Diverse Quality Of Service Research Articles

Enhancing Downlink QoS and Energy Efficiency Through a User-Centric Stienen Cell Architecture for mmWave Networks

This paper presents an analytical framework for performance characterization of a novel Stienen cell based user-centric architecture operating in millimeter wave spectrum. In the proposed architecture, at most one remote radio head (RRH) is activated within non overlapping user equipment (UE)-centric Stienen cells (S-cells) generated within the Voronoi region around each UE. Under the presented framework, we derive analytical models for the three key performance indicators (KPIs): i) SINR distribution (used as an indicator for quality of service (QoS)), ii) area spectral efficiency (ASE), and iii) energy efficiency (EE) as a function of the three major design parameters in the proposed architecture, namely UE service probability, S-cell radius coefficient and RRH deployment density. The analysis is validated through extensive Monte Carlo simulations. The simulation results provide practical design insights into the interplay among the three design parameters, tradeoffs among the three KPIs, sensitivity of each KPI to the design parameters as well as optimal range of the design parameters. Results show that compared to current non user-centric architectures, the proposed architecture not only offers significant SINR gains, but also the flexibility to meet diverse UE specific QoS requirements and trade between EE and ASE by dynamically orchestrating the design parameters.

On Providing Differentiated Service Exploiting Multi-Instance RPL for Industrial Low-Power and Lossy Networks

The rapid proliferation of low-power wireless devices enables the industrial users to improve the productivity and safety of the plants as well as efficient management of the system. This can be achieved through significant increase in data collection, remote monitoring, and control of the plants and promoting the development of industrial Internet of Things (IoT) applications. However, the industrial environment is typically harsh causing high link quality variations and topology changes. The wireless devices used in this environment are also resource constrained in terms of energy, memory, and processing power. In spite of their low-power and lossy nature, these networks demand provisioning of differentiated services for various industrial applications having diverse quality of service (QoS) requirements. Considering the unique characteristics of low-power and lossy networks (LLN), routing for low-power and lossy networks (RPL) is devised which was standardized by IETF in 2012. To meet the demand of diverse traffic, RPL supports multiple instances in a single network. This paper proposes MI-RPL, a multi-instance solution of RPL for industrial low-power and lossy networks (LLNs). MI-RPL defines four instances for four distinct traffic classes of industrial monitoring applications in terms of delay and reliability. MI-RPL also introduces composite routing metrics and proposes an objective function (OF) to compute the most suitable path for each instance. The performance of MI-RPL is investigated through simulations that exhibit MI-RPL has better delay and packet delivery performance for delay- and reliability-constrained traffic along with lower energy consumption compared to the standard RPL.

Evolutionary many-objective assembly of cloud services via angle and adversarial direction driven search

Cloud service composition (CSC) is an effective way to carry out large-scale complicated applications by the ensemble of existing individual services. Each service typically involves several Quality of Service (QoS) criteria contracted for non-functional aspects like time or price, among others, which greatly influence the overall performance of the resulting applications. Service composition approaches have emerged as an important technique in leveraging the quality of composite service efficiently and have attracted significant attention. However, most existing proposals ignore the many-objective nature of CSC and consider up to three objectives, the optimization of diverse QoS aspects of CSC from a many-objective perspective (at least four) still lacks. On another aspect, due to the rapid growth of nondominated solutions in high-dimensional objective spaces, the traditional multi-objective optimization algorithms are usually not capable of handling problems possessing many objectives. To address the above issue, we develop an angle and adversarial direction based optimizer for many-objective CSC scenarios, which evolves a number of subpopulations with adversarial search directions in a parallel paradigm. Additionally, vector angle based selection criterion, which adaptively captures beacon individuals, is utilized to diversify the population. Extensive experiments are carried out on a series of CSC instances utilizing synthetic datasets and the results show that our proposition is competitive and has better versatility compared with the state-of-the-art.

