Quality Of Service Scheduling Research Articles

Estimating the composition ratios of network services carried in mixed traffic

Network performance management (NPM) and quality of service (QoS) guarantee are the key concerns of network operators and service providers/consumers, respectively. The basis for traffic scheduling is one of the vital elements guiding the implementation of both NPM and QoS assurance. Although traffic identification technologies can theoretically be applied to providing basis for traffic scheduling, the computational complexity caused by the flow-by-flow processing makes them unsuitable for actual large-scale high-throughput network scenarios. In this work, we introduce a new scheme to provide a basis for service-oriented NPM and QoS guarantee. The proposed scheme treats the mixed traffic as a whole and estimates the composition ratios of network services carried in mixed traffic, which makes traffic scheduling for NPM and QoS more reasonable and flexible. To estimate the composition ratios, the observed mixed traffic is expressed as a bipartite graph according to the communication relationship. Then, the bipartite graph is further projected to a multi-dimensional hyper-image based on node coding, random walking and embedding mapping. By this way, we convert the service composition ratio estimation into a problem similar to image classification and enable it to be solved by general machine learning methods like convolutional neural networks. The proposed scheme facilitates batch traffic scheduling and can reorganize the distribution of compositions of network services for each link based on given management strategies. Its computational complexity is significantly lower than that of flow-by-flow processing methods. We validate the proposed solution via real network data and evaluate its performance with some benchmark methods based on traffic identification. The experimental results show that the proposed solution can accurately infer the service composition ratios of mixed traffic with low computational complexity.

Joint QoS and energy-efficient resource allocation and scheduling in 5G Network Slicing

Network Slicing (NS) is fast evolving as a prominent enabler for providing tailored services in the Fifth Generation of cellular networks (5G). Network Slices are virtualized network entities formed over physical substrates, deployed for the customized application use cases. A Network Slice needs to exhibit end to end capabilities and meet Quality of Service (QoS) specifications and Service Level Agreements (SLAs). To provide end-to-end traffic management capabilities in the network slice, firstly, traffic flows are categorized into different priority traffic classes, and their severity levels are assessed. Priorities can be applied across cellular and IP based systems. Machine Learning (ML) algorithms are employed on QoS profile attributes in establishing traffic priorities in slices. Secondly, we propose a novel algorithm for NS Resource Partitioning and User Allocation. We put forward an online virtual backbone based solution for resource allocation and priority class-based packet scheduling. This joint QoS and energy efficiency driven approach is built on top of established traffic classes and dynamic power savings techniques. Finally, through Cognitive Cycles (CC), we devise better network re-configuration to obtain more energy savings. Traffic classifier modules are implemented using Jupyter notebook and Python API. Scheduling and resource allocation modules in networks slices are emulated in Mininet, Flowvisor, and Beacon and POX controllers. The simulation results reveal the reduced node consumption is achieved through the evolutionary CC algorithm, and it outperforms other standard approaches by at least 23%. Similarly, for the traffic priority prediction, from the results, we could infer Gradient Boosting and Random Forest Regressors exhibit superior accuracy with the root mean square deviation of 2.2% and 1.2% respectively when compared to other standard ML algorithms.

A Study of Scheduling Algorithms in LTE -Advanced HetNet

Long Term Evolution (LTE), known as 4G technology, is considered the choice for Third Generation Partnership Project (3GPP) and 3GPP2 mobile operators. LTE is expected to bring an improved user experience with full mobility support.   However, to cope with the increasing number of mobile data subscribers that compete for the limited radio resources, effective network Quality of Service (QoS) mechanisms are necessary. A solution is to introduce small cells by adding low-power base stations to the existing macro- eNBs (Evolved Node B). The result is a Heterogeneous Network (HetNet) with large macro-cells combined with small cells (micro-, pico- and femto-cells) to cover areas not covered by the Macro-cells. Practically, HetNet is introduced in LTE-Advanced which is an extended version of LTE with more advanced features. Introducing HetNet in LTE-A brings challenges especially in terms of physical integration, QoS support and interference management. In this research, an architecture of HetNet integration is proposed. After that, some QoS scheduling algorithms are deployed for different types of on-going applications. Then, through the use of Riverbed Modeler, their performance parameters are studied with extensive simulations. Finally, an intensive comparison is done among the algorithms followed by a detailed discussion.

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach.

