QoS Requirements Of Users Research Articles

Robust Integrated Data and Energy Transfer Aided by Intelligent Reflecting Surfaces: Successive Target Migration Optimization Toward Energy Sustainability

Intelligent reflecting surfaces (IRSs) can actively adjust the wireless environment. However, accurate channel estimation on IRS-aided communication systems is difficult to obtain. Therefore, we study a robust beamforming design for an IRS-aided integrated data and energy transfer (IDET) with imperfect channel state information (CSI). Against the uncertain channel estimation error, we robustly design the transmit beamformers of the transmitter and the passive reflecting beamformer of the IRS to minimize the transmit power by satisfying both the wireless data transfer (WDT) and wireless energy transfer (WET) requirements for realising energy-sustainability in 6G. A successive target migration optimization (STMO) algorithm is proposed to obtain a robust design. The transmit covariance matrices are optimized by relaxing rank-one constraints, when a passive reflecting beamformer is given. Then, the target to minimize the transmit power is migrated to maximize the QoS requirements of energy users due to the fixed transmit power. A local optimal reflecting beamformer is obtained for improving the attainable WET performance, when the transmit covariance matrices are given. Finally, we prove that the rank-one transmit beamformers can always be found, which have the same WET and WDT performance as the transmit covariance matrices. The numerical results demonstrate the advantage of our design.

Preference-Aware User Access Control Policy in Internet of Things.

There are multiple types of services in the Internet of Things, and existing access control methods do not consider situations wherein the same types of services have multiple access options. In order to ensure the QoS quality of user access and realize the reasonable utilization of Internet of Things network resources, it is necessary to consider the characteristics of different services to design applicable access control strategies. In this paper, a preference-aware user access control strategy in slices is proposed, which can increase the number of users in the system while balancing slice resource utilization. First, we establish the user QoS model and slice QoS index range according to the delay, rate and reliability requirements, and we select users with multiple access options. Secondly, a user preference matrix is established according to the user QoS requirements and the slice QoS index range. Finally, a preference matrix of the slice is built according to the optimization objective, and access control decisions are made for users through the resource utilization state of the slice and the preference matrix. The verification results show that the proposed strategy not only balances slice resource utilization but also increases the number of users who can access the system.

Energy‐efficient task scheduling and resource management in a cloud environment using optimized hybrid technology

AbstractCloud computing is a significant platform emerging for IT enterprises, business applications, and mobile computing. It is a dynamic environment where efficiently and properly allocating resources in such an environment becomes a tedious task. Most existing approaches cannot guarantee energy‐efficient resource management due to the existence of time‐dependent tasks. Cloud resources consume a huge amount of energy, which may reduce the Makespan of the entire network. The process of appropriate task scheduling may satisfy the user's requirement. Reducing energy consumption by satisfying the user's QoS requirements is essential to ensure each user's service level agreement (SLA). Therefore, to achieve an energy‐efficient and unique task scheduling, a technique for order preference by similarity to ideal solution (TOPSIS) based unique ranking (uRank‐TOPSIS) and a Hybrid state‐action‐reward‐state‐action (SARSA), Reinforcement Learning with Black widow Algorithm (HSRLBA) are proposed in this paper. Here, the task scheduling process is carried out using the uRank‐TOPSIS process by producing a set of unique weights and ranking the alternatives. Also, HSRLBA is used to perform resource allocation, which uses the Black widow Algorithm (BWA) to rapidly converge parallel agents in the SARSA model of RL. The Cloudsim simulator is used for experiments. The efficiency of the proposed framework is measured in terms of Makespan, Task Completion Ratio (TCR), total energy consumption, response time and resource utilization. The proposed uRank‐TOPSIS and HSRLBA achieved an energy consumption of 325 KWh, response time of 15.42 s, Makespan of the 1150 s, TCR of 98% and resource utilization of 92%. The proposed framework maximizes TCR and resource utilization and minimizes the Makespan, energy consumption, and response time.