Evolutionary algorithms for many-objective cloud service composition: Performance assessments and comparisons

Service composition and optimal selection (SCOS) concerns the building of optimal composite service by integrating existing services with the aim of performing complex task. Due to a plethora of affordable cloud services providing similar functionalities while differing in quality of service (QoS), how to determine suitable candidates to orchestrate the best composite service, also known as QoS-aware SCOS problem, becomes more complicated. A number of evolutionary optimizers have been developed to resolve SCOS. Unfortunately, a large majority of these optimizers carry out the optimization by aggregating many diverse QoS attributes into a single objective or simply considering two or three representative QoS attributes. SCOS, particularly, from the perspective of many-objective optimization, has not received an appropriate attention. As more factors come into play, SCOS is strictly a many-objective problem. This study explores the scalability of recently state-of-the-art evolutionary many-objective optimization (EMaO) algorithms in addressing SCOS. Comparative results reveal that these EMaO algorithms, never before applied to many-objective SCOS, exhibit distinct search abilities with respect to the objective space dimensionality and problem scale. Based on the empirical observation, useful suggestions and insights for choosing suitable EMaO algorithms pertaining to different SCOS problems are given.

Outage probability and ergodic capacity analysis of uplink NOMA cellular network with and without interference from D2D pair

We consider the framework of uplink transmission and interference constraint for new system model to provide multiple access scheme for fifth generation (5G) wireless communications. In particular, such considered system contains normal cellular users deploying a non-orthogonal multiple access (NOMA) scheme and underlay device-to-device (D2D) users deploying traditional orthogonal multiple access (OMA). Such potentials motivate us consider uplink in cellular network in which the base station (BS) is required to fulfill two NOMA users’ diverse quality of service (QoS) requests under location arrangement with nearby D2D user pair. With regard to enhanced successive interference cancellation (SIC) scheme, the BS still performs the signal detection of NOMA users based on the decoding order and even though existence of an extra interference from D2D user pair. In term of quality evaluation, the ergodic capacity and outage probability are two underlying metrics need be investigated systematically. As important results, detailed derivations are provided in a scenario that considering coexistence of D2D users and NOMA users. As extended scenario, multiple antenna NOMA users exhibit improved performance and the related comparisons are provided. Finally, simulations in various scenarios are conducted to corroborate the precision of the mathematically derivations and to confirm potential of deployment in NOMA with SIC operation.

Comparative analysis of LTE backbone transport techniques for efficient broadband penetration in a heterogeneous network morphology

In the bid to bring about a solution to the nagging problem associated with the provision of ubiquitous broadband access, Next Generation Network (NGN) popularly referred to as Long Term Evolution (LTE) network with appropriate network integration technique is recommended as solution. Currently, Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) is the transport technique in LTE backbone infrastructure. This technique, however, suffers significantly in the event of failure of IP path resulting in delay and packet loss budgets across the network.The resultant effect is degradation in users’ quality of service (QoS) experience with real-time services. A competitive alternative is the Internet Protocol /Asynchronous Transfer Mode (IP/ATM). This transport technique provides great dynamism in the allocation of bandwidth and supports varying requests of multimedia connections with diverse QoS requirements. This paper, therefore, seeks to evaluate the performance of these two transport techniques in a bid to establish the extent to which the latter technique ameliorates the aforementioned challenges suffered by the previous technique. Results from the simulation show that the IP/ATM transport scheme is superior to the IP/MPLS scheme in terms of average bandwidth utilization, mean traffic drop and mean traffic delay in the ratio of 9.8, 8.7 and 1.0% respectively.

A Multiobjective QoS Model for Trading Cloud of Things Resources

The emerging Cloud of Things (CoT) paradigm promises to meet the diverse requirements of many real-world applications, which previously could not be fulfilled by either cloud computing or Internet of Things (IoT). Trading CoT resources is a challenging aspect, particularly when managing quality of service (QoS) as resource providers and application developers have different priorities. This article focuses on the challenge of supporting QoS when trading CoT resources and performing resource allocation. The contributions of this article are: 1) the problem of managing QoS while trading CoT resources is investigated as an optimization problem; 2) a QoS model is proposed to solve the problem by optimizing five different QoS objectives; and 3) experimental evaluation of the proposed model using three optimization algorithms. The evaluation results show the efficiency and dynamism of the proposed model in optimizing CoT resource allocation based on diverse QoS objectives, including resource cost, energy consumption, response time, fault tolerance, and resource coverage.

