Adaptive Quality Of Service Research Articles

Adaptive quality of service for packet loss reduction using OpenFlow meters.

Quality of Service (QoS) is a mechanism used in computer networks to prioritize, classify, and treat packets differently based on certain criteria. This helps the switching devices to schedule and reorder packets if there is congestion in the network. Edge routers experience high traffic congestion as a result of traffic aggregation from the internal network devices. A router can have multiple QoS classes configured, and each class could experience traffic at various rates. However, when a QoS class is underperforming or needs more bandwidth, some bandwidth can be borrowed or leased out to another QoS class to ensure the link is utilized to maximum capacity and the highest throughput is achieved. This article proposes a bandwidth allocation and distribution algorithm that purely uses the flow statistics from the OpenFlow switches to allocate bandwidth to different QoS classes optimally based on their current requirement. The algorithm does not guarantee in advance that the packet loss will be minimized but does guarantee the initial minimum bandwidth allocation. It adjusts the flows' rates with the aim to increase their current throughput. The algorithm uses the Software Defined Networking (SDN) controller's flow monitoring component to query the flow statistics from the switch to first approximate the traffic flow rate and then calculate the optimal bandwidth values to assign to each QoS class. The proposed algorithms will be applied to certain switches in the path with the assumption that all the switches are OpenFlow compatible. The algorithm's performance was compared with the Adaptive Quality of Service (AQoS) algorithm over various traffic scenarios. The results show that the proposed algorithm achieves an average of 9% performance gain compared to the AQoS algorithm.

An Adaptive QoS and Trust-Based Lightweight Secure Routing Algorithm for WSNs

The limited resources and low computational power of wireless sensor networks (WSNs) make them vulnerable to various security attacks. Conventional security mechanisms require too many resources to allow the reliable operation of WSNs due to their resource-constrained nature. In addition, multihop communication in WSNs creates a requirement for guaranteed Quality of Service (QoS). Therefore, providing security while maintaining QoS and energy efficiency in WSNs are important design considerations. To further increase the performance of WSNs, there is a need to overcome the energy-hole problem, which leads to poor coverage of the field of interest. An energy-hole problem is created because of using poor deployment strategies. In this article, we define a multiobjective WSN optimization problem and present a novel algorithm known as lightweight secure routing (LSR) to manage WSNs that directly addresses the multiobjective WSN optimization problem. Our LSR algorithm uses ant colony optimization (ACO), an adaptive security model based on direct and indirect trust calculations, an adaptive QoS model, a hybrid deployment model based on 2-D Gaussian and uniform distributions, and an adaptive connectivity model that uses an appropriate communicational radius to ensure high connectivity between sensor nodes to solve the multiobjective WSN optimization problem. We divide our simulation results into three analyses, namely, trust model analysis, network scalability analysis, and security risk analysis to show that LSR outperforms the existing techniques in terms of energy consumed to calculate trust values, trust values convergence, network lifetime, average routing delay, and packet delivery ratio.

Toward a Data-Driven Cognitive Framework for Adaptive QoS in IoT

Quality of service (QoS) is an increasingly important, stringent, and challenging requirement in the Internet of Things (IoT). Domain and context-dependent circumstances bring forth the need for a cognitive design to facilitate QoS that can learn and adapt as the requirements change and deployments evolve. The performance data that measure QoS metrics based on variations in different communication stack parameters/protocols is becoming available. The research literature highlights the impact of various configurable parameters, such as transmission power (TP) and packet size (PS) on metrics, such as reliability and delay. In this article, we propose the design of a cognitive framework for adaptive QoS in the IoT. The proposed approach is based on performance data from deployments of different types of communication networks forming IoT. The aim is to be able to adaptively configure communication parameters/protocols ensuring that the QoS constraints in evolving network scenarios are met.

A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique over WLAN SDN Architecture

Recently, Software-Defined Networking (SDN), a network architecture approach that enables the network to be intelligently and centrally controlled by using software applications, has been introduced. Another important issue in the network management context is Quality of Service (QoS). This work investigates the QoS provisioning of various traffic classes on an SDN-enabled network. We propose and implement the class-based adaptive QoS control scheme on SDN-enabled network for various traffic classes, namely VoIP, Video Streaming and File Transfer. The effectiveness of our proposed scheme is validated by simulation using Mininet and Ryu controller. The procedure to create the simulation platform and all details relevant to all software used are described step-by-step in detail. The main performance evaluation metric is the Maximum Number of Traffic Flows admittable with QoS while Average Throughput, Latency, Jitter, and Packet Loss Rate are maintained at the comparable level of the existing work in the literature called JMABC [11]. Our simulation results are illustrated with 95% confidence interval. According to the simulation results, it is obvious that our proposed class-based adaptive QoS control scheme adopting the optimization technique significantly outperforms the existing similar QoS provision scheme in terms of the maximum number of the high priority traffic flows (VoIP) admittable with QoS while the other evaluation metrics are maintained at the same level.