Robust Channel Estimation in Multiuser Downlink 5G Systems Under Channel Uncertainties

In wireless communication, the performance of the network highly relies on the accuracy of channel state information (CSI). On the other hand, the channel statistics are usually unknown, and the measurement information is lost due to the fading phenomenon. Therefore, we propose a channel estimation approach for downlink communication under channel uncertainty. We apply the Tobit Kalman filter (TKF) method to estimate the hidden state vectors of wireless channels. To minimize the maximum estimation error, a robust minimax minimum estimation error (MSE) estimation approach is developed while the QoS requirements of wireless users is taken into account. We then formulate the minimax problem as a non-cooperative game to find an optimal filter and adjust the best behavior for the worst-case channel uncertainty. We also investigate a scenario in which the actual operating point is not exactly known under model uncertainty. Finally, we investigate the existence and characterization of a saddle point as the solution of the game. Theoretical analysis verifies that our work is robust against the uncertainty of the channel statistics and able to track the true values of the channel states. Additionally, simulation results demonstrate the superiority of the model in terms of MSE value over related techniques.

Application Aware Multiple Constraint Optimal Paths for Transport Network Using SDN

The backhaul networks for the 5G networks consist of different links that handle massive heterogeneous traffic. Fulfilling the QoS requirement of users to comply with the growth and increasing revenue is a challenge for the service providers. We address QoS parameters like bandwidth and delay for the user’s transport network. We use Software-Defined Networking and solve this problem. We have added a component Flowlet based Multiple Constraint Optimal Paths (MCOP) for packet forwarding by taking input as QoS requirements and creating a logical network in backhaul transport networks when needed for the elephant traffic. Due to security reasons, the transport network cannot access L7 to identify the application type. We have added an application identification component to detect the traffic type. We use an experimental platform using mininet and OpenFlow. Our proposed algorithm has higher QoS performance than the conventional method after the primary link exhaust capacity. It takes around 20 to 30 packets to learn the traffic type up to L6 parameters. Our approach identifies 73.10% of traffic type. The multipath usage helps improve revenue growth by assuring the dynamic allocation of the resources to the customer for elephant flow with the required QoS.

Latency Aware 3D Placement and User Association in Drone-Assisted Heterogeneous Networks With FSO-Based Backhaul

Drone Base Station (DBS) is expected to play an important role in next generation cellular networks due to its maneuverability in establishing air-to-ground Line of Sight (LoS) links. Working as a mobile relay node between the Macro Base Station (MBS) and the ground users, the DBS can significantly reduce the latency of ground users by offloading traffic loads from the MBS to the DBS since a better channel condition can be provided. In this paper, we focus on how to determine the 3D location of the DBS, the user association and the bandwidth allocation policy between the MBS and the DBS in order to minimize the total average latency ratio of all users with the constraint of each user's QoS requirement and total available bandwidth. The formulated problem is a mixed integer nonconvex optimization problem, a very challenging and difficult problem. We propose a cyclic iterative algorithm to efficiently solve it by decomposing the primal problem into two subproblems, i.e., the user association and bandwidth allocation problem and the 3D DBS placement problem. In each iteration, the two sub-problems are alternatively optimized by using the output of one as the input for the other. Numerical simulation results demonstrate the significant latency ratio reduction of our proposed algorithm as compared to other baseline schemes.

Identification of Network Traffic over IOT Platforms

The Internet of Things (IoT) is a rising infrastructure of 21st century. The classification of traffic over IoT networks is attained significance importance due to rapid growth of users and devices. It is need of the hour to isolate the normal traffic from the malicious traffic and to assign the normal traffic to the proper destination to suffice the QoS requirements of the IoT users. Detection of malicious traffic can be done by continuously monitoring traffic for suspicious links, files, connection created and received, unrecognised protocol/port numbers, and suspicious Destination/Source IP combinations. A proficient classification mechanism in IoT environment should be capable enough to classify the heavy traffic in a fast manner, to deflect the malevolent traffic on time and to transmit the benign traffic to the designated nodes for serving the needs of the users. In this work, adaboost and Xgboost machine learning algorithms and Deep Neural Networks approach are proposed to separate the IoT traffic which eventually enhances the throughput of IoT networks and reduces the congestion over IoT channels. The result of experiment indicates a deep learning algorithm achieves higher accuracy compared to machine learning algorithms.

