Quality Of Service Threshold Research Articles

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization concerning with traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research focuses on extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which mainly focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A state-of-the-art Deep Learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.

Dynamic UPF placement and chaining reconfiguration in 5G networks

Network function virtualization (NFV) and multi-access edge computing (MEC) have become two crucial pillars in developing 5G and beyond networks. NFV promises cost-saving and fast revenue generation through dynamic instantiation and the scaling of virtual network functions (VNFs) according to time-varying service demands. Additionally, MEC provides considerable reductions in network response time and backhaul traffic since network functions and server applications can be deployed close to users. Nevertheless, the placement and chaining of VNFs at the network edge is challenging due to numerous aspects and attendant trade-offs. This paper addresses the problem of dynamic user plane function placement and chaining reconfiguration (UPCR) in a MEC environment to cope with user mobility while guaranteeing cost reductions and acceptable quality of service (QoS). The problem is formalized as a multi-objective integer linear programming model to minimize multiple cost components involved in the UPCR procedure. We propose a heuristic algorithm called dynamic priority and cautious UPCR (DPC-UPCR) to reduce the solution time complexity. Additionally, we devise a scheduler mechanism based on optimal stopping theory to determine the best reconfiguration time according to instantaneous values of latency violations and a pre-established QoS threshold. Our detailed simulation results evidence the efficiency of the proposed approaches. Specifically, the DPC-UPCR provides near-optimal solutions, within 15% of the optimum in the worst case, in significantly shorter times than the mathematical model. Moreover, the proposed scheduling method outperforms two scheduler baseline solutions regarding the number of reconfiguration events and QoS levels.

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

This paper presents High density heterogeneous networks (HetNet) which are the most promising technology for the fifth generation (5G) cellular network. Since 5G will be available for a long time, previous generation networking systems will need customization and updates. We examine the merits and drawbacks of legacy and Q-Learning (QL)-based adaptive resource allocation systems. Furthermore, various comparisons between methods and schemes are made for the purpose of evaluating the solutions for future generation. Microwave macro cells are used to enable extra high capacity such as Long-Term Evolution (LTE), eNodeB (eNB), and Multimedia Communications Wireless technology (MC), in which they are most likely to be deployed. This paper also presents four scenarios for 5G mm-Wave implementation, including proposed system architectures. The WL algorithm allocates optimal power to the small cell base station (SBS) to satisfy the minimum necessary capacity of macro cell user equipment (MUEs) and small cell user equipment (SCUEs) in order to provide quality of service (QoS) (SUEs). The challenges with dense HetNet and the massive backhaul traffic they generate are discussed in this study. Finally, a core HetNet design based on clusters is aimed at reducing backhaul traffic. According to our findings, MM-wave HetNet and MEC can be useful in a wide range of applications, including ultra-high data rate and low latency communications in 5G and beyond. We also used the channel model simulator to examine the directional power delay profile with received signal power, path loss, and path loss exponent (PLE) for both LOS and NLOS using uniform linear array (ULA) 2X2 and 64x16 antenna configurations at 38 GHz and 73 GHz mmWave bands for both LOS and NLOS (NYUSIM). The simulation results show the performance of several path loss models in the mmWave and sub-6 GHz bands. The path loss in the close-in (CI) model at mmWave bands is higher than that of open space and two ray path loss models because it considers all shadowing and reflection effects between transmitter and receiver. We also compared the suggested method to existing models like Amiri, Su, Alsobhi, Iqbal, and greedy (non adaptive), and found that it not only enhanced MUE and SUE minimum capacities and reduced BT complexity, but it also established a new minimum QoS threshold. We also talked about 6G researches in the future. When compared to utilizing the dual slope route loss model alone in a hybrid heterogeneous network, our simulation findings show that decoupling is more visible when employing the dual slope path loss model, which enhances system performance in terms of coverage and data rate.

Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE

In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a precise QoE model for every service that reflects the impact of network performance variations on the end-user experience. In this work, an automatic method is presented for deriving Quality-of-Service (QoS) thresholds in analytical QoE models of several services from radio connection traces collected in an Long Term Evolution (LTE) network. Such QoS thresholds reflect the minimum connection performance below which a user gives up its connection. The proposed method relies on the fact that user experience influences the traffic volume requested by users. Method assessment is performed with real connection traces taken from live LTE networks. Results confirm that packet delay or user throughput are critical factors for user experience in the analyzed services.

Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

Small cells (SCs) based ultra-dense heterogeneous networks (HetNets) are one of the promising solutions for increased coverage and capacity in 5G cellular networks. However, in multi-tiered architecture, co-tier and cross-tier interferences are both performance-limiting factors. Efficient resource allocation techniques can handle interferences effectively, however, their complexity linearly increases with the density of the HetNets resulting from dynamic and unplanned deployment of SCs. Therefore, HetNets can be implemented only through an algorithm that is self-organizing and adaptive to the dynamic conditions. In this research article, a Q-Learning (QL) based adaptive resource allocation scheme is proposed and evaluated for SC-based ultra-dense HetNets. This QL scheme allocates optimal power to the small cell base station (SBS) to meet the minimum required capacity of macrocell user equipment (MUEs) and the small cell user equipment (SUEs) to provide quality of service (QoS). The proposed QL scheme not only maintains the minimum required capacities of the MUEs and SUEs but has also shown a significant improvement in the capacities of MUEs and SUEs in high interference scenarios as compared to the prior works. In the high co-tier and cross-tier interference scenario, where the state of the art schemes fail to maintain the minimum required capacity of the MUE, the proposed scheme provides a minimum MUE capacity of 2 b/s/Hz, which is twice the minimum required QoS threshold. In a similar way, the proposed solution guarantees QoS up to 16 SCs which are 37.5% more SC than the previously reported works in high interference scenario while maintaining a minimum SUE capacity of 1.5 b/s/Hz, which is 33% higher than the minimum required QoS threshold. By simultaneously mitigating co-tier and cross-tier interferences in ultra-dense HetNets, the proposed solution not only improved the minimum capacities of MUEs and SUEs but also sets a new benchmark for minimum QoS threshold.

Simultaneous Wireless Information and Power Transfer for Decode-and-Forward Multihop Relay Systems in Energy-Constrained IoT Networks

This paper studies a multi-hop decode-and-forward (DF) simultaneous wireless information and power transfer (SWIPT) system where a source sends data to a destination with the aid of multi-hop relays which do not depend on an external energy source. To this end, we apply power splitting (PS) based SWIPT relaying protocol so that the relays can harvest energy from the received signals from the previous hop to reliably forward the information of the source to the destination. We aim to solve two optimization problems relevant to our system model. First, we minimize the transmit power at the source under the individual quality-of-service (QoS) threshold constraints of the relays and the destination nodes by optimizing PS ratios at the relays. The second is to maximize the minimum system achievable rate by optimizing the PS ratio at each relay. Based on convex optimization techniques, the globally optimal PS ratio solution is obtained in closed-form for both problems. By setting the QoS threshold constraint the same for each node for the source transmit power problem, we discovered that either the minimum source transmit power or the maximum system throughput can be found using the same approach. Numerical results demonstrate the superiority of the proposed optimal SWIPT PS design over conventional fixed PS ratio schemes.

User Association and Power Allocation for Multi-Cell Non-Orthogonal Multiple Access Networks

In this paper, user association and power allocation are investigated in a non-orthogonal multiple access (NOMA)-based multi-cell network. In order to perform successive interference cancellation (SIC) techniques for removing the intra-base station (BS) interference, the optimal decoding order is derived for all users associated with the same BS. In an effort to improve the system, a sum rate maximization problem is formulated by jointly designing user association and power allocation. Two game theory based algorithms are proposed to obtain the stable user structure by dividing users into different BSs’ clusters, where the sub-optimal and global optimal solutions can be achieved. The properties of the proposed algorithms, including complexity, convergence, stability and optimality, are analyzed. Based on the quality-of-service (QoS) constraint, the closed-from solutions for power allocation are derived, and thus the expressions for the sum rate of all users in each cluster is obtained. Moreover, the case that the QoS threshold cannot be achieved by all users in each cluster is considered. Simulation results demonstrate that: i) the proposed user association algorithms and the closed-form solutions for power allocation can significantly enhance the sum rate and outage probability; and ii) the proposed NOMA-based system is capable of achieving promising gains over the conventional orthogonal multiple access (OMA)-based framework in the multi-cell scenario.

