Multiple Quality Of Service Parameters Research Articles

A new QoS-aware service composition technique in cloud computing using capuchin search algorithm

Cloud platforms rapidly develop and supply many cloud-based services with various functions but varying Quality of Service (QoS) attributes. In the context of cloud computing, a major concern is how to incorporate disparate cloud services to maximize QoS value, which is underpinned by meeting the local QoS requirements of users. To deal with multiple QoS parameters in service composition, we propose a new mechanism using the Capuchin Search Algorithm (CapSA) that provides solutions to satisfy connectivity constraints. The CapSA algorithm mimics the dynamic behavior of capuchin monkeys in the forest to solve global and local optimization problems. The CapSA was selected for its simplicity, reduced computational complexity, and exploration/exploitation balance. The approach takes the form of an optimization problem to minimize energy consumption and total cost. Compared with other approaches, the proposed algorithm improves the efficiency of cloud service composition by convergent rapidly and obtaining better compositions.

Trust Evaluation and Prioritization of Cloud Service Providers Using MADM Methods

With the increasing market for cloud computing and the variety of Cloud Service Providers (CSPs) becoming available, making an appropriate choice of a CSP turns out to be challenging for the consumer. In order to cater to the user’s specific needs of quality, the services provided need to be user-centric. Trust plays an important role in the selection of cloud providers by users. This paper proposes CSP Trust evaluation, Prioritization and Recommendation System (CTPRS) for evaluating the trust of CSPs based on Quality of Service (QoS) parameters, and accordingly ranking and recommending them. MADM methods, namely ARAS, DEA, and GRA are used to evaluate trust by considering the user's preferences across multiple QoS parameters of CSPs. Comparative analysis of all three MADM methods is done using rank conformance, rank correlation, sensitivity, and computational complexity analysis. Experimental results demonstrate that GRA is superior to the other two methods. Thus, GRA is found to be the most suitable method and is therefore used for evaluating trust in CTPRS.

A hybrid whale optimization algorithm with differential evolution optimization for multi-objective virtual machine scheduling in cloud computing

Virtual machine (VM) scheduling in a dynamic cloud environment is often bound with multiple quality of service parameters; therefore, it is classed as an NP-hard optimization problem. Swarm-based metaheuristics, such as the whale optimization algorithm (WOA), have gained a notable reputation for solving optimization problems. The unique bubble-net hunting behaviour and fast convergence of the algorithm led to the development of a hybrid multi-objective whale optimization algorithm-based differential evolution (M-WODE) technique to solve the VM scheduling problem. The differential evolution (DE) strategy is used to replace the randomly generated solution produced by the WOA to ensure diversity in the solution and to strengthen the local search of the M-WODE. In addition, the DE technique is applied to the Pareto front produced by the WOA to escape local optima entrapment problems. The experimental results showed that the proposed M-WODE outperformed previous algorithms in most cases on makespan and the cost trade-off.

Intelligent Real-Time IoT Traffic Steering in 5G Edge Networks

In the Next Generation Radio Networks (NGRN), there will be extreme massive connectivity with the Heterogeneous Internet of Things (HetIoT) devices. The millimeter-Wave (mmWave) communications will become a potential core technology to increase the capacity of Radio Networks (RN) and enable Multiple-Input and Multiple-Output (MIMO) of Radio Remote Head (RRH) technology. However, the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring concurrently. The imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding requests. To handle this issue effectively, Machine Learning (ML) algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity conditions. This paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm, namely K-Nearest Neighbor (KNN), to improve the communication Quality of Service (QoS) in real-time IoT networks. KNN supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher power. The experimental dataset was generated by using computer software and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters, including communication reliability, latency, and throughput.

QoS-aware Automatic Web Service Composition with Multiple Objectives

Automatic web service composition has received a significant research attention in service-oriented computing over decades of research. With increasing number of web services, providing an end-to-end Quality of Service (QoS) guarantee in responding to user queries is becoming an important concern. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) are associated with a service, thereby, service composition with a large number of candidate services is a challenging multi-objective optimization problem. In this article, we study the multi-constrained multi-objective QoS-aware web service composition problem and propose three different approaches to solve the same, one optimal, based on Pareto front construction, and two others based on heuristically traversing the solution space. We compare the performance of the heuristics against the optimal and show the effectiveness of our proposals over other classical approaches for the same problem setting, with experiments on WSC-2009 and ICEBE-2005 datasets.

