Search

The Mobile intermittent wireless connectivity limits the evolution of the mobile landscape, such that this progress coupled with the ubiquitous nature of the Internet and the web services. The web service Reliability achieves the low overhead communication and retrieves the appropriate state response from the web service consumption. This paper discusses and analyzes the most recent approaches that achieve the reliability of web services consumed by mobile devices. Achieving the reliable web service consumption istended to two approaches: Middleware approach and Mobile Agent(MA) approach. Both of them focus on ensuring the request execution under the communication limitations and services temporary unavailability., 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2015. arXiv admin note: substantial text overlap with arXiv:2207.12404, arXiv:2207.11608

Pervasive networks formed by users’ mobile devices have the potential to exploit a rich set of distributed service components that can be composed to provide each user with a multitude of application level services. However, in many challenging scenarios, opportunistic networking techniques are required to enable communication as devices suffer from intermittent connectivity, disconnections and partitions. This poses novel challenges to service composition techniques. While several works have discussed middleware and architectures for service composition in well-connected wired networks and in stable MANET environments, the underlying mechanism for selecting and forwarding service requests in the significantly challenging networking environment of opportunistic networks has not been entirely addressed. The problem comprises three stages: i) selecting an appropriate service sequence set out of available services to obtain the required application level service; ii) routing results of a previous stage in the composition to the next one through a multi-hop opportunistic path; and iii) routing final service outcomes back to the requester. The proposed algorithm derives efficiency and effectiveness by taking into account the estimated load at service providers and expected time to opportunistically route information between devices. Based on this information the algorithm estimates the best composition to obtain a required service. It is shown that using only local knowledge collected in a distributed manner, performance close to a real-time centralized system can be achieved. Applicability and performance guarantee of the service composition algorithm in a range of mobility characteristics are established through extensive simulations on real/synthetic traces.

Composing several API-defined services into one composite service per user requirements has become an important service creation approach in the cloud-enabled API economy. Various service selection approaches in support of service composition on demand have been proposed. They usually assume that networking resources are over-provisioned and their usage needs not be considered when making quality-aware service composition decisions. In practice, these approaches often lead to wasteful network resource consumption and impractical end-to-end QoS optimality for cloud-based services. This paper proposes a network-aware cloud service composition approach, named NetMIP, with comparative experimental evaluations for the clouds that adopt the widely deployed fat-tree network topology. By formalizing the service composition goal as a multi-objective constraint optimization problem, we have validated the proposed approach can be used to effectively reduce network resource consumption and deliver QoS optimality while satisfying the end-to-end QoS constraints for the candidate composite services in the cloud. The comparative experimental evaluations are done via a credible cloud infrastructure simulation system, named WebCloudSim. Extensive evaluation results show that NetMIP outperforms several representative cloud service composition approaches in terms of network resource consumption, QoS optimality, and computation time under various service selection workloads and fat-tree network topology settings.

Workflow-based Service-oriented WSN s have recently received a lot of attention from both academia and industry. With well-defined service components, various flexible WSN applications can be developed. However, the key characteristic of WSN s is resource constraints. Sensor nodes in WSN s have limited storage, computation and especially limited energy. As service components in WSN s rely on the data gathered by sensor nodes, they are also resource-constrained. Unfortunately, traditional workflow technologies (i.e., service selection, composition and adaptation) ignore the residual energy of services. This will bring unbalanced energy consumption and furthermore shorten the network lifetime. In order to resolve this issue, we proposed an energy-aware QoS -guaranteed workflow management mechanism. In this mechanism, a new QoS model is first presented to improve the QoS evaluation. Then, based on this QoS model, an efficient service selection schema, which considers both the energy and the QoS of services, is proposed. Furthermore, an adaptation mechanism for balanced energy consumption is also proposed. Experimental evaluations demonstrate the capability of our proposed approach.

The modern electronic age consists of many new approaches for the communication among the human civilizations and the AD HOC network is one of the successful self-configuring and infrastructure-less communicating approach. Mobile ad hoc networks (MANETs) comprised of mobile nodes organize themselves in such a manner that the can move freely inside the network and frequently change their position. Open architecture and dynamic nature of the network enhance its use in multimedia applications that needs graphics, audio, data, image, video and animation. Now a day’s the increasing use of multimedia and Internet technology needs quality of service (QoS) in MANETs in order to provide better service for the companies. This type of applications lends themselves well to multicast operations. As multicasting supports group oriented computing it can improve the QoS of the wireless medium by means of sending multiple copies of packets by exploring the internet broadcast properties of wireless transmission. Hence QoS multicasting plays a great role in MANETs for multimedia applications. However it is very difficult and challenging task to provide QoS multicasting. With unique features and by means of different recovery mechanisms, many researchers have been proposed various QoS based multicasting routing protocols for MANETs. In order to assist QoS multicasting routing protocols design for MANETs, we characterize the taxonomy of the multicast routing protocols and design features in this paper.