The demand for bandwidth-intensive and delay-sensitive services is surging daily with the development of 5G technology, resulting in fierce competition for scarce radio resources. Power domain Nonorthogonal Multiple Access (NOMA) technologies can dramatically improve system capacity and spectrum efficiency. Unlike existing NOMA scheduling that mainly focuses on fairness, this paper proposes a power control solution for uplink hybrid OMA and PD-NOMA in dual dynamic environments: dynamic and imperfect channel information together with the random user-specific hierarchical quality of service (QoS). This paper models the power control problem as a nonconvex stochastic, which aims to maximize system energy efficiency while guaranteeing hierarchical user QoS requirements. Then, the problem is formulated as a partially observable Markov decision process (POMDP). Owing to the difficulty of modeling time-varying scenes, the urgency of fast convergency, the adaptability in a dynamic environment, and the continuity of the variables, a Deep Reinforcement Learning (DRL)-based method is proposed. This paper also transforms the hierarchical QoS constraint under the NOMA serial interference cancellation (SIC) scene to fit DRL. The simulation results verify the effectiveness and robustness of the proposed algorithm under a dual uncertain environment. As compared with the baseline Particle Swarm Optimization algorithm (PSO), the proposed DRL-based method has demonstrated satisfying performance.

QoS-DPSO: QoS-aware Task Scheduling for Cloud Computing System

Dynamic provisioning of computational resources in cloud computing system is a challenging problem. With many VMs being deployed in clouds, managing so much resources to stable work becomes a critical problem. Therefore, how to meet the different quality of service (QoS), we consider the service quality requirements is very important. In this paper, we address this problem. We first establish a QoS scheduling model by incorporating the cloud characteristics, and then, we develop a task scheduling objects to ensure faults can be tolerated during the task exaction. Finally, we proposed a QoS-aware scheduling algorithm, QoS-DPSO, to satisfy the QoS required in cloud computing systems. For the target requirements of QoS requirement, we take the time, reliability and cost as a single object problem. Experimental results show that QoS-DPSO can effectively improve the performance and obtain the high reliability.

Recent Progress on QoS Scheduling for Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) use algorithms that schedule transmissions in a fair and efficient manner. A multihop scheduler schedules transmissions so that the channel utilization is maximized while guaranteeing the quality of service (QoS) for all nodes. QoS-based scheduling in MANETs must be obtained under time-critical conditions as these networks have several features that produce unique queuing dynamics. Schedulers in MANETs take into account various QoS parameters such as end-to-end packet delay, packet delivery ratio, flow priority, etc. Also, scheduling in MANETs takes many forms such as distributed priority, fair, opportunistic, etc. This article provides a survey of scheduling techniques for MANETs and discusses the advantages and disadvantages of each category.

Enhancing performance of feedback-Based QoS scheduling for video delivery over WLANs

In IEEE 802.11e wireless local area networks (WLANs), it is well known that a feedback signaling for bandwidth allocation can help to increase bandwidth utilization. In order to evaluate feedback-based quality of service (QoS) scheduling in IEEE 802.11e WLANs, an analytical model is presented in this paper to calculate the rate of packet losses as a consequence of violation of their delay limits. Furthermore, an enhancement is proposed to reduce packet losses by speeding up the feedback signaling. It is demonstrated from both analytical results and ns-2 simulations that the packet loss rate can effectively be reduced under the proposed enhancement and more capacity for video flows can accordingly be obtained.

LBBA: An efficient online benefit-aware multiprocessor scheduling for QoS via online choice of approximation algorithms

Maximizing the benefit gained by soft real-time jobs in many applications and embedded systems is highly needed to provide an acceptable QoS (Quality of Service). This paper considers a benefit model for on-line preemptive multiprocessor scheduling. The goal is to maximize the total benefit gained by the jobs that meet their deadlines. This method prioritizes the jobs using their benefit density functions and schedules them in a real-time basis. We propose an online choice of two approximation algorithms in order to partition the jobs among identical processors at the time of their arrival without using any statistics. Our analysis and experiments show that we are able to maximize the gained benefit and decrease the computational complexity (compared to existing algorithms) while minimizing makespan (response time, also referred to as cost), with fewer missed deadlines and more balanced usage of processors. Our solution is applicable to a wide variety of soft real-time applications and embedded systems such as, but not limited to multimedia applications, medical monitoring systems or those with higher utilization such as bursty hosting servers.11This research is done by Behnaz Sanati and supervised by Professor Albert Cheng.