Optimal fitness aware cloud service composition using modified invasive weed optimization

Abstract Quality of Service (QoS)-aware cloud service composition is one of the pivotal problems in cloud computing. With the seamless proliferation of cloud services, it becomes challenging to obtain an optimal cloud service for composition that satisfies a user's requirements. Many composition models available in the literature compose cloud services based on one or two QoS parameters of the candidate services without considering the complete set. These composition models do not consider the connectivity constraints between the candidate cloud services for satisfying a workflow/function in a service composition. In this paper, we present a novel Optimal Fitness Aware Cloud Service Composition using Modified Invasive Weed Optimization dealing with multiple QoS parameters and satisfying the balancing of QoS parameters and the connectivity constraints of cloud service composition. We evaluate the performance of our approach on a data set of real world cloud services, to select the best optimal fitness aware cloud service composition. By performing the parametric and non-parametric test at 1% level of significance, our proposed method is statistically more accurate than the other methods compared.

Fuzzy Logic Aware QoS Multicasting in MANETs with Load Balance

Mobile ad-hoc network (MANET) is a decentralized and infrastructure less network where a nodes can communicate with other nodes within the access region. Due to mobility node can enter and leave a network at any moment. Due to unstable nature of MANETs, the provision of Quality of Service (QoS) to the applications is a difficult task. In this paper, fuzzy logic enabled QoS multicast routing is proposed. Here energy, bandwidth and link expiry time are considered as a QoS parameters. The existing methods lost their performance in handling multi constrained QoS protocols, since defining the dynamic priorities among the multiple QoS parameters is not a trivial task. In the proposed method “Fuzzy Logic Aware QoS Multicasting in MANETs with Load Balance-FQML”, this issue is overcome by using fuzzy logic. The Competency Factor of each intermediate node along the route is calculated by aggregating it’s QoS parameters using fuzzy inference system. In the classical multicast protocols, some of the nodes in the multicast tree are overloaded by having multiple branches towards the destination nodes. This leads to partition of the multicast tree and degrades the performance of protocols. In the proposed method, this problem is handled by limiting the number of branches at intermediate nodes. The results are taken in network simulator-ns2, where the proposed method could measure less number of path failures and improved results than existing methods.

Optimal media service selection scheme for mobile users in mobile cloud

Media cloud environments can provide a large number of multimedia services to mobile clients due to its flexibility and agility. However, a number of challenges need to be addressed so that these services are efficiently provided in terms of resources’ usage and energy consumption whilst improving the quality of service (QoS) and the user’s service satisfaction. This paper proposes a new media cloud distributed scheduling scheme that addresses these challenges, suitable for resource-intensive mobile application such as mobile video streaming. The proposed scheduling policy includes media service provisioning and cloud resource scheduling within the cloud datacenter, being able to jointly improve the mobile user’s satisfaction and the media cloud supplier’s revenue. Its aims are to minimize the service time, power consumption and costs for the service provider, through a convenient tradeoff of multiple QoS parameters and, consequently, increase the user’s satisfaction by reducing waiting times, service failure rate and power consumption of the mobile device. The validity of the proposed scheme is demonstrated by running experiments based on a practical use case of video streaming for mobile clients. The experiments were defined to allow to study the effects of request rate, video length, number of video streams and job size on the performance of the proposed media cloud distributed scheduling algorithm and compare it with related algorithms. The results show that proposed algorithm has better performance in terms of request failure rate, amount of energy consumed and response time.

A Fast and Scalable Mechanism for Web Service Composition

In recent times, automated business processes and web services have become ubiquitous in diverse application spaces. Efficient composition of web services in real time while providing necessary Quality of Service (QoS) guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose the services optimally. In this article, we present the design of a scalable QoS-aware service composition mechanism that balances the computational complexity of service composition with the QoS guarantees of the composed service and achieves scalability. Our design guarantees a single QoS parameter using an intelligent search and pruning mechanism in the composed service space. We also show that our methodology yields near optimal solutions on real benchmarks. We then enhance our proposed mechanism to guarantee multiple QoS parameters using aggregation techniques. Finally, we explore search time versus solution quality tradeoff using parameterized search algorithms that produce better-quality solutions at the cost of delay. We present experimental results to show the efficiency of our proposed mechanism.

A novel routing strategy for cognitive radio ad hoc network based on Sugeno fuzzy logic

In cognitive radio networks, a secondary user (SU) is dependent upon the spectrum, of varied frequency, temporarily vacated by primary users (PU) thereby making it quite a challenging task to design a protocol for finding a reliable and efficient routing path. To address the issue many routing schemes have been proposed based upon Mamdani type fuzzy inference system (FIS) to create a human intelligence analogy. The Mamdani type fuzzy inference mechanism is unable to simultaneously optimise multiple quality of service (QoS) parameters. This paper proposes a routing scheme based upon the Sugeno type fuzzy inference system (SFIS) that handles the continuously changing input parameters in quite effective manner and provides an effective performance with respect to multiple QoS parameters. The proposed routing scheme has been implemented using Matlab-9.0 and simulation results showed that the paths selected using this scheme are much more efficient than the paths selected by other routing schemes.

