system contains managers that detect faults as well as initiate recovery actions. Such management systems often come in an architecture that is not only a distributed one but also decoupled from the applications. Although an arrangement like this promotes scalability, it unfortunately makes the recovery of applications dependent on the fault management system itself. This work introduces two novel equations to meet the performance objectives of applications. To this end, we first create an equation that estimates the maximum number of jobs to be handled by an application instance for meeting a given performance objective. This formula is then used by admission control mechanism to restrict the number of jobs (targeted for operational application instances) to be allowed to enter the system. Next, we create a second equation that computes the response time distribution of an application. Thereafter, we develop a simulation model that predicts the impact of the failure of four sample fault management architectures on application’s performance. Exploiting our equations, we compare the architectures in terms of three distinct ways of handling affected jobs when application instances fail—allow job loss; retry jobs resulting in overload; employ admission control to mitigate the overload. Our simulation results show that boosting the number of managers may not always be beneficial; rather, it could possibly be the interconnection topology (i.e. the layout of interconnects linking the architectural components) of the management architecture, together with the model parameter values that may sometimes have a bigger role to play in the application’s performance.

Opt-ACM: An Optimized load balancing based Admission Control Mechanism for Software Defined Hybrid Wireless based IoT (SDHW-IoT) network

The wide network applications of Internet of Things (IoT) urge to integrate the different wireless networking technologies. However, the future generation technologies like Software Defined Networking (SDN) can be useful to alleviate the associated challenges such as security, installation issues, coverage, etc. The growing number of IoT users produce massive volume of traffic causing heavy network load conditions, resulting in the state of network congestion. The network congestion may cause severe performance degradation issues such as frequent packet drops, longer delays, low throughput, etc. Thus, a promising solution is needed to reduce and/or prevent the network congestion. This paper presents an Optimized load balancing based Admission Control Mechanism (Opt-ACM) for effective network flow management resulting in the reduced network congestion. In addition to this, the paper highlights the challenges of the existing solutions, and discusses a Software Defined Hybrid Wireless based IoT (SDHW-IoT) network architecture consisting of Software Defined Wireless Sensor Network (SDWSN) and Software Defined Wireless Mesh Network (SDWMN). To validate the efficiency of Opt-ACM, a Mixed-Integer Linear Programming (MILP) based optimization problem is formulated and tested using a well-known mathematical optimization solver called Gurobi. Additionally, Opt-ACM is also emulated in Mininet-Wifi with varying network scenarios against some traditional (OLSR and OSPF) and state-of-the-art (FACOR and EASDN) approaches. Opt-ACM achieves an overall efficiency of 9.47% and 12.32% over other approaches for Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR) respectively. Similarly, an average improved efficiency of 26.77% and 33.10% is achieved with respect to the Average Delay (AD) and Average Jitter (AJ) metrics respectively.

Joint Admission Control and Resource Allocation With Parallel VNF Processing for Time-Constrained Chains of Virtual Network Functions

Network Function Virtualization decouples network function deployment from dedicated hardware and reduces costs. Network services are structured as chains of VNFs. Each chain is a set of VNFs that should be executed according to a predefined order. For some applications, VNF chains should be executed within time constraints to meet the application’s objectives. Most studies provide a solution to allocate substrate network resources to the chains without considering admission control. Allocating resources to all chains may not be possible due to resource limitations. Efficient admission control is therefore required to determine chains admission. This paper proposes a joint admission control and resource allocation mechanism for VNF chains. We propose a resource allocation mechanism based on the idea of parallel VNF processing to meet tight time constraints. As the used assumptions in deterministic modeling of the system do not hold in a wide range of network conditions, we propose a stochastic modeling at which VNF chain execution is modeled by a Queue network. The Queue network is analyzed to calculate the expected value of the probability of deadline meeting in chains, according which the joint resource allocation and admission control problem is modeled as a non-linear optimization. The proposed optimization framework maximizes the profit of the network provider while keeping the confidence level of deadline-meeting for the admitted chains at desired levels. To have an efficient power usage, power consumption is also considered in network provider profit calculation. A heuristic for the joint resource allocation and admission control of VNF chains is proposed. The effectiveness of the proposed method is demonstrated through simulation.

A QoS-Based Medium Access Control Protocol for WBANs and Its Performance Evaluation

Nowadays, Wireless Body Area Networks (WBANs) are used in many fields. WBANs are described as small sensor nodes that communicate wirelessly and provide services to the personal area. Quality of Service (QoS) is an essential issue for WBANs due to the importance of human life. QoS problems can only be solved with a robust Medium Access Control (MAC) protocol in WBANs. To find a solution to this problem, developers performed many MAC protocols for WBANs. ISO/IEEE 11073 health informatics defines the standard of personal health information and purposes to provide interoperability between medical technologies. This paper presents a MAC protocol that provides ISO/IEEE 11073 communication standards with QoS support, bases on cross-layer architecture. We designed a slot assignment scheme, prioritization mechanism, admission control mechanism to provide QoS. The performance evaluation of the proposed MAC protocol is compared with IEEE 802.15.4 and IEEE 802.15.6 protocols by considering end-to-end delay, packet loss ratio, and throughput parameters, and it has achieved out performance. It is observed that the proposed protocol doesn't exceed 45 ms delay, reached 81% traffic load, and a maximum error rate of 0.162%.

Maximum Throughput Under Admission Control With Unknown Queue-Length in Wireless Sensor Networks

In a Wireless Sensor Network (WSN) node, as the input traffic increases, the throughput can be assumed to first increase and then start to decrease, indicating congestion in the buffer. This suggests the need for an admission control mechanism to maintain high throughput as the arrival traffic increases. Considering the stochastic nature of WSNs, the information of the queue-length of arrival or newly sensed data packets can be unknown to a sensor node. This paper proposes a probabilistic admission control model with the maximum throughput for the node. In the proposed model, a reward when a data packet arriving to a sensor is accepted (not rejected) for transmission is considered, but a holding cost per unit time for the delay of accepted data packets in the sensor is also incurred. For the sensor node, by constructing a suitable Markov decision process (MDP), a probabilistic admission control algorithm on how to accept data packets on sleep and active phases to achieve a maximum throughput is proposed. Furthermore, for the identified (p;q) model, the energy consumption of the node in active and sleep phases, as well as the energy consumption switching from active to sleep per unit time and vice versa is investigated. An extensive simulation is implemented. The numerical results show that the problem is effectively solved by an optimal scheme with high energy efficiency. The results of this paper can be applied in designing optimal sensor nodes in WSNs.