Cyber-physical-social systems comprise physical, cyber, and social worlds with various resources as services. The operation and configuration of these services require service composition approaches that can be used to integrate essential components in these worlds, and organize and share relevant information as needed. Although the present service-oriented architecture can effectively support service composition, there still exist many challenges such as high time cost and low reliability for cyber-physical-social systems. In this paper, we propose a fast and reliable service composition approach to integrate the physical network, cyberspace, and social network. In this approach, first, skyline component computation is performed to reduce the solution space; then, the coefficient of variation is employed to filter the components with high quality of service (QoS) fluctuation and finally the best optimal components are selected by maximizing the fitness function based on users’ end-to-end QoS requirements. We applied our approach to real-world dataset and the results showed that our proposed approach has better reliability as well as lower time cost than other approaches.

Mobile edge computing is an emerging technology that provides services within the close proximity of mobile subscribers by edge servers that are deployed in each edge server. Mobile edge computing platform enables application developers and content providers to serve context-aware services (such as service recommendation) by using real time radio access network information. In service recommendation system, quality of service (QoS) prediction plays an important role when mobile devices or users want to invoke services that can satisfy user QoS requirements. However, user mobility (e.g., from one edge server to another) often makes service QoS prediction values deviate from actual values in traditional mobile networks. Unfortunately, many existing service recommendation approaches fail to consider user mobility. In this paper, we propose a service recommendation approach based on collaborative filtering and make QoS prediction based on user mobility. This approach initially calculates user or edge server similarity and selects the Top-K most-similar neighbors, predicts service QoS, and then makes service recommendation. We have implemented our proposed approach with experiments based on Shanghai Telecom datasets. Experimental results show that our approach can significantly improve on the accuracy of service recommendation in mobile edge computing.

Quality of cloud service (QoS) is one of the crucial factors for the success of cloud providers in mobile cloud computing. Context-awareness is a popular method for automatic awareness of the mobile environment and choosing the most suitable cloud provider. Lack of context information may harm the users’ confidence in the application rendering it useless. Thus, mobile devices need to be constantly aware of the environment and to test the performance of each cloud provider, which is inefficient and wastes energy. Crowdsourcing is a considerable technology to discover and select cloud services in order to provide intelligent, efficient, and stable discovering of services for mobile users based on group choice. This article introduces a crowdsourcing-based QoS supported mobile cloud service framework that fulfills mobile users’ satisfaction by sensing their context information and providing appropriate services to each of the users. Based on user's activity context, social context, service context, and device context, our framework dynamically adapts cloud service for the requests in different kinds of scenarios. The context-awareness based management approach efficiency achieves a reliable cloud service supported platform to supply the Quality of Service on mobile device.

To provide data rates of the order of hundreds of Mbps and multimedia services, standardization efforts for next generation (4G) systems are focusing on target technologies and seamless connectivity through various types of networks, including wireline networks and WLANs. Different types of multiple access techniques, such as the ones based on multicarrier CDMA and OFDM (orthogonal frequency division multiplexing) have been proposed. There is a need for functional integration of the multiple networks, and, with the evolution of IPv6 and QoS support for IP networks, an IP based interconnectivity is best suited. A QoS aware adaptive radio resource management technique based on multi-code multicarrier CDMA is discussed. We develop a novel radio access method and develop algorithms for allocating and controlling radio network resources so that system performance can be maximized and guaranteed QoS for multimedia services can be provided within the DiffServ environment.