A Prioritized Load Aware Weighted Round Robin (PLAWRR) algorithm in Broadband Wireless Networks

Broadband Wireless networks (BWNs) provide a reliable internet access for the delivery of high-speed multimedia applications. The BWNs such as WiMAX provides quality of service (QoS) support for heterogeneous service classes with diverse QoS requirements. Scheduling algorithm is one of the mechanisms used to assure QoS. The existing scheduling algorithm uses a priority value to prioritize traffics according to varying traffic conditions. However, it wastes network resources due to failure to consider channel conditions; thus, lead to increase in delay and packet loss as well as decrease in throughput. In this paper, a prioritized load aware weighted round robin (PLAWRR) algorithm is proposed to improve resource utilization. The PLAWRR algorithm employs a modified value priority according to not only traffic load but also channel condition and throughput history for traffics prioritization. It also introduces a new dynamic weight according to the prioritization value. The performance of the proposed algorithm is evaluated using simulations. The results show that the proposed PLAWRR achieves superior performance compared to the existing algorithm in terms of delay and packet loss as well as increase in throughput.

Prioritized Clock Synchronization for Event Critical Applications in Wireless IoT Networks

With the proliferation of the Internet of Thing (IoT) technologies in vertical industry applications, provisioning of accurate synchronization and low latency communication has become critical for dense wireless IoT networks to support distributed sensing and control. Due to the contention-based channel access, achieving accurate synchronization in most of unlicensed wireless IoT networks could be extremely challenging. Specifically, the critical challenge at medium access control (MAC) layer for dense IoT communication is how to eliminate random access delay while supporting a large number of heterogeneous nodes with diverse quality of service (QoS) requirements. In this paper, we propose an efficient MAC protocol for supporting distributed synchronization through guaranteed channel access for time-critical sensor nodes in dense wireless IoT networks. The proposed protocol assigns time slots to timestamp packets of the time-critical nodes using a prioritized channel access mechanism, and also guarantees channel access in event-based situations. In addition, the proposed protocol also provides deterministic scheduling for the scenarios where the delay bound of a certain priority traffic changes based on the circumstances of the emergency situation. Our results show that the proposed scheme significantly improves the synchronization precision of the event critical sensor nodes, and also enhances the reliability of overall IoT networks.

A novel hybrid narrowband/wideband networking waveform physical layer for multiuser multiband transmission and reception in software defined radio

Increasing demands of adaptability and reconfigurability impose many challenges on the design of Software Defined Radio (SDR) waveforms for highly heterogeneous wireless networks. Usually, narrowband waveforms provide robustness and work for longer distances but lack in providing higher throughput, whereas wideband waveforms provide much higher throughput but they are not much robust and operate for smaller distances. A heterogeneous SDR network having diverse Quality of Service (QoS) and range requirements, and channel conditions cannot fully rely on one of the narrowband or wideband waveform. In this paper, a novel hybrid narrowband/wideband networking waveform physical layer is proposed. The proposed waveform is based on simultaneous transmission and reception of signals having multiple bandwidths through minimal changes in the existing analog wideband front end. A digital front end architecture is proposed where sample rate conversion and channelization of multiple signals at the digital front end formulate a composite signal which is then transmitted by using the wideband RF front end settings of a wideband waveform. At the receiver, the composite signal is received by using the wideband RF front end configured to wideband mode. Individual downconversion and filtering of each of the respective signal is done digitally. In this way, multiple signals having bandwidths as per their QoS and range requirements can be transmitted/received through the same wideband front end. As a proof of concept, the proposed idea is validated on actual SDR platform by taking actual dumps from FPGA and DSP. The presented results show the viability of the proposed hybrid SDR waveform.