Segregation of IoT Traffic with Machine Learning Techniques

The Internet of Things (IoT) is emerging as a new infrastructure of 21st century. With the advent of cloud computing and evolution of IoT, the classification of traffic over IoT networks has attained significance importance due to rapid growth of users and devices. It is need of the hour to isolate the benign traffic from the malevolent traffic and to channelise the normal traffic to the intended destination to suffice the QoS requirements of the IoT users. A proficient classification mechanism in IoT environment should be capable enough to classify the heavy traffic in a fast manner, to deflect the malevolent traffic on time and to transmit the benign traffic to the designated nodes for serving the needs of the users. In this manuscript, machine learning and deep neural networks-based approaches are proposed for segregating the IoT traffic which eventually enhances the throughput of IoT networks and reduces the congestion over IoT channels. This paper also provides insights into the future research endeavors to channelise the normal traffic and to handle the malicious traffic

Joint resource allocation and routing optimization for spectrum aggregation based CRAHNs

As an integration of spectrum aggregation (SA) and cognitive radio Ad Hoc networks (CRAHNs), SA-enabled CRAHNs are capable of utilizing non-continuous spectrum components and is promising in improving the network performance. However, existing research on CRAHNs mainly assuming of an ideal spectrum sensing while ignoring the false alarm probability, resulting in an inaccurate capacity characterization. Hence, in this paper, we propose a more accurate capacity characterization while the detection and false alarm probability are considered. Moreover, we propose the joint optimization model for SA-enabled CRAHNs constrained by QoS requirements of primary users and network resource allocation. We propose the prim-dual method to decompose this problem into two sub-problems: a physical (PHY) layer sub-problem on CC assignment and power allocation, and a network layer sub-problem on route selection. Besides, these two sub-problems are coupled on the link capacity constraint. For sub-problem at PHY layer, we propose the genetic algorithm to obtain the optimal CC assignment and successive convex approximation method to the find the optimal power allocation. For sub-problem at network layer, we propose to apply the standard convex method to find the optimal solution. The numerical simulations demonstrate that network throughput can be improved by increasing the number of CCs. The obtained network throughput outperforms algorithms that without power control, spectrum aggregation or route optimization.

Throughput and Outage Probability Analysis for Cognitive Radio-Non-Orthogonal Multiple Access in Uplink and Downlink Scenarios

Non orthogonal multiple access (NOMA) in cognitive radio (CR) network has been recognized as potential solution to support the simultaneous transmission of both primary and secondary users. In addition, CR-NOMA can also be used to serve multiple secondary networks in overlay cognitive radio networks. The aim of our work is to increase the secondary user’s throughput without compromising in QoS requirements of the primary users. Our presented work analyses the performance of power domain NOMA in cognitive radio networks for both uplink and downlink scenarios. The primary aspect of the work is to investigate the impact of power allocation on spectrum efficiency and fairness performance of CR-NOMA. Objective function is to maximize the overall throughput under the QOS constraints of the users. We have derived closed form expressions for optimized power allocation coefficient(α) for CR-NOMA uplink and downlink communications. Parameters causing the channel outage, have been examined and conditions for outage probability is derived for CR-NOMA communication. Finally, we have presented the simulation results to validate the mathematical models that are developed for power allocation coefficient and outage probability.

A Unified Optimisation Framework for QoS Management and Congestion Control in VHTS Systems

The design of Very High Throughput Satellite (VHTS) systems will be an important milestone for future telecommunications systems in the light of the ongoing integration between satellite and 5G technologies as well as with the upcoming 6 G evolution. To this end, satellite systems will be operating at EHF frequency bands, hence requiring a special attention to the overall network design in terms of ground and space segment optimisation. In particular, the mitigation of feeder link outage events through advanced prediction techniques and the exploitation of smart gateway diversity concepts will be combined with an overarching network resources’ orchestration, jointly conceived to meet the QoS requirements of the end users. In this respect, this paper proposes a novel optimisation framework able to achieve very high throughput system figures, building on an adaptive QoS management concept. The proposed framework has been compared with other existing solutions from the literature and validated by means of extensive simulation campaigns, whose collected results show significant performance improvements.

Containers Placement and Migration on Cloud System

Currently, many businesses are using cloud computing to obtain an entire IT infrastructure remotely while delegating its management to a third party. The provider of this architecture ensures the operation and maintenance of the services while offering management capabilities via web consoles.These providers offer solutions based on bare metal or virtualization platforms (mainly virtual machines). Recently, a new type of virtualization-based on containerization technology has emerged. Containers can be deployed on bare metal servers or in virtual machines. The migration of virtual machines (VMs) and Containers in Dynamic Resource Management (DRM) is a crucial factor in minimizing the operating costs of data centers by reducing their energy consumption and subsequently limiting their impact on climate change.In this article, live migration for both types of virtualization will be studied. for that, container placement and migration algorithms are proposed, which takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, a dynamic approach is suggested based on a threshold of RAM usage for host and virtual machines in the data center to avoid unnecessary power consumption. In this paper, the proposed work is compared with VM/Container placement and migration methods, the results of the experiment indicate that using container migration instead of VMs demonstrates a reduction in power consumption, and also reduces the migration time which impacts QoS and reduces SLA violation.

