Quality Of Service Metrics Research Articles

An On-Line Virtual Machine Consolidation Strategy for Dual Improvement in Performance and Energy Conservation of Server Clusters in Cloud Data Centers

As data centers are consuming massive amount of energy, improving the energy efficiency of cloud computing has emerged as a focus of research. However, it is challenging to reduce energy consumption while maintaining system performance without increasing the risk of Service Level Agreement violations. Most of the existing consolidation approaches for virtual machines (VMs) consider system performance and Quality of Service (QoS) metrics as constraints, which usually results in large scheduling overhead and impossibility to achieve effective improvement in energy efficiency without sacrificing some system performance and cloud service quality. In this article, we first define the metrics of peak power efficiency and optimal utilization for heterogeneous physical machines (PMs). Then we propose Peak Efficiency Aware Scheduling (PEAS), a novel strategy of VM placement and reallocation for achieving dual improvement in performance and energy conservation from the perspective of server clusters. PEAS allocates and reallocates VMs in an on-line manner and always attempts to maintain PMs working in their peak power efficiency via VM consolidation. Extensive experiments on Cloudsim show that PEAS outperforms several energy-aware consolidation algorithms with regard to energy consumption, system performance as well as multiple QoS metrics.

Towards techniques, challenges and efforts of software as a service layer based on business applications in cloud environments

Purpose Cloud computing is qualified to present proper limitless storage and computation resources to users as services throughout the internet. Software as a service (SaaS) layer is the key paradigm perspective in the software layer of the cloud computing. SaaS is connected by business applications to access consumers on existing public, private and hybrid cloud models. This purpose of this paper is to present a discussion and analysis on the SaaS layer based on business applications in the cloud environment in form of a classical taxonomy to recognize the existing techniques, challenges and efforts. Design/methodology/approach Existing techniques, challenges and efforts are classified into four categories: platform-dependent, application-dependent, data-dependent and security-dependent mechanisms. The SaaS layer mechanisms are compared with each other according to the important factors such as the structural properties, quality of service metrics, applied algorithms and measurement tools. Findings The benefits and weaknesses of each research study are analyzed. In the comparison results, the authors observed that the application-based method, the non-heuristic algorithms, the business process method have the highest percentage of the usage in this literature. Originality/value The SaaS layer mechanisms based on business applications have some main features such as high accessibility, compatibility, reusability and collaboration to provide activated application and operation services for user with help of Web browsers. A comprehensive analysis was presented as originality on the SaaS layer mechanisms based on business applications for high level of the cloud environment that 46 peer-reviewed studies were considered.

Notice of Violation of IEEE Publication Principles: Clustering Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions

Notice of Violation of IEEE Publication Principles Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions, by T. Rahman, I. Ullah, A. U. Rehman and R. A. Naqvi,in IEEE Access, vol. 8, January 2020, pp. 25135-25158 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This paper contains a figure (Figure 18) that was copied from the paper cited below. The figure was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. BICSF: Bio-Inspired Clustering Scheme for FANETs, by A. Khan, F. Aftab and Z. Zhang,in IEEE Access, vol. 7, March 2019, pp. 31446-31456 Mobile ad hoc networks (MANETs) have recently received a lot of attention from researchers due to their ease of deployment and versatility. Clustering is one of the most efficient ways to coordinate routing in MANETs to improve network performance using different attributes and metrics. In this paper, the authors have extensively studied clustering schemes and divided the schemes into multiple types based on the Cluster Head (CH) selection criteria, which provides a good understanding of how each type of clustering algorithm differs from each other. The authors analyzed the performance of existing schemes based on the quality of service (QoS) metrics. Based on findings, the authors clarified some important tradeoffs between QoS metrics and also established some important factors influencing the efficiency of clustering schemes. These studies also contain open research challenges and proposed solutions to improve the performance of clustering schemes.

Overview and Framework of Quality Service Metrics for Cloud-Based Robotics Platforms

With the rapid development of big data, cloud computing and other technologies, Cloud-based robotic has become one of the key research directions for service robot, such as used in hospitals. A framework and set of metrics for evaluating the quality of service (QOS) of a cloud robotic platform would be greatly facilitate research into and actual practice of service robots. In this paper, a QOS metrics framework of cloud robotic computing is summarized and the research of components and metrics of a cloud robotic platform is reviewed. QOS metrics are organized into software, network, and robotic services. By summarizing and analyzing the above three groups of metrics, a QOS framework or index system is proposed. Finally, future research towards open source and standardization of components of robotic cloud platform is discussed.