To upper-bound both delay and error-rate for sup-porting time-sensitive multimedia services over 5G and beyond (5G+) mobile wireless networks, researchers have developed several advanced techniques, including statistical delay-bounded quality-of-service (QoS) provisioning and finite blocklength coding (FBC). On the other hand, cell-free massive-multi-input multi-output (m-MIMO) system, where a large number of distributed access points (APs) jointly serve all users in a coordinated manner, has emerged as one of the key promising techniques to significantly improve various mobile QoS performance indices, including throughput, user-coverage probability, energy/spectrum efficiencies, etc. Inspired by the more diverse distances between mobile users and APs in cell-free m-MIMO systems compared with traditional centralized m-MIMO systems, integrating non-orthogonal multiple access (NOMA) with cell-free m-MIMO can significantly enhance spectral efficiency for massive connections of mobile devices. But, due to the complexity of analyzing the networking dynamics in the finite blocklength regime, how to efficiently characterize the queuing process for cell-free m-MIMO-NOMA schemes using FBC based statistical delay/error-rate bounded QoS theory is still an open problem. To solve the above challenges, we propose FBC based cell-free m-MIMO-NOMA schemes over 5G+ mobile wireless networks under statistical delay/error-rate bounded QoS constraints. In particular, we establish cell-free m-MIMO-NOMA system models over Rician wireless fading channels. Considering statistical delay/error-rate bounded QoS constraints, we derive a closed-form expression for an upper bound on the delay violation probability over downlink Rician wireless fading channels with non-vanishing error probability. Our simulation results validate and evaluate our proposed FBC based cell-free m-MIMO-NOMA schemes.

Today, it is essential to provide the sufficient quality-of-service (QoS) for real-time mobile applications over wireless channels. Researchers have proposed a variety of solutions, thereby, including resource allocation, data scheduling, and cross-layer design, etc. However, hardly any previous study has investigated the advantage of users’ mobility. This paper proposes a novel QoS-oriented navigation approach to usher the mobile device(s) to the appropriate location, which can enable a sufficient QoS. The proposed mechanism will establish a two-dimensional geographical packet-success-rate (PSR) map, which can help to guide the mobile user(s) to locate the spot providing the QoS sufficing the application(s). With the help of the indoor positioning system and this PSR map, an innovative ushering method can be facilitated for the appropriate mobile users’ placement to reach the satisfactory QoS. The corresponding performance for our proposed scheme has been evaluated through the experiments over the femtocell network. Experimental results demonstrate the effectiveness of our proposed method to provide the sufficient QoS by ushering the user's placement for three networked multimedia applications.

Automated service composition fulfills complex tasks by combining different existing web services together. Unfortunately, optimizing service composition is still a challenging area that needs to be addressed. In this article, we propose a novel relational database approach for automated service composition. All possible service combinations are generated beforehand and stored in a relational database. When a user request comes, our system composes SQL queries to search in the database and return the best Quality of Service (QoS) solutions. We test the performance of the proposed system with a web service challenge data set. Our experimental results demonstrate that this system can always find top-K valid solutions to satisfy user's functional and non-functional requirements.

With the increasing processing capability of mobile platforms and advancements in Internet of Things, modern mobile devices have shown a favorable prospect for on-the-go service provisioning. However, there is much to be done to realize this. A detailed, dynamic, and lightweight service description is an important requirement for automatic and efficient discovery, selection, and subsequent provisioning of services over mobile devices. Traditional approaches for service description are usually not directly adaptable to mobile environments owing to the latter’s dynamic and distinct nature. In this paper, we propose a dynamic, lightweight, extensible, and detailed service description especially designed for mobile environments, considering crucial aspects such as isolated data source, collaborator partners, and hardware aspects along with the functional, non-functional, business, and contextual aspects. The description has been partitioned along these lines and various parts of the description are distributed between service registries and the mobile service providers. An up-to-date and light weight description has been achieved by this, without compromising on the overall consistency of the description. A prototype of the proposed system has been implemented with the intent of validating the feasibility of the approach. Further, the proposed approach is suitable for a heterogeneous environment comprising both wired and wireless systems.

This paper aims to present a novel dynamic quality of service (QoS) algorithm for home networks. The algorithm carries out the QoS management of a service for a user in a home network based on the profile consisting of the past responses of the user for that service. It performs the prediction of the future responses to the service from the profile using the general regression neural network algorithm. The algorithm finds the bandwidth allocation consuming minimum total bandwidth among those predicted to produce desired responses. The minimum bandwidth allocation is then used for the service. The corresponding user responses are used to update the profiles for the subsequent dynamic QoS managements. The algorithm requires no offline training and achieves fast adaptation to the user responses. Both analytical and numerical results reveal that the proposed algorithm provides an effective alternative for user-oriented QoS management in the heterogeneous home networks.