Big-Data-Driven and AI-Based Framework to Enable Personalization in Wireless Networks

Current communication networks use design methodologies that prevent the realization of maximum network efficiency. In the first place, while users' perception of satisfactory service diverges widely, current networks are designed to be a "universal fit," where they are generally over-engineered to deliver services appealing to all types of users. Also, current networks lack user-level data cognitive intelligence that would enable fast personalized network decisions and actions through automation. Thus, in this article, we propose the utilization of AI, big data analytics, and real-time non-intrusive user feedback in order to enable the personalization of wireless networks. Based on each user's actual QoS requirements and context, a multi-objective formulation enables the network to micro-manage and optimize the provided QoS and user satisfaction levels simultaneously. Moreover, in order to enable user feedback tracking and measurement, we propose a user satisfaction model based on the zone of tolerance concept. Furthermore, we propose a big-data-driven and AI-based personalization framework to integrate personalization into wireless networks. Finally, we implement a personalized network prototype to demonstrate the proposed personalization concept and its potential benefits through a case study. The case study shows how personalization can be realized to enable the efficient optimization of network resources such that certain requirement levels of user satisfaction and revenue in the form of saved resources are achieved.

QoS-enhanced bandwidth reallocation mechanism based on auction model in mobile clouds

In recent years, mobile cloud has become a promising platform to offer elastic and scalable service for our daily life. Even so, the features of mobile communication introduce a series of new challenges to conventional bandwidth allocation service. In this paper, we propose an auction-based bandwidth reallocation mechanism to handle the bandwidth shifting issue which may significantly degrade user's QoS satisfactory. To take both resource provider's revenue and user's QoS requirement into consideration, we formulate the bandwidth reallocation problem as constrained optimisation problem, and then design an auction model which introduces a trade-off parameter to adjust the auction price with aiming at improving the user QoS satisfactory in terms of resource cost. Theoretical analysis indicates that the proposed auction model is capable of balancing the benefits between cloud providers and mobile users. Performance evaluation in a realistic mobile cloud shows that the proposed bandwidth reallocation mechanism outperforms existing approaches in terms of various performance metrics.

QoS-enhanced bandwidth reallocation mechanism based on auction model in mobile clouds

In recent years, mobile cloud has become a promising platform to offer elastic and scalable service for our daily life. Even so, the features of mobile communication introduce a series of new challenges to conventional bandwidth allocation service. In this paper, we propose an auction-based bandwidth reallocation mechanism to handle the bandwidth shifting issue which may significantly degrade user's QoS satisfactory. To take both resource provider's revenue and user's QoS requirement into consideration, we formulate the bandwidth reallocation problem as constrained optimisation problem, and then design an auction model which introduces a trade-off parameter to adjust the auction price with aiming at improving the user QoS satisfactory in terms of resource cost. Theoretical analysis indicates that the proposed auction model is capable of balancing the benefits between cloud providers and mobile users. Performance evaluation in a realistic mobile cloud shows that the proposed bandwidth reallocation mechanism outperforms existing approaches in terms of various performance metrics.

Uplink Low Power Based Radio Resource Management in Wireless Heterogeneous Networks

Due to the rapid rise of energy consumption and limited battery power of wireless user equipment (UE), low power communication becomes very important. Heterogeneous network deployment can significantly reduce regional power consumption. For heterogeneous network scenarios, this paper proposes an uplink low-power resource allocation scheme considering battery power limitation and user QoS requirements. Based on the assumption that each user has access to at most one base station (BS), the scheme is modeled as a mixed integer nonlinear programming (MINLP) problem. Since each user has access to one BS at most, the transmitting power matrix from the UE to the BSs (UE-to-BS matrix) is a sparse matrix. The MINLP problem can be transformed into an optimization problem searching sparse solution. In this paper, the reweighted L1 norm is introduced as a penalty function to remove the integer constraint and to regulate the sparsity of the UE-to-BS transmitting power matrix. In order to obtain the optimal resource allocation scheme, a reweighted L1 norm penalty based radio resource management (RRM) algorithm is proposed. Simulation results show that the reweighted L1 norm penalty based RRM algorithm has good convergence performance and the results are very close to optimal solutions. The proposed resource management scheme can prolong the working time of low-energy users and guarantee the QoS requirements of UE in the system.