Standing on the shoulders of giants: Seeding search-based multi-objective optimization with prior knowledge for software service composition

Search-Based Software Engineering, in particular multi-objective evolutionary algorithm , is a promising approach to engineering software service composition while simultaneously optimizing multiple conflicting Quality-of-Service (QoS) objectives. Yet, existing applications of evolutionary algorithms have failed to consider domain knowledge about the problem into the optimization, which is a perhaps obvious but challenging task. This paper aims to investigate different strategies of exploring and injecting knowledge about the problem into the Multi-Objective Evolutionary Algorithm (MOEA) by seeding . Further, we investigate various factors that contribute to the effectiveness of seeding, including the number of seeds, the importance of crossover operation and the similarity of historical problems. We conduced empirical evaluations with NSGA-II, MOEA/D and IBEA based on a wide spectrum of problem instances, including 10 different workflow structures, from 5 to 100 abstract services and 510 to 5.529 × 10 203 candidate concrete services with diverse QoS on latency, throughput and cost, which was chosen from the real-world WS-DREAM dataset that contains 4500 QoS values. We found that, (i) all seeding strategies generally outperform their non-seeded counterparts under the same search budget with large statistical significance. Yet, they may involve relatively smaller compromise on one or two of the quality aspects among convergence, uniformity and spread. (ii) The implication of the number of seeds on the service composition problems is minimal in general (except for IBEA). (iii) In contrast to the non-seeded counterparts, the seeding strategies suffer much less implications by the crossover operation. (iv) The differences of historical problems, which are important for two proposed seeding strategies, can indeed affect the results in a non-linear manner; however, the results are still greatly better than the non-seeded counterparts even with up to 90% difference of the problem settings. The paper concludes that (i) When applying the seeding strategies, the number of seeds to be placed in is less important in general, except for the pre-optimization based strategies under IBEA. (ii) Eliminating or having less crossover is harmful for multi-objective service composition optimization, but the seeding strategies are much less sensitive to this operator than their non-seeded counterparts. (iii) For the history based seeding strategies, the seeds do not have to come from the most similar historical composition problem to achieve the best HV value, but a largely different historical problem should usually be avoided, unless they are the only available seeds.

Optimal Cross-Layer Resource Allocation for Critical MTC Traffic in Mixed LTE Networks

Machine-type communications (MTC) play an important role in implementing and enabling the Internet of Things. Long -term evolution (LTE) is a strong candidate technology for the interconnection of the MTC devices. However, to optimize LTE for MTC purposes, several issues need to be addressed. This is due to the different and diverse quality of service (QoS) requirements of MTC compared to those of human-to-human (H2H) communications. In particular, critical-MTC pose many challenges to radio resource management in LTE. They have stringent QoS requirements that need to be considered without sacrificing the QoS of the H2H traffic. In this paper, we formulate the resource allocation optimization problem from a cross-layer design perspective to consider both the QoS requirements of critical-MTC and those of H2H communications. We propose methods to handle the optimization problem to reduce the computational complexity of the optimal solution. Additionally, the performance of the proposed resource allocation algorithms is evaluated analytically. Moreover, more computationally efficient algorithms are proposed to practically implement the resource allocation process in several operational cases. Finally, the computational complexity of the proposed algorithms is analyzed. The simulations results show the superiority of the proposed algorithms and methods compared to other techniques from the literature.

Device-Aware Cell Association in Heterogeneous Cellular Networks: A Matching Game Approach

Heterogeneous cellular networks (HCNs) provide a promising paradigm for supporting different types of devices that have diverse quality of service (QoS) requirements, such as Internet of Things devices (IoTDs) and human-to-human devices (H2HDs). In this paper, a distributed cell association algorithm is developed to consider the dissimilar association requirements for IoTDs and H2HDs coexisting in HCNs. The device association process is formulated as a multi-objective optimization problem to minimize the uplink (UL) transmit power for IoTDs and maximize the downlink (DL) data rate for H2HDs while considering the various devices’ QoS requirements. To solve this problem, an approach dependent on matching theory is proposed to model the interactions between devices and base stations in the network. Using this approach, a distributed algorithm is developed to provide the device association solution. The proposed algorithm is then proved to converge to a stable matching. Simulation results validate the effectiveness of the proposed algorithm in improving the UL power performance for IoTDs as well as DL rate performance for H2HDs compared to other association algorithms.