Multicast routing based on data envelopment analysis with multiple Quality of Service parameters

SummaryThe requirement for real‐time multimedia communications raises challenges for constructing multicast trees with multiple Quality of Service (QoS) parameters, for example, bandwidth, delay, delay jitter, error rates, and so on. In this paper, we present a new method for establishing multicast trees with multiple QoS parameters based on data envelopment analysis techniques. We first apply data envelopment analysis techniques for evaluating the relative efficiency of arcs in the presence of QoS parameters. Then, an integer linear programming model based on the relative efficiency of arcs is proposed to obtain multicast trees for transmitting data from a source to each destination. The proposed approach is illustrated and evaluated through numerical examples. Copyright © 2015 John Wiley & Sons, Ltd.

Research on QoE Evaluation for Mobile Streaming Service over LTE Network

The Quality of Experience (QoE) evaluation for mobile streaming service over LTE network is essential for mobile networking development. However, few QoE researches for mobile streaming service focus on LTE network. Based on the research of QoE for mobile streaming service over other networks, this paper achieves the simulation for network transmission for different videos using H.264 format on the ns-3 LTE simulation platform. What’s more, exponential model fittings are performed for not only single Quality of Service (QoS) parameter and QoE, but also multiple QoS parameters and QoE. Simulation results show that the network parameters and the Mean Opinion Score (MOS) of QoE have strong exponential relationships for streaming service over LTE network.

A Multicast Routing Algorithm with Cost, Delay and Bandwidth Constraints

Quality of service (QoS) generally assumes more than one QoS measure which implies that routing can be categorized as an instance of routing subject to multiple constraints: such as cost, delay, bandwidth, etc. The problem of constructing multicast trees is studied to meet the QoS requirements where it is necessary to provide bounded constraints among the source and all destinations while keeping the cost of the multicast tree low. So, a kind of source-destination QoS multicast routing problem is addressed about communication networks. The algorithm we presented takes bandwidth, delay and loss rate as premise, constructs routing selected function based on shortest path, modifies selected path according to the function above so as to fit multi-QoS parameters. Simulation results show that the algorithm has both lower delay and better performance and can be extended to cases of multiple QoS parameters conveniently.

Pragmatic Correlations of Quality-of-Experience and Quality-of-Service in IMS-Based IPTV Network

To let the service system react quickly on customers’ perception or user experience while using the service, mapping functions between Quality of Experience (QoE) and Quality of Service (QoS) are strongly required in purpose of building up an intelligent QoE control system upon adjusting QoS parameters. This article studies the changing behavior of QoE with respect to changes of QoS parameters in the context of video streaming service in an IP Multimedia Subsystem-based IP Television network (IMS-based IPTV network). The article is, in fact, an extended version of the paper published by the same authors (Thu-Huong Truong, 2012). In (Thu-Huong Truong, 2012), the authors studied QoE in both terms of Mean Opinion Scores and VQM as functions of each single QoS parameter such as: loss, jitter, and delay. In this extended content, the correlation between QoE and multiple QoS parameters will be introduced. The QoE-QoS correlation could be a significant first step to build a smart QoE monitoring and control mechanism as an added value to promote the IMS-based IPTV network.

A Fast Multi-objective Genetic Algorithm based Approach for Energy Efficient QoS-Routing in Two-tiered Wireless Multimedia Sensor Networks

With the growing demand for real time services in wireless sensor networks (WSNs), quality of service (QoS) based routing has emerged as an interesting research topic. But offering some QoS guarantee in sensor networks raises significant challenges. The network needs to cope with battery constraints, while providing QoS (end-to-end delay and reliability) guarantees. Designing such QoS routing protocols that optimize multiple objectives is computationally intractable. Higher power relay nodes can be used as cluster heads in a two-tiered WSN and these relay nodes may form a network among themselves to route data towards the sink. In this model, the QoS guarantee is determined mainly by these relay nodes. In this paper a solution based on NSGA-II is proposed for energy efficient QoS routing in two-tiered WSNs. Simulation results demonstrate that the proposed protocol outperforms network performance by optimizing multiple QoS parameters and energy consumption.

QoS based network selection scheme for 4G systems

Exponential growth of user demands on a single convergence platform has brought researchers to explore various aspects/features of Fourth Generation (4G) Mobile Communication System. Selection of application as per the user preference based on QoS (Quality of Service) is one salient feature of 4G. This user-centric system works on the basis to provide best services of the network to the users. In this paper, a selection algorithm has been proposed which provides a better way to implement user preferences as per the features of 4G. Rank based on distance function has been computed for various available services/access technologies, called networks. Weighted distance function is obtained based on multiple QoS parameters as per user needs. The proposed algorithm shows better results compared to single parameter based system, under a heterogeneous network system.