Packet Scheduling and Access Priority Control for QoS and Fairness in Wireless LAN

Abstract In recent years, the increasing demand for multi-media communication, even in the wireless mobile environment overcrowding, QoS (Quality of Service) is important. In such environment, since many mobile stations (STAs) are using the same channel, it occurs the problem of unfair channel access time and downlink channel. In this paper, we propose a scheme to achieve a good balance of QoS, fairness and downlink/uplink traffic asymmetry by the combined operation of channel access priority control and packet scheduling with require modification only to AP and require no changes in STAs.

Uplink QoS Scheduling Algorithm with Delay Estimation for LTE Systems

In this paper, we propose a quality of service (QoS) uplink scheduling algorithm for long term evolution (LTE) that collaborates with delay estimation. Unlike downlink scheduling, uplink scheduling cannot incorporate packet delay information due to specification constraints of LTE. The limited information results in difficulty ensuring QoS when conventional scheduling algorithms are employed. To overcome the limitation in QoS scheduling, we estimate delays taking into account buffer status reports (BSRs) of LTE and propose an uplink QoS scheduling scheme with delay estimation. For given system parameters, such as BSR period, an optimal parameter for estimation is also proposed and analyzed. Via simulations, we show that the proposed QoS scheduling scheme can reduce the number of unsatisfied QoS services by half, even with a simple estimation.

Joint Scheduling and Routing for QoS Guaranteed Packet Transmission in Energy Efficient Reconfigurable WDM Mesh Networks

The explosion of Internet traffic calls for quality of service (QoS)-guaranteed packet transmission in wavelength division multiplexing (WDM) networks with high energy and bandwidth efficiency. Conventional routing and wavelength assignment (RWA) algorithms focus on circuit switching, which does not well meet this requirement due to the bursty nature of IP traffic. Based on a novel traffic matrix decomposition technique, we study the joint design of traffic scheduling and routing in a reconfigurable WDM optical network to improve energy and bandwidth efficiency. Specifically, every node in the network is equipped with a set of parallel tunable lasers, each with a reconfiguration overhead. A dynamic matrix is adopted to model the traffic among the nodes and is decomposed into a set of transmission configurations (i.e., traffic scheduling). The configurations are then fulfilled by tuning the parallel lasers and routing the scheduled traffic under the topology constraint, to achieve loss-free packet transmissions with bounded delay (i.e., QoS guarantee). We reveal that a tradeoff exists between the packet delay and the required number of tunable lasers. The latter is then minimized under a given packet delay to save energy. As far as we know, this is the first work to adopt traffic matrix decomposition in WDM networks to save energy. The proposed framework is validated by extensive simulation studies.

REVAMPING QUALITY OF SERVICE OF VIDEO STREAMING OVER WIRELESS LAN

Just like video streaming over a wired network, streaming can also be done over a wireless LAN. However, real-time streaming over wireless networks is a challenging proposition due to the highly variable nature of wireless links and the resource-poor nature of mobile devices. In such a context, transmission control schemes have to dynamically adapt both to the application requirements and the channel conditions. In this paper, we put forward a short review of some recent innovations, which have been devised to thoroughly revamp the Quality of Service (QoS) of video streaming over WLANs. Depending on their applications, the solutions have focused mainly on the following three network layers: Media Access Control layer (MAC), Application layer (APP) and Physical layer (PHY). In this paper, we propose an adaptive cross-layer quality-of-service (QoS) scheme for wireless channel and streaming applications. For the sake of adaptive QoS, the cross-layer architecture assumes that layer information could be exchanged between application layer and lower layers. In addition, priority-based adaptive QoS scheduling for MPEG video streams is proposed here. It considers the frame type of the MPEG-4 video file so as to efficiently provide non-similar priorities to important packets of video. IEEE 802.11e protocol assigns top priority to video applications. It does this in order to reduce delay and packets losses, which could happen due to other competing traffic. Simulation results performed with the network simulator ns-2 will show that the cross-layer architecture allows a good performance under both, light and heavy loads, while minimizing the mean packet delay and frame jitter. We aim to minimize the dropping of frames and frame jitter while gracefully degrading video quality to enable the same.