Adaptive Group-based QoS Aware Link State Proactive Multipath Routing for Large-Scale MANETs (GMP-OLSR)

Objectives: To propose a group-based QoS aware proactive multipath routing based on OLSR protocol. Methodology: This work proposes a group-based proactive multipath routing based on the OLSR protocol, which is designed to be adaptive for a big crowded Mobile Ad-hoc Network to satisfy the desire for real-time multimedia applications. This approach will restrict mobile nodes to be participated in the cluster head selection process based on the mobility and energy of a node. It uses a grouping strategy to manage the network topology and performing route maintenance, and preserve multiple prioritized routes with the support of Quality of Service (QoS) aware computation. Findings: The study evaluated the performance of the proposed group-based QoS aware multipath Optimal Link State Routing (GMP-OLSR) routing through the OMNET++ network simulation environment. Within different network density, the proposed approach offered a significant reduction in end to end delay, overhead and an increment in data packet delivery ratio Applications: Using an appropriate groupbased QoS aware multipath routing strategy for mobile ad hoc networks provides better performance and improves the services of mobile applications. Keywords: MANET, GMP-OLSR, Clustering, Multipath Routing, QoS Aware Routing.

A QoS Model for Real-Time Application in Wireless Network Using Software Defined Network

Network convergence has vital importance due to limited network resources. Though it decreases network cost, it also reduces Quality of Service (QoS) as well. Due to the increase in real-time network applications, such as video conferencing and voice over IP calls, there is a need to achieve fairness in user's demand through QoS. QoS measurement tasks are more challenging and increased network losses in infrastructure less network. QoS is not yet implemented in Wireless Local Area Network (WLAN) using Software Defined Network (SDN). We have proposed a model to support QoS in WLAN using SDN architecture for real-time traffic. It is efficiently utilizing bandwidth and reducing flow starvation due to centralize control. We named it, an Adaptive QoS model. This model allocates queues dynamically to flows using real-time traffic analysis in SDN architecture. A specific queue is allocated to a specific type of traffic. Flows are dynamically switched based on the increase in traffic demand if other queues are under-use. We achieved handsome improvement in Adaptive QoS model rather than a standard QoS model in terms of throughput and losses.

Adaptive QoS constraint-based service differentiated routing in wireless sensor network

Achieving the best quality of service (QoS) as per user requirement is one of the important challenges. The time-critical applications in wireless sensor networks (WSNs) demand energy efficient transmission of data with limited resource availability. To resolve these issues, AQSDR has been proposed. The proposed protocol support packet differentiation and selection of sensor node based on energy, delay, and congestion for path establishment to transfer normal data packets providing energy conservation. The multipath is chosen for transmission of emergency packets satisfying delay requirements of non-delay tolerant applications. AQSDR support adaptive path selection, according to application requirement. The proposed technique in this work outperforms when compared with the existing protocol in terms of minimised energy consumption, delay, control overhead, packet drop ratio, and high throughput.

Context-Aware and Adaptive QoS Prediction for Mobile Edge Computing Services

Mobile edge computing (MEC) allows the use of its services with low latency, location awareness and mobility support to make up for the disadvantages of cloud computing, and has gained a considerable momentum recently. However, the dynamically changing quality of service (QoS) may result in failures of QoS-aware recommendation and composition of MEC services, which significantly degrades users’ satisfaction and negates the advantages of MEC. To address this issue, by considering user-related and service-related contextual factors and various MEC services scheduling scenarios, we propose two context-aware QoS prediction schemes for MEC services. The first scheme is designed for the situations when MEC services are scheduled in real-time, which contains two context-aware real-time QoS estimation methods. One method can estimate the real-time multi-QoS of MEC services and the other method can estimate the real-time fitted QoS of MEC services. The second scheme is designed for the situations when MEC services are scheduled in the future. This scheme includes two context-aware QoS prediction methods. One method can predict the multi-QoS of MEC services and the other method can predict the fitted QoS of MEC services. Finally, adaptive QoS prediction strategies are developed in the light of characteristics of the proposed QoS prediction methods. According to these strategies, the most appropriate QoS prediction method can be scheduled. Extensive experiments are conducted to validate our proposed approaches and to demonstrate their performance.

Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition

As there are more and more available Web services with the same or similar functionalities but different Quality of Service (QoS), the challenge of QoS-aware service composition is to efficiently select appropriate component services to achieve maximum utility and meet the global QoS constraints with low time cost. In this paper, we propose a dynamic service selection approach based on adaptive global QoS constraints decomposition. Fuzzy logic technology and Cultural Genetic Algorithm are used to adaptively decompose global QoS constraints into near-optimal local constraints. According to the near-optimal local constraints, the optimal service is selected for each service class during the running time efficiently. Experimental results show that the proposed approach not only achieves the near-optimal solution, but also significantly reduces the computation time, and has good adaptability and scalability.

RETRACTED ARTICLE: An adaptive QoS computation for medical data processing in intelligent healthcare applications

Efficient computation of quality of service (QoS) during medical data processing through intelligent measurement methods is one of the mandatory requirements of the medial healthcare world. However, emergency medical services often involve transmission of critical data, thus having stringent requirements for network quality of service (QoS). This paper contributes in three distinct ways. First, it proposes the novel adaptive QoS computation algorithm (AQCA) for fair and efficient monitoring of the performance indicators, i.e., transmission power, duty cycle and route selection during medical data processing in healthcare applications. Second, framework of QoS computation in medical applications is proposed at physical, medium access control (MAC) and network layers. Third, QoS computation mechanism with proposed AQCA and quality of experience (QoE) is developed. Besides, proper examination of QoS computation for medical healthcare application is evaluated with 4–10 inches large-screen user terminal (UT) devices (for example, LCD panel size, resolution, etc.). These devices are based on high visualization, battery lifetime and power optimization for ECG service in emergency condition. These UT devices are used to achieve highest level of satisfaction in terms, i.e., less power drain, extended battery lifetime and optimal route selection. QoS parameters with estimation of QoE perception identify the degree of influence of each QoS parameters on the medical data processing is analyzed. The experimental results indicate that QoS is computed at physical, MAC and network layers with transmission power (− 15 dBm), delay (100 ms), jitter (40 ms), throughput (200 Bytes), duty cycle (10%) and route selection (optimal). Thus it can be said that proposed AQCA is the potential candidate for QoS computation than Baseline for medical healthcare applications.

Smart routing for Vehicle at optimal position with Ant Colony Optimization and AQRV in VANET

The Coimbatore is one of the leading tier two cities in India. Vehicles in this city are increasing day by day. The VANET is a self-organizing network between vehicles. There are so many interesting issues in VANET. So many routing protocols are already availing in the market. In this paper, we put forward an adaptive quality of service (QoS) based smart routing for VANETs with ant colony optimization algorithm find a smart route in Coimbatore city. This smart routing protocol is to adaptively choose the connections in the various available nodes, through which data packets clearance to reach the destination. The selected smart route should fulfill with the QoS constraints and satisfy the best QoS in terms of three metrics, namely connectivity, probability, packet delivery ratio and delay. The AQRV and Ant colony algorithm was simulated. The comparative result was studied at various optimal positions in the city.

IAODV: AN IMPROVED AODV ROUTING PROTOCOL FOR MANET

Mobile Ad hoc Network is a network of independent nodes, which are mobile, connected through the wireless medium and highly dynamic by nature, and exemplified by the absence of physical infrastructure. The diversity and variations in wireless mobile devices are generating the revolutionary changes in the way users are using the services and resources for their purposes, at the same time it also poses many challenges to the researchers. In this paper, authors contribute toward the design of a new adaptive Quality of Service (QoS) paradigm for MANET. An honest attempt is made to develop an approach to decrease the overall delay, i.e. End-to-End delay and increase the efficiency of Protocol and also tried to make it more reliable by increasing the Packet Delivery Ratio (PDR) by applying Dijkstra Algorithm (for finding shortest and optimal route) and reducing queuing delay for increasing throughput by using a new hybrid algorithm. The modified AODV, which is named as Improved AODV (IAODV), enhance the “Packet Delivery Ratio” and reduce the “Queuing Delay”.