Multi-tenant software-defined networks have emerged as a promising service platform to manage and share virtual network services between tenants and end-users. However, when using a service level agreement (SLA) in a multi-tenant circumstance, the resource competition among tenants incurred in many challenges on the quality of services (QoS) provisioning. First, as the existing QoS provisioning was to translate the high-level SLA contact into a group of corresponding low-level QoS rules, this traditional top-down translation mechanism was too ossified to support the dynamic SLA negotiation. Second, since an end-user usually requested multiple services and different services had different QoS requirements, the QoS provisioning required tenants to monitor and control the network status though OpenFlow protocol in a fine-grained way. To address these issues, we propose an SLA-aware fine-grained QoS provisioning (SFQP) scheme for MTSDN. The SFQP scheme extracts the eigen characteristics of each packet by application-aware technology automatically. Considering tenants are more willing to serve for users which have popular SLA contact to earn more revenues, we predict the SLA popularity by K-nearest neighbor. Moreover, we present an SLA-aware dynamic bandwidth allocation algorithm to implement the proposed SFQP scheme. The feasibility of the proposed scheme is verified by simulations of the SLA taxonomy, bandwidth utilization, and bandwidth fluctuation.

Mobile users are making more demands of common networks by running applications such as network streaming of audio, video as well as immersive gaming that demand a high Quality of Service. One way to address this problem is by making services mobile, such that the services will move closer to the users as they move around. This ensures that low latencies are maintained between the client and server resulting in a better Quality of Service. At present, new architectures such as the Y-Comm framework, attempt to provide a platform to support intelligent service migration mechanisms. However, what is also needed is to provide QoS mechanisms in order to facilitate efficient service migration. This requires techniques to measure the QoS in terms of bandwidth, latency and burst characteristics at various locations to which the server could be migrated. In addition, the emergence of Software Defined Networking as well as new end to end control mechanisms such as the Network Management Control Protocol and the development of new transport protocols will allow a new framework to support mobile QoS-Aware applications and services that will be a key part of the Future Internet. This paper explores the development of a new applications and services framework for Future Internet that replaces the traditional IP framework. New mechanisms are developed to decide when and where to move services and a video on demand scenario is analysed. An analytical model is investigated to provide results based on bandwidth and latency. The results show that this approach is valid and should lead to better QoS and better Quality of Experience for mobile users.

The ever-increasing mobile traffic has inspired deployment of capacity and performance enhancing network services within mobile networks. Owing to recent advances in network function virtualization, such network services can be flexibly and cost-efficiently deployed in the mobile network as software components, avoiding the need for costly hardware deployment. Nevertheless, this complicates network planning by bringing the need for service function chaining. In this paper, we study mobile network planning through a software-defined approach, considering both quality-of-service and reliability of different classes of traffic. We define and formulate the traffic steering problem for service function chaining in mobile networks, which turns out to be $\mathcal {NP}$ -hard. We then develop a fast approximation scheme for the problem, and evaluate its performance via extensive simulation experiments. The results show that our algorithm is near-optimal, and achieves much better performance compared with baseline algorithms.

The development of information and communication technology has improved the stability in wireless network services at various levels. The services provided can be accessed by the network users through a variety of devices like Mobile phones, PDA (Personal Digital Assistant) and communication media. The quality of service in wireless network highly depends on various factors like service availability, data availability, throughput and latency. Many approaches were introduced earlier for location-based services, but still, there are hurdles in data management, service selection, and other QoS metrics. It is necessary to develop an optimized location based service selection approach using multi attribute data estimation (MADE) technique. In that intention, the service requested by the user is identified with similar service. Upon identifying the service, the set of supporting services which are necessary to complete the request are recognized. Based on the services’ necessity, user location and service availability, the service availability measure (SAM) is computed based on the selected service point. By selecting the service according to their SAM value, the service selection efficiency can be improved. The selection of service point is performed as per the data availability and multi attribute data estimation technique. The MADE approach enables to collect the data requested and the data sensor which possess the data. Further, it facilitates the data depth of each sensor. Based on these contents, a service support score is computed by SAM, MADE to select a single service point. The proposed algorithm improves the performance in service selection and increases the quality of service parameters.

The implementation of a robust communication system is essential for smart grids. This new system should provide an excellent quality of service (QoS) and a robust communication infrastructure, which must integrates the various devices on the network and its areas. This paper presents a study of the major network's needs, and describes the main applications of smart grids, and its QoS requirements. Some proposals in the literature are presented, and QoS needs in electrical grids are discussed. Because of its importance, the Advanced Metering Infrastructure (AMI) is evaluated and tested. The tests are made in an AMI with QoS implemented and also without QoS. The results indicate that the mechanisms and parameters for QoS in AMI are needed an also improving the messages speed.