An Efficient Partially Overlapping Channels Assignment for Smart Grid IoT With Differentiated QoS

In smart grid (SG), internet of things (IoT) has drawn much attention around the years due to increased volume of traffic. Various types of electrical devices in SG demand a differentiated quality of service (QoS). However, the spectrum resource for SG wireless communication is quite limited. The network performance and spectrum utilization in a SG-IoT network can be greatly enhanced by adopting wireless mesh networks (WMNs) with multi-radio multi-channel (MRMC) and partially overlapping channels (POCs). In this paper, a game-based efficient POCs assignment algorithm is proposed to satisfy the diverse QoS and improve the spectrum utilization efficiency. The utility function in the proposed game model is taken both of the differentiated QoS and interference into consideration to optimize the network capacity. Then Nash Equilibrium (NE) of the game model is analyzed, and a variable learning step algorithm (VLSCA) is designed to improve its convergence speed. In the simulations, seven SG application services divided into four priority levels are considered to compare with the other two typical channel assignment algorithms. The results show that our algorithm has better performance to ensure a higher priority level traffic with lower end-to-end delays and packet loss ratios.

PCF-Based LTE Wi-Fi Aggregation for Coordinating and Offloading the Cellular Traffic to D2D Network

Device-to-device (D2D) communication is a promising technology towards 5G networks. D2D communication can offload traffic using licensed/unlicensed band by establishing a direct communication between two users without traversing the base station or core network. However, one of the major challenges of D2D communication is resource allocation and guaranteeing quality-of-service (QoS). In this paper, we establish an optimal queuing scheduling and resource allocation problem for three-tier heterogeneous network based on LTE Wi-Fi aggregation, to offload voice/multimedia traffic from licensed band to unlicensed band using scalable MAC protocol (SC-MP) under various static delay constraints. The access mechanism used for Wi-Fi in SC-MP is point coordination function, which further offloads the multimedia traffic using D2D communication in unlicensed band. Resource allocation and optimal joint queuing scheduling problems are formulated with diverse QoS guarantee between licensed and unlicensed band to minimize the bandwidth of licensed band. Furthermore, an iterative algorithm is proposed to express the nonconvex problem as a series of subproblems based on block coordinate descent and difference of two convex functions (D.C) program. We have simulated the proposed scheme using two scenarios: voice traffic using licensed band and voice traffic using both licensed and unlicensed band, whereas multimedia traffic uses unlicensed band for both the scenarios. The simulation results show that both the schemes perform better than the existing scheme and scenario 2 outperforms scenario 1.

User Association for Backhaul Load Balancing With Quality of Service Provisioning for Heterogeneous Networks

This letter addresses the user association problem for quality of service (QoS) provisioning and backhaul load balancing (LB) in heterogeneous networks (HetNets). This problem is exacerbated by base stations with different backhaul capacities and users with diverse QoS requirements. A user association scheme is proposed to achieve QoS provisioning and backhaul LB for HetNets. Simulation results show that the proposed scheme outperforms conventional user association schemes in terms of call blocking probability, QoS, and backhaul LB.

Real‐time multiuser scheduling based on end‐user requirement using big data analytics

SummaryWith the rapid growth in wireless data networks and increasing demand for multimedia applications, the next generation of wireless networks should be able to provide services for heterogeneous traffic with diverse quality of service (QoS) requirements. Multiuser diversity refers to a type of diversity present across different users in a fading environment. This diversity can be exploited by scheduling transmissions so that users transmit when their channel conditions are favorable. Hence, scheduling algorithms that support QoS and maintain a required throughput to ensure users' satisfaction are crucial to the development of these wireless networks. In this paper, different scheduling techniques have been evaluated using OFDM with different scenarios. The goal is to analyze the properties of networks such as throughput, fairness, and delay. Experimental results indicate that the PFS approach outperforms the other techniques in terms of fairness, throughput, and delay.