Fuzzy QoS requirement-aware dynamic service discovery and adaptation

The integration of coherent services plays a potential role in the field of Service Oriented Applications. Achieving this potential standard crucially depends on the ability to recognize and exploit the available services based on user requirements. In general, the user preferences on Quality of Service (QoS) requirements are fuzzy in nature. In addition to that, the QoS requirements are user dependent even if the functional requirements are the same. With a large number of available services, service selection for dynamic composition at run time is a challenge. Functional and non-functional assumptions made at design time may violate at run-time. These violations require run time reaction, by adopting a run-time process. Therefore, dynamic and fuzzy QoS-aware service discovery for run-time composition and continuous adaptation is a strong requirement in service oriented computing. Considering that different users follow different fuzzy reasoning in various contexts at different times, a fuzzy inference based service selection approach has been proposed in this paper. Continuous adaptation is done, as and when a design time assumption violation is reported by a run-time monitoring system. We have implemented and tested the proposed approach and the results show its effectiveness.

Fuzzy based QoS prediction using bayesian network in cloud computing environment

The Quality of Service (QoS) is enforced in discovering an optimal web service (WS).The QoS is uncertain due to the fluctuating performance of WS in the dynamic cloud environment. We propose a Fuzzy based Bayesian Network (FBN) system for Efficient QoS prediction. The novel method comprises three processes namely Semantic QoS Annotation, QoS Prediction, and Adaptive QoS using cloud infrastructure. The FBN employs the compliance factor to measure the performance of QoS attributes and fuzzy inference rules to infer the service capability. The inference rules are defined according to the user preference which assists to achieve the user satisfaction. The FBN returns the optimal WSs from a set of functionally equivalent WS. The unpredictable and extreme access of the selected WS is handled using cloud infrastructure. The results show that the FBN approach achieves nearly 95% of QoS prediction accuracy when providing an adequate number of past QoS data, and improves the prediction probability by 2.6% more than that of the existing approach.

An Erlang Factor Integrated Channel Allocation Method for Boosting Quality of Services in mobile ad hoc networks

The choice of using the channel efficiently in mobile Ad hoc Networks is improved by creating multiple channel accessibility for attaining improved system functionality that depends on the metrics such as point to point delay, throughput and overhead. Most probably delivery of QoS (quality of service) hugely complicated in multiple channel ad hoc networks and the available adaptation and signaling methods for QoS, exist in literature survey primarily helps static-width channels or dynamic-width channels that limit the performance of a network. This Erlang Factor Integrated Channel Allocation Mechanism (EFICAM) model analyzes to improve ad hoc network's system functionality. EFICAM utilizes a combined scheme of availability and performance of the existing channel conditions through Erlang Factor that computes the rejection rate of the network. The decision on user request rejection rate depends upon the transmission delay and queuing delay by two thresholds like Channel Allocation Verge (CAV) and Estimated Channel Allocation Verge (ECAV). The QoS adjustments are achieved by EFICAM by calculating the delay in queue which helps in splitting the packet into smaller fragments for improving the level of QoS. This distributed approach refines the QoS by altering the channel sizes in a multiple channel ad-hoc networks that is constructed upon the ETT (Expected Transmission Time) routing parameter to meet the need of adaptability of bandwidth. The outcome investigation of EFICAM is done in an NS-2 simulator and the expected mechanism provides an enhancement of 17% and 21% with respect to throughput and packet delivery ratio respectively than the current QoS adaptation mechanisms exists in the literature.

Wireless Sensor Network Softwarization: Towards WSN Adaptive QoS

Connecting wireless sensor networks (WSNs) to remote servers or clouds through the Internet of Things inspires the formation of a highly complex system. This has significant potential to encourage WSN softwarization. Such a network enables interaction between the physical and virtual sensor network, allowing for flexibility within the physical system to adapt with the dynamics of the service requirements. An architecture of a test environment with adaptive quality of service (AQoS) has been presented. The concept provides an avenue for a flexible system that is capable of reacting to dynamic changes of process demands. Physical network performance can be predicted by analyzing the historical data in the background on a network simulator or virtual network. This in turn allows for estimation of the necessary adjustments needed to improve the network performance without disturbing the physical system. This paper reports the experimentation and applicability of user-driven AQoS models on the system. Early stages of testing the organization, taking into account the system behavior and subsequent reaction to necessary adjustment of the WSN operational configuration, has shown encouraging results.