Analytical model of quality of service scheduling for optical aggregation in data centers

Optical switching technologies represent a promising solution for data center interconnection networks to support the increasing bandwidth requirements of current cloud-based applications, while reducing interconnection complexity and energy consumption. Furthermore, the heterogeneity of intra- and inter-data center traffic characteristics requires some form of quality of service management. This paper describes modeling and design aspects of data center optical interconnections with particular emphasis on the aggregation level, where hybrid switching and packet scheduling are jointly applied to effectively implement service differentiation. Priority scheduling of three different service profiles is applied to maximize intra- and inter-data center traffic throughput, while guaranteeing time transparency for delay-sensitive services and zero loss/fixed delay for guaranteed connections. An analytical model is defined and validated to assess loss of real time and throughput of best effort traffic, in asynchronous packet context, when considering best effort traffic saturating the channels of the optical link. The model can also be used to dimension the optical output interface of the aggregation level switch.

QoS-Aware Scheduling of Services-Oriented Internet of Things

The Internet of Things (IoT) contains a large number of different devices and heterogeneous networks, which make it difficult to satisfy different quality of service (QoS) requirements and achieve rapid services composition and deployment. In addition, some services in service-oriented IoT are required to be reconfigurable and composable for QoS-aware services. This paper proposed a three-layer QoS scheduling model for service-oriented IoT. At application layer, the QoS schedule scheme explores optimal QoS-aware services composition by using the knowledge of each component service. At network layer, the model aims at dealing with scheduling of heterogeneous networks environment; at sensing layer, it deals with the information acquisition and resource allocation scheduling for different services. The proposed QoS-aware scheduling for service-oriented IoT architecture is able to optimize the scheduling performance of IoT network and minimize the resource costs.

An economic market for the brokering of time and budget guarantees

SummaryGrids offer best effort services to users. Service level agreements offer the opportunity to provide guarantees upon services offered, in such a way that it captures the users' requirements, while also considering concerns of the service providers. This is achieved via a process of converging requirements and service cost values from both sides towards an agreement. This paper presents the intelligent scheduling for quality of service market‐oriented mechanism for brokering guarantees upon completion time and cost for jobs submitted to a batch‐oriented compute service. Web Services agreement (negotiation) is used along with the planning of schedules in determining pricing, ensuring that jobs become prioritised depending on their budget constraints. An evaluation is performed to demonstrate how market mechanisms can be used to achieve this, whilst also showing the effects that scheduling algorithms can have upon the market in terms of rescheduling. The evaluation is completed with a comparison of the broker's capabilities in relation to the literature. Copyright © 2014 John Wiley & Sons, Ltd.

Study analysis of Cross QoS based Scheduler for 3.9G LTE Network

The proposed algorithm prolongs the scheduler from scheduling the Real Time (RT) and Non Real Time (NRT) services based upon their priority required on Quality of Service (QoS). The proposed Cross QoS Scheduling Algorithm (XQSA) is capable of scheduling data, video and voice services over the same wireless channel. The complimentary advantages of both RT (video & voice) and NRT (Best Effort of service, BE) services and cross layering technology is utilized by XQSA which prioritizing services in the scheduler to achieve a significant cell user throughput, a low delay, normalized fairness and a low packet loss ratio. The XQSA adapted the two different scheduling principles together to govern the effective channel utilization. The simulation of the XQSA under multiple cell scenarios for scheduling in the 3.9G LTE (Long Term Evolution) network is done using LTE-Sim tool. In the XQSA policy, the traditional RT and NRT scheduling algorithm are integrated together to support the multi-service scheduling (i.e., Modified Long Weighted Delay First/Proportional Fair (M_LWDF/PF) & Exponential Proportional Fair/Proportional Fair (EXP_PF/PF)). The study analysis of the XQSA is examined experimentally through simulation in terms of throughput, packet loss ratio, delay and cell spectral efficiency and fairness index. The simulation based comparison indicates that M_LWDF/PF and EXP_PF/PF scheduler outperforms PF scheduler especially during RT service. The PF scheduler has high throughput for the NRT service. In all the simulations, the performance level of EXP_PF/PF scheduling algorithm for multi-service scheduling seems to be an optimal solution for guaranteeing required QoS (Quality of Service). Index Terms—Channel Quality Indicator (CQI), Channel Utilization, Cross Layering, Cross QoS Scheduling Algorithm (XQSA), 3.9G, LTE- Sim, LTE, Multi-service scheduling, Quality of Service (QoS), Spectral Efficiency, User's Fairness. ——————————  ——————————