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

Web services have emerged as an accessible technology with the standard ’Extensible Mark Up’ (XML) language, which is known as ’Web Services Description Language’ WSDL. Web services have become a promising technology to promote the interrelationship between service providers and users. Web services users’ trust is measured by quality metrics. Web service quality metrics vary in many benchmark datasets used in the existing studies. The selection of a benchmark dataset is problematic to classify and retest web services. This paper proposes a method to rank web services quality metrics for the selection of benchmark web services datasets. To measure the diversity in quality metrics, factor analysis with Varimax rotation and scree plot is a well-established method. We use factor analysis to determine percentage variance among principal factors of four benchmark datasets. Our results showed that the two-factor solution explained 94.501, 76.524, and 45.009% variances in datasets A, B, and D, respectively. A three-factor solution explained 85.085% variance in dataset C. Reliability, and response time quality metrics were predicted as the most dominating quality metrics that contributed to explain the percentage variance in four datasets. Our proposed web metric ranking (WMR) method resulted in reliability as the top-most web metric with (57.62%) score and latency web metric at the bottom-most with (3.60%) score. The proposed WMR method showed a high (96.17%) ranking precision. Obtained results verified that factor solutions after reducing the dimensions could be generalized and used in the quality improvement of web services. In future works, the authors plan to focus on a dataset with dominating quality metrics to perform regression testing of web services.

Enhancing Modeling for Network Selection Using Graph Theory in Beyond 4G Networks

In recent years, the development and the deployment of different generation systems such as 3G (UMTS, Wi-Fi), 4G (LTE, WIMAX), and 5G have become a reality for different telecommunication operators. At the same time, the development of new mobile devices with multiple interfaces have shifted the behavior of users concerning the utilization of the internet. Additionally, several streaming servers are available to provide real time applications such as e-commerce transactions, video streaming, online gaming, etc. As a result, the users have the privilege to use different multimedia applications at anytime and anywhere. The major trend in Beyond 4G networks is determining the best access network for the end user in terms of quality of service (QoS) during the network selection decision. Thus, in this article, the authors investigate graph theory and the AHP approach to deal with the network selection issue. The experimental results show that the proposed policy can achieve a significant performance in terms of QoS metrics for real-time streaming than conventional algorithms.

An Adaptive on-Demand Multipath Routing Protocol With QoS Support for High-Speed MANET

The mobility and resource limitation of nodes are the critical factors that affect the performance of Mobile AD hoc network (MANET). The mobility of nodes will affect the stability of links, and the limitation of node resources will lead to congestion, so it is very difficult to design a routing protocol that supports quality of service (QoS) in MANET. Especially in the scenario of high-speed node movement, frequent link interruption will damages QoS performance, so it is necessary to design MANET routing protocol that can adapt to network topology changes to support QoS. In this paper, we propose a Topological change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) routing protocol, which can adapt to high-speed node movement to support QoS. In this protocol, a stable path selection algorithm is designed, which not only takes node resources (residual energy, available bandwidth and queue length) as the path selection parameters, but also considers the link stability probability between nodes. Furthermore, in order to adapt to the rapid change of topology, link interrupt prediction mechanism is integrated into the protocol, which updates the routing strategy based on periodic probabilistic estimates of link stability. Different scenarios with node speed in the range of 10-50m/s, data rate in the range of 4-40kbps and number of nodes in the range of 10-100 are simulated on NS2 platform. Our results show that the QoS metrics (packet delivery rate, end-to-end delay, and throughput) of the proposed protocol are significantly improved when the node speed is higher than 30m/s although it is slightly better when the node speed is lower than 30m/s. Our on-demand multipath routing protocol demonstrates high potential to support QoS for high-speed MANET.

Scheduling Scientific Workflow Using Multi-Objective Algorithm With Fuzzy Resource Utilization in Multi-Cloud Environment

The provision of resources and services for scientific workflow applications using a multi-cloud architecture and a pay-per-use rule has recently gained popularity within the cloud computing research domain. This is because workflow applications are computation intensive. Most of the existing studies on workflow scheduling in the cloud mainly focus on finding an ideal makespan or cost. Nevertheless, there are other important quality of service metrics that are of critical concern in workflow scheduling such as reliability and resource utilization. In this respect, this paper proposes a new multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS) for scheduling scientific workflow based on particle swarm optimization (PSO) method. The algorithm minimizes cost and makespan while considering reliability constraint. The coding scheme jointly considers task execution location and data transportation order. Simulation experiments reveal that FR-MOS outperforms the basic MOS over the PSO algorithm.