Cloud applications built on service-oriented architectures generally integrate a number of component services to fulfill certain application logic. The changing cloud environment highlights the need for these applications to keep resilient against QoS variations of their component services so that end-to-end quality-of-service (QoS) can be guaranteed. Runtime service adaptation is a key technique to achieve this goal. To support timely and accurate adaptation decisions, effective and efficient QoS prediction is needed to obtain real-time QoS information of component services. However, current research has focused mostly on QoS prediction of working services that are being used by a cloud application, but little on predicting QoS values of candidate services that are equally important in determining optimal adaptation actions. In this paper, we propose an adaptive matrix factorization (namely AMF) approach to perform online QoS prediction for candidate services. AMF is inspired from the widely-used collaborative filtering techniques in recommender systems, but significantly extends the conventional matrix factorization model with new techniques of data transformation, online learning, and adaptive weights. Comprehensive experiments, as well as a case study, have been conducted based on a real-world QoS dataset of Web services (with over 40 million QoS records). The evaluation results demonstrate AMF’s superiority in achieving accuracy, efficiency, and robustness, which are essential to enable optimal runtime service adaptation.

A novel quality of service (QoS) provisioning algorithm for home networks is presented in this paper. The algorithm carries out the QoS-aware bandwidth allocation using the general regression neural networks (GRNNs). Among all the allocations predicted to receive positive service responses, the algorithm finds the allocation with minimum total bandwidth for the current service. The service response prediction is based on the GRNN with the training set containing the bandwidth allocations and their service responses for past transmissions. The new service responses will then be used to update the training set for the subsequent transmissions. To attain accurate tracking of diversified service requirements, flexible specification of service response levels and QoS levels are provided. Both analytical and numerical studies reveal that the proposed algorithm is able to provide prompt or steady reactions to the service feedback depending on the variations of the source data rates. Because of its simplicity and effectiveness, the proposed algorithm is well suited for dynamic QoS management for heterogeneous home networks.

Objectives: Differentiated Services are implemented by marking in the Internet Protocol (IP) packet header and treat them differently on the basis of the requirements of the services to which a packet belongs. Objective of this study is to determine the impact of Differentiated Services on the different parameters of Quality of Services (QoS) of major services of the Internet. This study aims to investigate the impact of Differentiated Services on Quality of Services (QoS) of different services of internet. Method: In order to determine the impact of the Differentiated Services on the Quality of Services parameters of the different services like Electronic-Mail, web browsing, IP telephony are selected for assessing the impact on Response time, waiting time, end to end delays and jitter. For this purpose, network simulator is used. Findings: It is observed that Differentiated Services is very successful for the implementation of Quality of Services of the Internet for various services of the Internet. It is observed that Differentiated Services has substantial impact on the response time, waiting time, end to end delays, packet delays and jitter. These are the major parameters to measure the Quality of Service of different services of the Internet. Application: This finding would be helpful to design the comprehensive Quality of Services framework in future and would be helpful for appropriate decisions for the researcher and technology development organization to make future Internet services to operate at acceptable level.

Purpose In this paper, a Quality of Service-sensitive customer behavior model graph (QoS-CBMG) is proposed for use in service quality adaptation in e-commerce systems. Success in achieving customer satisfaction and maximizing profit in e-commerce is highly dependent on the QoS provided. However, providing high-level QoS for all customers in all Web sessions is often deemed costly and inefficient. Therefore, a QoS-sensitive model for formulating QoS-aware offers to customers is required. The paper aims to respond to this necessity. Design/methodology/approach Process mining is adopted as the knowledge extraction technique for developing a QoS-CBMG. If it is assumed that user navigation on a website is a process, then clickstreams during one user’s navigations can be considered process steps. Findings The application of both QoS-CBMG (the new model) and CBMG (the classic version) to the same real data set demonstrated that the proposed method outperforms CBMG due to its reduction of average absolute error in the measurement scale. This finding also verifies the assumption that customer behavior is sensitive to the level of QoS. Research limitations/implications From a theoretical viewpoint, the obtained QoS-CBMG facilitates the adaption in e-commerce systems, which leads to conduct the user to the desired behavior by tuning QoS levels in different Web sessions in a dynamic manner. This implication is due to the fact that QoS-CBMG can predict the upcoming clickstream of the customer at different QoS levels. Practical implications Using the proposed model for the adaptation of service quality in e-commerce websites not only results in the efficient management of the provider’s resources but also encourages customer purchases from the website and increases profitability. It is noteworthy that with the advent of cloud computing, e-commerce websites are enabled to provide various levels of QoS for their customers by supplying their basic services (e.g. infrastructure, platform) through cloud platforms. Originality/value According to the best of our knowledge, no previous model has taken into account the QoS dimension for customer behavior modeling. The main contribution of this paper is to propose a CBMG that is sensitive to the QoS provided to customers during their navigation to formulate QoS-aware offers to them.