Courier: Multi-dimensional QoS guarantees for the consolidated storage system

The increasing cost and complexity of data management is driving data centers to consolidate resource and provide storage service for multiplex applications. Therefore, storage systems must be able to guarantee multi-dimensional Quality of Service (QoS) for various applications. However, satisfying performance targets for each workload is challenging, because that the I/O characteristics of workloads usually varies widely and capability of storage system changes significantly. In this paper, we design and implement a novel QoS scheduler, Courier, to maintain satisfactory performance for applications even in this highly-volatile scenario. Courier dynamically alternates between a feedback-based latency controller and reward budget-based scheduling to achieve per-application performance requirement. The feedback-based controller is employed to estimate request service times and adjust scheduling strategy dynamically. Based on the estimation, it can identify time-critical requests from throughput-sensitive requests and schedule applications with time-critical requests preferentially to avoid latency violations. In addition, Courier rewards well-behavior application with more budget to maintain high storage utilization while providing performance guarantees. We evaluate the effectiveness of our approach using synthetic and real workloads, and the results show that Courier has good ability to achieve per-application performance targets.

Network Coding Based Link Scheduling for QoS Provisioning in Multi-radio and Multi-channel Wireless Mesh Networks

This paper addresses Quality-of-Service (QoS) improvement in wireless mesh networks (WMNs) with multi-radio multi-channel by applying a novel network coding technique. It is known that network coding over wired networks enables connections at rates that cannot be achieved by traditional routings. However, the properties of wireless networks (e.g., omnidirectional transmissions, destructive interference, single transceiver per node, finite energy) modify the formulation of network coding and deviate from the classical network coding approach used in wired networks. In this paper, we investigate the problem of network-wide data transmission under the consideration of QoS requirements (namely packet delivery ratio, packet delay) over multi-radio and multi-channel WMNs. To solve this problem, we first introduce a network coding method, namely COPE, which is used to increase network-wide throughput for unicast wireless networks, and then present an Integer Linear Programming formulation of a given multi-radio and multi-channel WMN for addressing network coding traffic, routing, QoS requirements and scheduling optimizations. The proposed analytical formulation increases the network-wide throughput while satisfying the generalized QoS requirements by combining network coding strategy and interference free schedules. Our evaluations both in 16-node graph topology and 32-node random topology network show that a route selection strategy that is aware of network coding leads to higher in end-to-end throughput when compared to coding-oblivious routing strategies.

Hardware design and implementation of packet fair queuing algorithms for the quality of service support in the high-speed internet

The increasing amount of real-time traffic carried over the Internet requires end-to-end quality of service (QoS) support. To this end, the QoS Schedulers, that are implemented in routers, assign the available bandwidth resources to packet flows according to their respective allocated rates. Packet Fair Queuing (PFQ) schedulers can provide fair service and low end-to-end delay bound to the traffic flows. However, they have higher implementation complexity compared to other algorithms, because of the requirements of tracking the system state, and searching for the packet to get service among all flows, that are queued at the outgoing interface. QoS scheduling is a data plane functionality, which requires hardware implementation for high speed router interfaces. The previous works on hardware implementation of PFQ schedulers are specific to certain algorithms, and they do not provide any results on real hardware platforms. In this paper, we present a general hardware design framework for PFQ schedulers, and apply this framework to the WF2Q+ PFQ algorithm to demonstrate its properties. We carry out the entire implementation of the WF2Q+ algorithm on an FPGA, and evaluate its performance with real traffic flows. In addition, we implement WFQ as a second PFQ algorithm to demonstrate the generality of the framework.

Performance Analysis of Two-Level QoS Scheduler for Wireless Backhaul Networks

In this paper, we propose a two-level quality-of-service (QoS) scheduler based on the Ripple medium access control (MAC) protocol to deal with the unfairness problem for a chain-based wireless backhaul network (WBN). A queueing model was first presented to analyze the end-to-end throughput, aggregated throughput, and mean delay of a WBN accommodating single-class traffic. The model was then extended to a WBN with multiple-class traffic-adopting strict-priority (SP) service discipline or weighted-fair-queueing (WFQ) service discipline. Simulation results verified the accuracy of the analysis and the effectiveness of the proposed scheme.