A Load Balancing Solution for Improving Video Quality in Loaded Wireless Network Conditions

There is an increasing user demand for high-quality content-rich multimedia services. Despite their advantages, current wireless networks in general and wireless mesh networks in particular have limitations in terms of quality of service (QoS) provisioning, especially when dealing with increased amounts of time sensitive traffic such as video. This paper presents video load balancing solution (ViLBaS), a load balancing-based mechanism which enhances delivery performance of video services over multi-hop wireless mesh networks and improves user quality of experience (QoE) levels. ViLBaS involves performance monitoring at the level of the nodes and load balancing by off-loading traffic from loaded nodes to less loaded neighboring nodes. Simulation-based results show how the proposed ViLBaS improves video delivery performance in terms of both QoS and QoE metrics [delay, throughput, packet loss, and peak signal-to-noise-ratio (PSNR)]. The comparison is performed against three other traditional approaches in different network topologies, for diverse video flow distributions, and different sizes for the video queue.

Performance of low level protocols in high traffic wireless body sensor networks

The rapid development of medical sensors has increased the interest in Wireless Body Area Network (WBAN) applications where physiological data from the human body and its environment is gathered, monitored, and analyzed to take the proper measures. In WBANs, it is essential to design MAC protocols that ensure adequate performance and Quality of Service (QoS). This paper investigates Medium Access Control (MAC) protocols used in WBAN, and compares their performance in a high traffic environment with respect to different QoS and network performance metrics. Such scenario can be induced in case of emergency for example, where physiological data collected from all sensors on human body should be sent simultaneously to take appropriate action. This study can also be extended to cover collaborative WBAN systems where information from different bodies is sent simultaneously leading to high traffic. OPNET simulations are performed to compare the performance of the different MAC protocols under the same experimental conditions. A new MAC scheme aiming to enhance the delay, throughput, and energy performance of the system is then proposed and compared to existing protocols, to evaluate its performance in high traffic environment.

A new approach of social media analytics to predict service quality: evidence from the airline industry

PurposeResearch on how to use social media data to measure and evaluate service quality is still limited. To fill the research gap in the literature, the purpose of this paper is to open a new avenue for future work to measure the service quality in the service industry by developing a new analytical approach of using social media analytics to evaluate service quality.Design/methodology/approachThis paper uses social media data to measure the service quality of the airline industry with the SERVQUAL metrics. A novel benchmark data set was created for each SERVQUAL metric. The data set was analyzed through text mining and sentiment analysis.FindingsBy comparing the results from social media with official service quality report from the Department of Transportation, the authors found that the proposed service quality metrics from social media are valid and can be used to estimate the service quality.Practical implicationsThis paper presents service quality metrics and a methodology that can be easily adopted by other businesses to assess service quality. This study also provides guidance and suggestions to help businesses understand how to collect and analyze social media data for the purpose of evaluating service quality.Originality/valueThis paper offers a novel methodology that uses text mining and sentiment analysis to help the airline industry assess its service quality.

Adaptive Performance Evaluation for SDN Based on the Statistical and Evolutionary Algorithms

Being able to send different types of data (i.e. text, audio, or video) through the network is the most important aspect of networks. Different networks have different issues and restrictions while sending data. These restrictions are basically the QoS (Quality of Service) metrics and security. The recent Software-Defined Networking (SDN) that aims to separate the control plane from the data plane can be applied where Business requirements are not responsible for the way the network is configured; instead, it is the responsibility of the high-level business policies and objectives. SDN gives preferable techniques for centralized dynamic management and control configurations. In this work, a proposed model has been estimated and discussed to promote QoS requirements in some suggested topologies. Adaptive Resource Management (ARM) and control to send different types of data through different hosts have been investigated. The intended requirements are basically the capacity and delay of traffic metrics sent through different hosts through the network. It produces a mathematical model and implementation for three proposed algorithms to enhance the quality of a sample video sent from source host to destination host by Visible Light Communication (VLC)-media player in three different topologies. These algorithms (statistical, MOGA, and PSO) have been implemented using Mininet emulator, FNSS tool, PULP, and network libraries; with two types of controllers which are Floodlight and OVS under Linux operating system and in python programming language.