The service-oriented paradigm is emerging as a new approach to heterogeneous distributed software systems composed of services accessed locally or remotely by middleware technology. How to select the optimal composited service from a set of functionally equivalent services with different quality of service (QoS) attributes has become an active focus of research in the service community. However, existing middleware solutions or approaches are inefficient as they search all solution spaces. More importantly, they inherently neglect QoS uncertainty owing to the dynamic network environment. In this paper, based on a service composition middleware framework, we propose an efficient and reliable service selection approach that attempts to select the best reliable composited service by filtering low-reliability services through the computation of QoS uncertainty. The approach first employs information theory and probability theory to abandon high-QoS-uncertainty services and downsize the solution space. A reliability fitness function is then designed to select the best reliable service for composited services. We experimented with real-world and synthetic datasets and compared our approach with other approaches. Our results show that our approach is not only fast, but also finds more reliable composited services. Design a service composition middleware for heterogeneous distributed systems.An efficient and reliable service selection approach based on information and variance theory is proposed.Experiments with real-world dataset show that the proposed technique is superior to other existing approaches.

In recent years, service-based applications are deemed to be one of the new solutions to build an enterprise application system. In order to answer the most demanding needs or adaptations to the needs of changed services quickly, service composition is currently used to exploit the multi-service capabilities in the Information Technology organizations. While web services, which have been independently developed, may not always be compatible with each other, the selection of optimal services and composition of these services are seen as a challenging issue. In this paper, we present cuckoo search algorithm for web service composition problem which is called ‘CSA-WSC’ that provides web service composition to improve the quality of service (QoS) in the distributed cloud environment. The experimental results indicate that the CSA-WSC compared to genetic search skyline network (GS-S-Net) and genetic particle swarm optimization algorithm (GAPSO-WSC) reduces the costs by 7% and responding time by 6%, as two major reasons for the reduction of improvement of the quality of service. It also increases provider availability up to 7.25% and the reliability to 5.5%, as the two important QoS criteria for improving the quality of service.

With the progress of network technology in recent years, multimedia streaming applications have become increasingly popular. However, it is difficult to achieve quality of service and efficiency for multimedia streaming over vehicular networks because of the high mobility feature. Over the existing network architecture, it is difficult to immediately analyze the status of the entire network, and then establish the rules of allocation and management. However, the novel network architecture, software-defined networking, offers other options for making network management more efficient, especially for the 5G network environment. Hence, a buffer-aware streaming approach is proposed to allow users to play multimedia streaming over vehicular 5G networks, in the case of handover between different eNodeBs, to achieve minimum delay and have better quality of service. According to the user's mobility information, the status of the player buffer, and the current strength of the network signal, the proposed approach can provide the transmission strategy of multimedia streaming to the SDN controller. Finally, the experimental results proved that the proposed approach is able to not only adjust the priority of streaming content segments with the buffer and mobility status of user equipment to effectively retain overall streaming services quality, but also avoid the delay of streaming content transmission for 5G vehicular networks.

Innovations in Internet networks and applications are equally important. However, there is controversy in the literature regarding whether it is network or application innovation that leads to the ...

With the development of cloud service technologies, the amount of services grows rapidly, leading to building high-quality services an urgent and crucial research problem. Service users should evaluate QoS to select the optimal cloud services from a series of functionally equivalent service candidates, because QoS performance of services is varying over time. The reason is that QoS is related to the service overload and network environments. This phenomenon makes QoS prediction for users located in different places even harder. Furthermore, since service invocations are charged by service providers, it is impractical to let users invoke required cloud services to evaluate quality with respect to time and resources. To solve this problem, this paper proposes a cloud service QoS prediction method, called TPP (Time-aware and Parallel Prediction), to provide time-aware and parallel QoS value prediction for various service users. TPP is able to predict without additional invocation of cloud services, since it uses past cloud service usage experience from different service users. We propose and implement tensor decomposition algorithm on the Spark system. The results of extensive experimental show the accuracy and efficiency of TPP.