High volume bus stop upstream average waiting time for working capacity and quality of service

High volume bus facilities range from on-street facilities to dedicated Bus Rapid Transit (BRT) facilities. The aim of this study is to better understand performance by directly relating critical stop bus capacity to quality of service (QOS), which measures performance from the passengers’ and operator’s perspectives. The US transit capacity and quality of service manual methodology estimates facility bus capacity based on critical stop operation using a failure rate approach. However, this approach is inaccurate under high volume conditions and the failure rate is a difficult measure to prescribe and to interpret. This research provides an improved understanding of operation by instead considering bus upstream average waiting time to measure capacity and QOS performance, because it is experienced directly by passengers and is an indicator of the impact upon general traffic. A fundamental microscopic simulation model is developed with many bus stop operational aspects occurring stochastically, including upstream arrivals. An empirical upstream average waiting time model is calibrated for five bus stop scenarios including BRT and on-street conditions. This is then used to determine working capacity based on QOS threshold upstream average waiting time. Assigning a worse threshold gives diminishing returns in working capacity, particularly for waiting times beyond 60 s. For an assigned threshold, as loading area processing time reduces, working capacity increases more markedly. This demonstrates that BRT stations are more productive than general on-street bus stops. An example shows that a policy decision exists for the operator as to which QOS threshold to accept in balance with working capacity that suits the desired schedule. It is also discussed that the model enables the operator to evaluate the impact of design or operational changes as well as timetable changes on capacity and QOS.

Convexity of Weighted Sum Rate Maximization in NOMA Systems

This letter investigates the optimal power allocation for weighted sum rate maximization (WSRM) in nonorthogonal multiple access (NOMA) systems with power order and quality-of-service (QoS) constraints. We show that the NOMA WSRM problem is a convex problem under some condition of the user weights. The optimal solution to the WSRM problem without QoS constraints is analytically characterized in two cases. Then, we investigate the feasibility of the WSRM problem with QoS constraints. We further show that the power order constraint can be omitted without loss of any optimality under some mild condition of the QoS thresholds, which enables us to derive an analytical expression of the optimal power allocation.

ReQoS

As multicore processors with expanding core counts continue to dominate the server market, the overall utilization of the class of datacenters known as warehouse scale computers (WSCs) depends heavily on colocation of multiple workloads on each server to take advantage of the computational power provided by modern processors. However, many of the applications running in WSCs, such as websearch, are user-facing and have quality of service (QoS) requirements. When multiple applications are co-located on a multicore machine, contention for shared memory resources threatens application QoS as severe cross-core performance interference may occur. WSC operators are left with two options: either disregard QoS to maximize WSC utilization, or disallow the co-location of high-priority user-facing applications with other applications, resulting in low machine utilization and millions of dollars wasted. This paper presents ReQoS, a static/dynamic compilation approach that enables low-priority applications to adaptively manipulate their own contentiousness to ensure the QoS of high-priority co-runners. ReQoS is composed of a profile guided compilation technique that identifies and inserts markers in contentious code regions in low-priority applications, and a lightweight runtime that monitors the QoS of high-priority applications and reactively reduces the pressure low-priority applications generate to the memory subsystem when cross-core interference is detected. In this work, we show that ReQoS can accurately diagnose contention and significantly reduce performance interference to ensure application QoS. Applying ReQoS to SPEC2006 and SmashBench workloads on real multicore machines, we are able to improve machine utilization by more than 70% in many cases, and more than 50% on average, while enforcing a 90% QoS threshold. We are also able to improve the energy efficiency of modern multicore machines by 47% on average over a policy of disallowing co-locations.