Design o f a Fault Tolerant Strategy f or Resource Scheduling i n Cloud Environment

Cloud computing supports the technological need of the industry supporting many other technologies. Also, the demand for computing power and storage by recent technologies is reasonably growing in a drastic way. Cloud computing, serving for these technologies are to be developed with advancements that lead to performance improvement both in support to the technologies like block-chain and big data. The allocation of cloud resources is an important strategy to be followed in a wiser manner to incorporate the needs of extra ordinary computing power. In this paper, an efficient resource allocation strategy (FTVMA) is introduced that involves the creation of effective virtual machines (VMs) and performs VM allocation in an efficient manner by considering the failure rates, previous history of failure of VM, execution efficiency as a part of effective scheduling. There exist many reasons for cloudlet failure in VMs. Some of them are overloading of VMs and non-availability of VMs. The introduced FTVMA algorithm considers the failure rate of the physical machine, load of virtual machines and the cost priority of the tasks in order to achieve Quality of Service (QoS) and Quality of Experience (QoE) of the user. The FTVMA methodology proposed in this paper works better for computation intensive VMs and is tested using CloudSim environment. The QoS metrics used to measure the performance of the proposed algorithm are Makespan and VM Utilization. The metric to measure QoE are Priority Miss Rate and Failure Rate. The proposed algorithm shows its improvement in terms of the QoS and QoE metrics. The results obtained are compared with the existing resource scheduling algorithms and it is inferred that the proposed algorithm performs better in terms of QoS and QoE.

A hybrid approach for the optimization of quality of service metrics of WSN

The core objective behind this research paper is to implement a hybrid optimization technique along with proactive routing algorithm to enhance the network lifetime of wireless sensor networks (WSN). The combination of two soft computing techniques viz. genetic algorithm (GA) and bacteria foraging optimization (BFO) techniques are applied individually on destination sequence distance vector (DSDV) routing protocol and after that the hybridization of GA and BFO is applied on the same routing protocol. The various simulation parameters used in the research are: throughput, end to end delay, congestion, packet delivery ratio, bit error rate and routing overhead. The bits are processed at a data rate of 512 bytes/s. The packet size for data transmission is 100 bytes. The data transmission time taken by the packets is 200 s i.e. the simulation time for each simulation scenario. Network is composed of 60 nodes. Simulation results clearly demonstrates that the hybrid approach along with DSDV outperforms over ordinary DSDV routing protocol and it is best suitable under smaller size of WSN.

QoS Aware Trust Based Routing Algorithm for Wireless Sensor Networks

In Wireless Sensor Network (WSN), the lifetime optimization based on minimal energy consumption and security are the crucial issues for the effective design of protocols to perform multi-hop secure routing. In order to address these issues, we propose a new routing protocol called Secured Quality of Service (QoS) aware Energy Efficient Routing Protocol in this paper which is designed based on trust and energy modelling for enhancing the security of WSN and also to optimize the energy utilization. In this proposed work, the trust modelling uses an authentication technique with a key based security mechanism for providing trust scores. Moreover, three types of trust scores namely direct, indirect and overall trust scores are calculated in this work for enhancing the security of communication. In addition, a cluster based secure routing algorithm is proposed in this work in which the cluster head has been selected based on QoS metrics and trust scores to perform cluster based secure routing. Finally, the final path has been selected based on path-trust, energy and hop count to efficiently carry out the secure routing process. The proposed work has been assessed by simulations carried out using NS2 simulator. The simulation results demonstrate that the proposed algorithm provides better performance in terms of increase in packet delivery ratio, network life time and security. Moreover, it provides reduction in delay and energy consumption when the proposed secure routing algorithm is compared to the other related secure routing algorithms.

Target coverage computation protocols in wireless sensor networks: a comprehensive review

Wireless sensor network comprises tiny sensor nodes having the ability of sensing to surveil an area of interest. The amount of area sensed by a sensor node is known as coverage. Coverage is a multi-dimensional quality of service (QoS) metric in terms of diversity in its computation algorithms according to the real-time applications. Different applications require a distinctive level of coverage. But every coverage computation algorithm has one common objective, i.e. to maximize the network’s lifespan and coverage quality with minimum number of sensor nodes. Several algorithms have derived optimal or close-to-optimal solutions based on similar or distinct parameters. This paper presents a concise review of various target coverage protocols for both static and mobile targets. Total target coverage provided by the deployed sensor nodes is a measure of sensing quality. Target coverage is bifurcated as static coverage and dynamic coverage. This paper highlights and describes various application specific target coverage protocols and their QoS metrics and parameters. Each protocol has solved multi-dimensional coverage issues and challenges and proposed optimum solutions. The comprehensive review of the target coverage protocol helps in understanding about various research challenges and its existed solutions in target coverage, detection and tracking in a better way.