Many multimedia applications exhibit QoS flexibility, as they support certain quality degradation. In the end, increasing or decreasing the QoS level results in increasing or decreasing the resources demanded by the applications, which may be useful for overcoming system overload. In this context, the present paper, based on previous work of authors, proposes a model-based and multi-agent based approach for the management of QoS flexibility in multimedia applications founded on a suitable management of system resources. More precisely, it describes a modeling approach to capture the QoS flexibility demands, mapping them to resource demands. At runtime, a multi-agent based middleware allows the best QoS level of the running applications by adjusting their demand of system resources, i.e. their QoS level, to the resource availability at a concrete time. This proposal has been evaluated through a video-surveillance application.

Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases.

With the advances of network function virtualization and cloud computing technologies, a number of network services are implemented across data centers by creating a service chain using different virtual network functions (VNFs) running on virtual machines. Due to the complexity of network infrastructure, creating a service chain requires high operational cost especially in carrier-grade network service providers and supporting stringent QoS requirements from users is also a complicated task. There have been various research efforts to address these problems that only focus on one aspect of optimization goal either from users such as latency minimization and QoS based optimization, or from service providers such as resource optimization and cost minimization. However, meeting the requirements both from users and service providers efficiently is still a challenging issue. This paper proposes a VNF placement algorithm called VNF-EQ that allows users to meet their service latency requirements, while minimizing the energy consumption at the same time. The proposed algorithm is dynamic in a sense that the locations or the service chains of VNFs are reconfigured to minimize the energy consumption when the traffic passing through the chain falls below a pre-defined threshold. We use genetic algorithm to formulate this problem because it is a variation of the multi-constrained path selection problem known as NP-complete. The benchmarking results show that the proposed approach outperforms other heuristic algorithms by as much as 49% and reduces the energy consumptions by rearranging VNFs.

Cloud data centers are widely employed to offer reliable cloud services. However, low resource utilization and high power consumption have been great challenges for cloud providers. Moreover, the rapid increase in demand for affordable cloud services magnifies the obstacles for proficient resource management policies. In this paper, we investigate how to improve resource utilization and power consumption in cloud data centers when delivering services with statistically guaranteed Quality of Service (QoS). We assume that the service provider hosts different types of services, each of which has request classes with different QoS requirements. Different from the traditional approaches that distribute workloads with different QoS levels on different Virtual Machines (VMs), we introduce an approach to pack requests of the same service type, even with different QoS requirements, into the same VM, and to remove potential failure requests in time to improve resource usage and energy cost. We formally prove that our algorithm can statistically guarantee QoS conditions in terms of deadline miss ratios. We develop a cloud prototype to empirically validate our proposed methods and algorithm. Our experimental results demonstrate that our approach can significantly outperform other traditional approaches in terms of QoS guarantees, power consumption, resource demand and electricity cost.

Now we are in the era of ubiquitous computing. Internet of things (IoT) is getting matured in various parts of the world. In coming few years' billions and trillions of things will be connected to the internet. To deal with these huge number of devices in a network we need to consider Quality of Service (QoS)parameters so that system operations can be performed in a smoother way. Mathematical modelling of these QoS parameters gives an idea about which factors are needs to consider while designing any IoT-enabled system at the same time it will give the performance analysis of the system before implementation. In this paper comprehensive literature survey is done to discuss various issues related to QoS and gap analysis is also done for IoT Enabled systems. This paper proposes general steps to build a mathematical model for a system. It also proposes the mathematical model for QoS parameters like reliability, communication complexities, latency and aggregation of data for IoT. To support proposed mathematical model proof of concept also given.

This paper makes the use of cloud architecture as computational power for mobile device application. The multiple context based service scheduling scheme for balancing the cost and the benefits of mobile users and cloud datacenter supplier in mobile cloud is proposed, which can adapt to the system context without compromising mobile device user's QoS. The multiple context based mobile cloud service scheduling is formulated as a utility optimization problem, which is expressed as the summation of utilities of mobile cloud users. The proposed model aims to satisfy huge number of mobile requests, improve mobile user's QoS experiences and reduce system overheads. The proposed problem consists of cloud resources allocation optimization subproblem and mobile user QoS optimization subproblem. The multiple context based service scheduling algorithm is proposed to efficiently and adaptively schedule resource according to the context information. The proposed algorithm is validated through a series of experiments. The proposed algorithm can provide better QoS satisfaction for mobile device user, improve cloud resource utilization and reduce the cost.