ReQoS

As multicore processors with expanding core counts continue to dominate the server market, the overall utilization of the class of datacenters known as warehouse scale computers (WSCs) depends heavily on colocation of multiple workloads on each server to take advantage of the computational power provided by modern processors. However, many of the applications running in WSCs, such as websearch, are user-facing and have quality of service (QoS) requirements. When multiple applications are co-located on a multicore machine, contention for shared memory resources threatens application QoS as severe cross-core performance interference may occur. WSC operators are left with two options: either disregard QoS to maximize WSC utilization, or disallow the co-location of high-priority user-facing applications with other applications, resulting in low machine utilization and millions of dollars wasted. This paper presents ReQoS, a static/dynamic compilation approach that enables low-priority applications to adaptively manipulate their own contentiousness to ensure the QoS of high-priority co-runners. ReQoS is composed of a profile guided compilation technique that identifies and inserts markers in contentious code regions in low-priority applications, and a lightweight runtime that monitors the QoS of high-priority applications and reactively reduces the pressure low-priority applications generate to the memory subsystem when cross-core interference is detected. In this work, we show that ReQoS can accurately diagnose contention and significantly reduce performance interference to ensure application QoS. Applying ReQoS to SPEC2006 and SmashBench workloads on real multicore machines, we are able to improve machine utilization by more than 70% in many cases, and more than 50% on average, while enforcing a 90% QoS threshold. We are also able to improve the energy efficiency of modern multicore machines by 47% on average over a policy of disallowing co-locations.

Multiantenna Relay Beamforming Design for QoS Discrimination in Two-Way Relay Networks

This paper investigates the relay beamforming design for quality of service (QoS) discrimination in two-way relay networks. The purpose is to keep legitimate two-way relay users exchange their information via a helping multiantenna relay with QoS guarantee while avoiding the exchanged information overhearing by unauthorized receiver. To this end, we propose a physical layer method, where the relay beamforming is jointly designed with artificial noise (AN) which is used to interfere in the unauthorized user's reception. We formulate the joint beamforming and AN (BFA) design into an optimization problem such that the received signal-to-interference-ratio (SINR) at the two legitimate users is over a predefined QoS threshold while limiting the received SINR at the unauthorized user which is under a certain secure threshold. The objective of the optimization problem is to seek the optimal AN and beamforming vectors to minimize the total power consumed by the relay node. Since the optimization problem is nonconvex, we solve it by using semidefinite program (SDP) relaxation. For comparison, we also study the optimal relay beamforming without using AN (BFO) under the same QoS discrimination constraints. Simulation results show that both the proposed BFA and BFO can achieve the QoS discrimination of the two-way transmission. However, the proposed BFA yields significant power savings and lower infeasible rates compared with the BFO method.

QoS-Based Manycasting Over Optical Burst-Switched (OBS) Networks

Many distributed applications require a group of destinations to be coordinated with a single source. Multicasting is a communication paradigm to implement these distributed applications. However in multicasting, if at least one of the members in the group cannot satisfy the service requirement of the application, the multicast request is said to be blocked. On the contrary in manycasting, destinations can join or leave the group, depending on whether it satisfies the service requirement or not. This dynamic membership based destination group decreases request blocking. We study the behavior of manycasting over optical burst-switched networks (OBS) based on multiple quality of service (QoS) constraints. These multiple constraints can be in the form of physical-layer impairments, transmission delay, and reliability of the link. Each application requires its own QoS threshold attributes. Destinations qualify only if they satisfy the required QoS constraints set up by the application. We have developed a mathematical model based on lattice algebra for this multiconstraint problem. Due to multiple constraints, burst blocking could be high. We propose two algorithms to minimize request blocking for the multiconstrained manycast (MCM) problem. Using extensive simulation results, we have calculated the average request blocking for the proposed algorithms. Our simulation results show that MCM-shortest path tree (MCM-SPT) algorithm performs better than MCM-dynamic membership (MCM-DM) for delay constrained services and real-time service, where as data services can be better provisioned using MCM-DM algorithm.