An Application-Aware Clustering Protocol for Wireless Sensor Networks to Provide QoS Management

Wireless sensor networks (WSNs) have long been established as a suitable technology for gathering and processing information from the environment. However, recent applications and new multimedia sensors have increased the demand for a more adequate management of their quality of service (QoS). The constraints and demands for this QoS management greatly depend on each individual network’s purpose or application. Low-Energy Adaptive Clustering Hierarchy (LEACH) is arguably the most well-known routing protocol for WSNs, but it is not QoS-aware. In this paper, we propose LEACH-APP, a new clustering protocol based on LEACH that takes the network’s application into account and is aimed at providing a better overall QoS management. We thoroughly describe our proposal and provide a case study to explain its operation. Then, we evaluate its performance in terms of two significant QoS metrics—throughput and latency—and compare it to that of the original protocol. Our experiments show that LEACH-APP increases the throughput by roughly 250% and reduces the latency by almost 80%, overall providing a more flexible and powerful QoS management.

Smart‐city medium access for smart mobility applications in Internet of Things

AbstractIn this paper, we target the wireless medium access problem under rapid mobility conditions in smart cities. We take advantage of cloud computing and put forward an agile vehicular cloud and mobile Internet of Things communication framework. The framework is employing the standardized Long Term Evolution (LTE) while improving the quality of service (QoS) in modern mobile applications. It minimizes the communication delay and error rates in smart real‐time transportation applications while maintaining the highest network throughput via enhanced medium access protocol. The objective of this study is to improve the connection between the vehicles and the roadside units, which are equipped with real‐time devices. The proposed approach integrates Markovian process with IEEE 802.16 to analyze various QoS metrics, namely throughput, error rate, and the average packet delay.

Differential evolution CapsNet model for QoS routing enhancement in wireless networks

SummaryOne of the nondeterministic polynomial (NP) hard problem in the area of wireless networks is the quality of service (QoS) routing problem. This has always been a key area wherein research is required to enhance the QoS metrics considered with respect to the considered network model. In this paper, mobile ad hoc network (MANET) model is identified for which an effective QoS routing protocol which meets the set QoS constraints is designed employing the novel variant of differential evolution (DE) model. The ultimate aim of the proposed work is to identify an optimal feasible path for the MANET model with the set objective function criterion being met. A variant of DE capsule net model is built by hybridizing it with the nature‐inspired firefly (FF) algorithmic approach. The effectiveness of the proposed technique is achieved by minimizing the set objective function on satisfying the QoS constraints in the MANETs. The developed technique chose better QoS path with respect to the best fitness value than that of the route replay the shortest path algorithm of ad hoc on‐demand distance vector (AODV) protocol. The modeled variant DE‐FF model achieves better QoS metrics with all the set constrained being met. Simulated results depict the superiority and better performance of the developed DE‐FF model than that of the other methods considered for comparison from the existing literature works.

A Comparative Analysis of Energy-Efficient and Improved QoS-Driven Task and Resource Scheduling in Mobile Cloud Computing Environment

Mobile Cloud Computing (MCC) is a combination of cloud computing in to a mobile environment. It is refers to an infrastructure where data storage and data processing happen outside of the mobile device. MCC is an computing platform located in clouds, which is accessed over the wireless connection. MCC can significantly enhance the computation capability and saves energy of the smart mobile devices. Some built in defects of mobile devices, such as limited battery energy, insufficient storage; the mobile applications faces many challenges in mobility management, Quality of Service (QoS), energy management and security issues. A task is an application which is running in a mobile device and those tasks will be executed by Virtual Machines (VM) which is known as Resources. The pool of VM in a cloud computing data center needs to manage an efficient task and resource scheduling to maintain efficient energy, QoS and resource utilization. This work investigates comparative analysis of energy efficient and improved QoS-driven Task and Resource scheduling in a MCC environment by using Differential Evolution (DF). The evaluation of these algorithms is based on energy and QoS metrics. Based on the analysis of the simulation result, one of the scheduling will be concluded as best scheduling process in terms of energy and QoS.