A Model-Driven Approach to Enable Adaptive QoS in DDS-Based Middleware

Critical and distributed systems need to be reliable and comply with the required performance at run-time. In this vein, data distribution service for real-time systems (DDS) provides developers with highly configurable middleware to control the end-to-end quality of service (QoS) of the applications through a wide range of attributes. However, dynamic and unpredictable environment pose a major challenge to these systems as their workload and resources may fluctuate significantly in time depending on the execution context. Developers usually find it difficult to choose and apply the right DDS QoS attributes, as once selected, they remain fixed during the whole execution of the system. They do not automatically change according to the execution context, e.g., to meet nonfunctional requirements related to performance or resource consumption. Moreover, changing the QoS attributes at run-time may lead to incompatibilities, since the configuration used by the different participants needs to be mutually consistent. In this paper, we propose a model-driven approach that enables the safe, automatic, and transparent adaptation of the QoS attributes in DDS-based middleware, providing the best performance possible within the available resources at run-time. An example in robotics is presented to demonstrate the feasibility and the benefits of our proposal.

Hyper-Erlang channel allocation factor-based QoS enhancement mechanism for mobile ad hoc networks

The availability of multiple channels in Mobile Ad hoc NETworks (MANET) enhances the options of utilizing channels efficiently for achieving improved system performance in terms of throughput, end-to-end and communication overhead that impacts spectral efficiency. However, ensuring quality of service (QoS) in multichannel ad hoc networks is crucial and most of the existing signaling and adaptation mechanisms for QoS available in the literature mainly supports fixed-width channels or variable-width channels that restrict network performance. In this paper, we propose a Hyper-Erlang Channel Allocation Factor-based QoS Enhancement Mechanism (HECAFQEM) for improving the support capability of ad hoc networks. HECAFQEM uses a composite model that models the availability and performance of the prevailing channel conditions through Hyper-Erlang Factor that quantifies the rate of rejection of the network. The degree of rejection of user request depends on queuing delay, transmission delay and two significant parameter called Channel Allocation factor (CAF) and Expected Channel Allocation Factor (ECAF). The QoS adaptations are performed by HECAFQEM by measuring the queuing delay that helps in segmenting the packet stream into small bursts for enhancing the degree of QoS. This distributed mechanism enhances QoS through the estimation of Channel Allocation Factor (CAF) that dynamically adapts channel widths in a multichannel ad-hoc network to suit bandwidth adaptability. The performance analysis of HECAFQEM is carried out based on ns-2 simulator and the proposed approach shows an improvement on an average by 23% and 19% in terms of packet delivery ratio and throughput than the existing QoS adaptation mechanisms available in the literature.

A Policy Based Management of a Smart Adaptive QoS for the Dynamic and Multipoint Virtual Private Network

The IP Virtual Private Network technology is increasingly being used due to its efficiency in terms of cost, its availability and its high security level, companies deploy multiple secure tunnels and send through them different traffics, in order to guaranty a reliable level of communication and the availability of resources, many qualities of service’s policies must be defined. According to our research, most of works treat only the problematic of the Quality of Service management in a site to site Virtual Private Network, this was an incentive for us to: design and implement a new model of a smart adaptive quality of service management for the dynamic and multipoint Virtual Private Network, and create a new friendly-user web application to facilitate the quality of service management for such complex networks. This paper discusses a new model for the policy-based management of a smart adaptive quality of service for the Dynamic and Multipoint Virtual Private Network using a new WEB interface.

Runtime Adaptive Circuit Switching and Flow Priority in NoC-Based MPSoCs

With the significant increase in the number of processing elements in NoC-based MPSoCs, communication becomes, increasingly, a critical resource for performance gains and quality-of-service (QoS) guarantees. The main gap observed in the NoC-based MPSoCs literature is the runtime adaptive techniques to meet QoS. In the absence of such techniques, the system user must statically define, for example, the scheduling policy, communication priorities, and the communication switching mode of applications. The goal of this paper is to investigate the runtime adaptation of the NoC resources, according to the QoS requirements of each application running in the MPSoC. This paper adopts an NoC architecture with duplicated physical channels, adaptive routing, support to flow priorities and simultaneous packet and circuit switching. The monitoring and adaptation management is performed at the operating system level, ensuring QoS to the monitored applications. The QoS acts in the flow priority and the switching mode. Monitoring and QoS adaptation were implemented in software, resulting in flexibility to apply the techniques to other platforms or include other adaptive techniques, as task migration or DVFS. Applications with latency and throughput deadlines run concurrently with best-effort applications. Results with synthetic and real application reduced in average 60% the latency violations, ensuring smaller jitter and throughput. The execution time of applications is not penalized applying the proposed QoS adaptation methods.