Hybrid Solution for Container Placement and Load Balancing based on ACO and Bin Packing

Currently, data centers energy consumption in the cloud is attracting a lot of interest. One of the most approaches to optimize energy and cost in data centers is virtualization. Recently, a new type of container-based virtualization has ap-peared, containers are considered very light and modular virtual machines, they offer great flexibility and the possibility of migra-tion from one environment to another, which allows optimizing applications for the cloud. Another approach to saving energy is to consolidate the workload, which is the amount of processing that the computer has to perform at any given time. In this article, we will study the container placement algorithm that takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, we proposed a Hybrid approach for managing resources and workload based on ant colony optimization (ACO) and the first-fit decreasing (FFD) algorithm to avoid unnecessary power consumption. The results of the experiment indicate that using the first-fit decreasing algorithm (FFD) for container placement is better than ant colony optimization especially in a homogeneous systems. On the other hand the ant colony optimization shows very satisfying results in the case of workload management.

Multiclass Queueing Network Modeling and Traffic Flow Analysis for SDN-Enabled Mobile Core Networks With Network Slicing

The back-haul networks of 5G are formed by heterogeneous links which need to handle massive traffic. The service providers are not able to provide good QoS for their users. The technology like Software Defined Networks(SDN) and Network Slicing helps a little for a service provider to providing QoS for multiple links. The service providers face a challenge in the efficient utilization of resources to fulfill the QoS requirement of users to comply with the growth and thereby increasing the revenue. These problems require an accurate traffic model to determine the steady-state of the system. The proposed model uses an architecture that has the combination of two technologies: SDN and network slicing, which empowers an administrator a flexible, programmable network, and the best management of network resources. Heterogeneous application is well managed by creating multiple logical networks called slicing. The slicing can be modeled using multi-class queuing networks. These technologies encourage service providers to fulfill QoS and revenue growth. To leverage the benefits of these technologies in allocating QoS is to identify the performance of the system, which requires a precise model of traffic to decide the steady-state condition of the framework. In this paper, we focus on SDN and slicing in mobile networks and quantify the performance measure considering an in-band OpenFlow architecture for a single node and homogeneous traffic class, which is further extended to the multi-class heterogeneous class queuing model and analyzed. The results obtained help a service provider to monitor the utilization of resources in every node by every class of core network, which in turn helps to allocate the resources precisely to fulfill QoS requirements

Research on 3D Wireless Sensor Networks ISC-EAR Routing Algorithm

In order to improve the performance of 3D wireless sensor networks, an Iterative Split Clustering Energy Angle Routing (ISC-EAR) Algorithm is proposed. The design idea is discussed theoretically, and the design idea is as follows: on the basis of the link bandwidth and other meeting user QoS requirements, all nodes within the node perception radius or communication radius are regarded as next hop candidates. Select the next hop node according to the total energy consumption of the node (including sending and receiving energy consumption), and try to ensure that the farthest transmission distance consumes the least energy. Select the current node, the destination node and the next hop node, the space vector with the current node as the vertex has the smallest angle, that is, the candidate node closest to the destination node as the next hop node. In order to verify the performance of the algorithm, use C/C++ for programming simulation. Through three different topological structures of mine topology, average topology and random topology, the performance evaluation is carried out using four indicators: the number of alive nodes, network lifetime, network energy consumption and average energy of node. Through simulation calculation, the ISC-EAR routing algorithm has good technical performance advantages compared with the benchmark routing algorithm IGreedy, which can increase the survival time of nodes, reduce network energy consumption, and prolong the survival time of the network. It has better advancement.

Revised reinforcement learning based on anchor graph hashing for autonomous cell activation in cloud-RANs

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes to balance energy consumption and QoS satisfaction in wireless networks. We formulate the cell activation problem as a Markov decision process and set up a revised reinforcement learning model based on K-means clustering and anchor-graph hashing to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and user mobility. Extensive simulations are conducted to show the effectiveness of our proposed solution compared with existing schemes.