Evaluating the QoS Cognizance in Composition of Cloud Services: A Systematic Literature Review

Enterprise level computing constantly investigates novel approaches that maximize their profits and minimize their expenses. With the rapid growth of cloud computing XaaS – ‘anything as a service’, service providers are enabled with the rapid deployment of virtual services to service requestors. Because of the enormous growth in the variety of the services and based on the demand of the virtualized resources, cloud service providers are facing tough competition to facilitate the composite service requests made by the service requestors. QoS (Quality of Service) is considered to be a preliminary factor while composing a new cloud service out of heterogeneous and distributed atomic services. Therefore service composition is promising area that focuses on the design and development of the automated approaches to deal with diverse phases of service composition techniques that include service discovery, negotiation, service selection and optimization of the atomic services. This paper provides anatomy of existing studies addressing the problem of cloud service composition that enable to identify intended objectives of the technique along with diverse QoS aware problem solving approaches. Furthermore, the key areas of the improvement in cloud service composition are identified for future research.

Smart Multi-RAT Access Based on Multiagent Reinforcement Learning

The ongoing increasing traffic in the era of big data yields unprecedented demands in user experience and network capacity expansion. The users of next generation mobile networks (5 G) should be able to use 3GPP, IEEE, and other technologies simultaneously. The integration of multiple radio access technologies (RATs) of licensed or unlicensed bands has been widely deemed as a cost-efficient way to greatly increase the network capacity. In this paper, we propose a smart aggregated RAT access (SARA) strategy with aim of maximizing the long-term network throughput while meeting diverse traffic quality of service (QoS) requirements. We consider the scenario that users with different QoS requirements access to a heterogeneous network with coexisting cellular-WiFi. In order to maximize system throughput while meeting diverse traffic QoS requirements in such a complex and dynamic environment, we exploit multiagent reinforcement learning to perform RAT selection in conjunction with resource allocation for individual user access requests, through sensing dynamic channel states and traffic QoS requirements. In SARA, we first use Nash Q-learning to provide a set of feasible RAT selection strategies while decreasing the strategy space in learning process, and then employ Monte Carlo tree search (MCTS) based Q-learning to perform resource allocation. Numerical results reveal that the network throughput can be maximized while meeting various traffic QoS requirements with limited number of searches by using our proposed SARA algorithm. For bulk arrival access requests, a suboptimal solution can be obtained as high computational complexity is incurred for achieving global optimality. Another attractive feature of SARA is that a tradeoff between the solution optimality and learning time can be readily made by terminating the search of MCTS according to the time constraint. Compared with traditional WiFi offloading schemes, SARA can significantly improve network throughput while guaranteeing traffic QoS requirements.

Radio Resource Management in Machine-to-Machine Communications—A Survey

In futuristic wireless communications, a massive number of devices need to access networks with diverse quality of service (QoS) requirements. It is estimated that the number of connected devices will exceed 20 billions in 2020, and machine-to-machine (M2M) devices will account for nearly half of total connected devices. However, existing cellular systems and wireless standards, designed primarily for human-to-human (H2H) communications focusing on reducing access latency, increasing data rate, and system throughput, are not well suited for M2M communications that require massive connections, diverse QoS requirements, and low energy consumption. Radio resource management (RRM) in conventional H2H communications aims at improving spectrum efficiency and energy efficiency. Similarly, RRM also plays a vital role in M2M communications. In this paper, we make a comprehensive survey on state-of-the-art research activities on RRM in M2M communications. First, we discuss the issues on RRM for machine-type communications in LTE/LTE-A cellular networks including access control, radio resource allocation, power management, and the latest 3GPP standards supporting M2M communications. Acknowledging the fact that a single technology can not support all M2M applications, we discuss RRM issues for unlicensed band radio access technologies in M2M capillary networks, including IEEE 802.11ah, Bluetooth low energy, ZigBee, and smart metering networks. We also survey M2M RRM methods in heterogeneous networks consisting of cellular networks, capillary networks, and ultra dense networks. Finally, we review recent standard activities and discuss the open issues and research challenges.