A meta-model for multiobjective routing in MPLS networks

MPLS (Multiprotocol Label Switching) enables the utilisation of explicit routes and other advanced routing mechanisms in multiservice packet networks, capable of dealing with multiple and heterogeneous QoS (Quality of Service) parameters. Firstly the paper presents a discussion of conceptual and methodological issues raised by multiobjective routing optimisation models for MPLS networks. The major contribution is the proposal of a multiobjective routing optimisation framework for MPLS networks. The major features of this modelling framework are: the formulation of a three-level hierarchical routing optimisation problem including network and service performance objectives, the inclusion of fairness objectives in the different levels of optimisation and a two-level stochastic representation of the traffic in the network (traffic flow and packet stream levels). A variant of the general model for two classes of traffic flows, QoS traffic and Best Effort traffic, is also presented. Finally a stochastic teletraffic modelling approach, underlying the optimisation model, is fully described.

QuESt: a QoS‐based energy efficient sensor routing protocol

AbstractRapid penetration of smart wireless devices and enormous growth of wireless communication technologies has already set the stage for deployment of wireless sensor networks (WSNs). While these small sensor nodes are often considered as the future of wireless communications, they also suffer from energy constraints. On the other hand, with increasing demand for real‐time services in next generation wireless networks, quality‐of‐service (QoS)‐based routing has emerged as an interesting research topic. Naturally offering some QoS‐guarantee in sensor networks raises significant challenges. The network needs to cope up with battery‐constraints, while providing precise QoS (end‐to‐end delay and bandwidth requirement) guarantee. More precisely, designing such QoS‐protocols, optimizing multiple objectives, is computationally intractable. Based on the multi‐objective genetic algorithm (MOGA), in this paper we propose a QoS‐based energy‐efficient sensor routing (QuESt) protocol that determines application‐specific, near‐optimal sensory‐routes demand, by optimizing multiple QoS parameters, (end‐to‐end delay and bandwidth requirements) and energy consumption. Simulation results demonstrate that the proposed protocol is capable of discovering a set of QoS‐based, near‐optimal routes, even with imprecise network information. Copyright © 2007 John Wiley & Sons, Ltd.

A QoS multicast routing optimization algorithm based on genetic algorithm

Most of the multimedia applications require strict quality of service (QoS) guarantee during the communication between a single source and multiple destinations. This gives rise to the need for an efficient QoS multicast routing strategy. Determination of such QoS-based optimal multicast routes basically leads to a multi-objective optimization problem, which is computationally intractable in polynomial time due to the uncertainty of resources in Internet. This paper describes a network model for researching the routing problem and proposes a new multicast tree selection algorithm based on genetic algorithms to simultaneously optimize multiple QoS parameters. The paper mainly presents a QoS multicast routing algorithm based on genetic algorithm (QMRGA). The QMRGA can also optimize the network resources such as bandwidth and delay, and can converge to the optimal or near-optimal solution within few iterations, even for the networks environment with uncertain parameters. The incremental rate of computational cost can close to polynomial and is less than exponential rate. The performance measures of the QMRGA are evaluated using simulations. The simulation results show that this approach has fast convergence speed and high reliability. It can meet the real-time requirement in multimedia communication networks.

QM2RP: A QoS-Based Mobile Multicast Routing Protocol Using Multi-Objective Genetic Algorithm

With the increasing demand for real-time services in next generation wireless networks, quality-of-service (QoS) based routing offers significant challenges. Multimedia applications, such as video conferencing or real-time streaming of stock quotes, require strict QoS guarantee on bandwidth and delay parameters while communicating among multiple hosts. These applications give rise to the need for efficient multicast routing protocols, which will be able to determine multicast routes that satisfy different QoS constraints simultaneously. However, designing such protocols for optimizing multiple objectives, is computationally intractable. Precisely, discovering optimal multicast routes is an NP-hard problem when the network state information is inaccurate - a common scenario in wireless networks. Based on the multi-objective genetic algorithm (MOGA), in this paper we propose a QoS-based mobile multicast routing protocol (QM2RP) that determines near-optimal routes on demand. Our protocol attempts to optimize multiple QoS parameters, namely end-to-end delay, bandwidth requirements, and residual bandwidth utilization. Furthermore, it is fast and efficient in tackling dynamic multicast group membership information arising due to user mobility in wireless cellular networks. Simulation results demonstrate that the proposed protocol is capable of discovering a set of QoS-based, near-optimal multicast routes within a few iterations, even with imprecise network information. Among these routes one can choose the best possible one depending on the specified QoS requirements. The protocol is also scalable and yields lower multicast call-blocking rates for dynamic multicast group size in large networks.