An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements

Grid computing is increasingly considered as a promising next-generation computational platform that supports wide-area parallel and distributed computing. In grid environments, applications are always regarded as workflows. The problem of scheduling workflows in terms of certain quality of service (QoS) requirements is challenging and it significantly influences the performance of grids. By now, there have been some algorithms for grid workflow scheduling, but most of them can only tackle the problems with a single QoS parameter or with small-scale workflows. In this frame, this paper aims at proposing an ant colony optimization (ACO) algorithm to schedule large-scale workflows with various QoS parameters. This algorithm enables users to specify their QoS preferences as well as define the minimum QoS thresholds for a certain application. The objective of this algorithm is to find a solution that meets all QoS constraints and optimizes the user-preferred QoS parameter. Based on the characteristics of workflow scheduling, we design seven new heuristics for the ACO approach and propose an adaptive scheme that allows artificial ants to select heuristics based on pheromone values. Experiments are done in ten workflow applications with at most 120 tasks, and the results demonstrate the effectiveness of the proposed algorithm.

Forward-Link Capacity Analysis for MC-CDMA

OFDM-based networks utilizing the frequency reuse factor of 1 may produce the severe ICI (intercell interference) at the cell boundary even though overall cell capacity is increased and network deployment is facilitated. In the forward-link, the ICI may rise above a QoS (quality of service) threshold beyond some distance from BSs (base stations). In this paper, we analyze the forward-link capacity of an MC-CDMA system as a function of the ICI according to the distance from a cell. To achieve this goal, a closed form of the outage probability is derived and utilized to obtain the accommodated number of users and system parameters.

A dynamic multiple-threshold bandwidth reservation (DMTBR) scheme for QoS provisioning in multimedia wireless networks

Next-generation wireless networks target to provide quality of service (QoS) for multimedia applications. We study the wireless systems that support two QoS requirements: keeping the handoff dropping probability less than a predefined QoS threshold while maintaining relative priorities for different traffic classes based on blocking probability. To achieve this goal, a dynamic multiple-threshold bandwidth reservation (DMTBR) scheme, which is capable of granting differential priorities to different traffic classes and to new and handoff traffic for each class by dynamically adjusting bandwidth reservation thresholds, is proposed. In this scheme, the thresholds are obtained in two steps. The initial values are estimated based on instantaneous network traffic situation, then the thresholds will be further adapted according to the instantaneous network QoS status. In times of network congestion, a preventive measure is taken to throttle the new connections. Another contribution of this paper is to generalize the concept of relative priority and hence give the network operator more flexibility to adjust admission control policy by taking into account some dynamic factors such as offered load. The extensive simulations are conducted for two purposes. First, we verify the performance of the proposed scheme and show our scheme performs well under various traffic loads. Second, we demonstrate that the DMTBR scheme gains more advantages when taking the offered load into consideration.

User-Capacity Maximization in Synchronous CDMA Subject to RMS-Bandlimited Signature Waveforms

We consider the symbol-synchronous code-division multiple-access (CDMA) channel equipped with either a multiuser linear receiver or a multiuser decision-feedback receiver. The network, or user, capacity is defined to be the number of users that can be supported with available resources, such that every user achieves a certain quality of service (QoS). In this paper, the QoS threshold is given as a signal-to-interference ratio and the bandwidth is given as the root mean squared bandwidth of the received power spectral density of the users' transmitted waveforms. Given the QoS threshold and constraints on bandwidth and the sum of the users' received powers, we maximize user capacity for both the linear and decision-feedback receivers by optimally and jointly designing the users' signature waveforms and power-control polices.

Optimal sequences for CDMA with decision-feedback receivers

We consider a symbol-synchronous code-division multiple-access (CDMA) system that is equipped with a multiuser decision-feedback receiver and for which power control is available. The users are each assigned a quality-of-service (QoS) threshold to be guaranteed by the system, and to cover scenarios for which there are multiple classes of users, these are not required to be equal to each other. For an ideal decision-feedback receiver, it is known that with enough power the system can always meet the users' QoS thresholds, so we instead minimize the sum of the users' received powers over system designs (i.e., signature sequences, power-control policy, and decision-feedback receiver) which guarantee the QoS requirements. It is found that the optimal design produces two classes of users, those whose sequences and powers satisfy with equality the generalized Welch bound inequality and those oversized users that are mutually orthogonal to each other and the rest of the users. In terms of power and bandwidth savings, the optimal sequences for the decision-feedback receiver are found to compare very favorably to optimal designs for linear receivers and to random sequences for the decision-feedback receiver.