With traveling professions and nomadic lifestyles on the increase there is a growing need for comprehensive mobile networking within the Next Generation Network, and the security, reliability and technical independency of the Short Message Service (SMS) is becoming more and more indispensable for business processes. Since competitiveness goes hand-in-hand with this business capability, and methods to determine the quality of this service are becoming equally indispensable. This paper presents possible methods to evaluate the “Completion Rate of SMS” and “End-to-End Delivery Time” and combinations of them (new metric in this paper). A software tool, QoSCalc(SMS), has been developed in collaboration with the authors in order to verify whether service providers are abiding by the Service Level Agreements. The tool has been tested in a real measurement environment and the results are presented graphically here and interpreted.

Previous research on e-commerce through multimedia mobile applications primarily focused on the relationships between e-service factors and positive customer behavior. However, these studies did not consider the consumer purchasing decision process sufficiently to develop firms’ strategic priorities in the context of e-commerce services through multimedia mobile applications. To fill this gap, we conducted a choice-based conjoint analysis (CBCA) to identify the preferences for each attribute and the interaction effects between each pair of attributes related to the purchasing decision, and used an analytic hierarchy process (AHP) analysis to calculate the overall priorities for every sub-criterion and partial priorities for each main criterion. The main findings from the integrated CBCA and AHP are that consumers must be able to fulfill their most basic needs through multimedia mobile applications’ e-commerce service, e-commerce service providers using multimedia mobile applications should enhance their existing advantages to complement their shortcomings, and e-commerce service providers besides social commerce operators should change their business structures to adapt to the new e-commerce environment. We also discuss the main contributions and implications to improve e-commerce service satisfaction and strategic decision-making.

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.

The recent rapid growth of cities, the evolution of wireless/mobile technologies and diverse demanding applications, the world is becoming more and more urban. Today, wireless and mobile networks as well multimedia applications play an essential challenging role within urban environment and they are take a considerable attention to improve and render the urban environment increasingly sustainable and livable. The main purpose of wireless and mobile networks is to share the fresh updated information collected around the urban environment between the stakeholders and the citizen constantly. However, it’s very difficult to achieve this task in a complex, distributed, large (billions of heterogeneous connected devices) and diverse innovative demanding applications and services in urban environment. It is noteworthy that these demanding applications and services require constructing a wireless network that satisfies the scalability and QoS in this environment. In this paper, we propose an efficient k-hop scalability and QoS topology management scheme for large scale Mobile Ad Hoc Networks which is suitable for demanding application in urban environment. Our proposed scheme meets the scalability needs of urban environment as well the QoS of demanding applications like multimedia applications, by constructing a two hierarchical topology level based on trade-off between the clustering and virtual backbone mechanisms. To achieve and supports network scalability when network size increases (thousands of nodes) and satisfy the QoS of demanding applications, we propose in the first level, an efficient weight based clustering approach which considers a trade-off between the clustering metrics such as trust, density, mobility and energy, and QoS requirements like available bandwidth and link quality. In the second level, we based on connected dominating set algorithm to build a virtual backbone. Simulation results show that our scheme is able to reduce the number of cluster heads, the number of cluster heads re-affiliations, the total overheads and the number of dominating set, and increase the clusters stability, packets delivery ration and the network life time compared to other topology management schemes.

We study the problem of dynamically migrating a service in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. The service may have single or multiple replicas, each running on a virtual machine. As the origin of mobile accesses frequently changes over time, this problem is particularly important for time-bounded services to achieve enhanced Quality of Service and cost effectiveness. Moving the service closer to the client locations not only reduces the service access latency but also minimizes the network costs for service providers. However, these benefits are not free. The migration comes at a cost of bulk-data transfer and service disruption, and hence, increasing the overall service costs. To gain the benefits of service migration while minimizing the caused monetary costs, we propose an efficient search-based algorithm Dmig to migrate a single server, and then extend it as a scalable algorithm, called mDmig , to the multi-server situation, a more general case in the cloud. Both algorithms are fully distributed, symmetric, and characterized by the effective use of historical access information to conduct virtual migration so that the limitations of local search in the cost reduction can be overcome. To evaluate the algorithms, we compared them with some existing algorithms and an off-line algorithm. Our simulation results showed that the proposed algorithms exhibit better performance in service migration by adapting to the changes of mobile access patterns in a cost-effective way.

IPTV is an emerging TV content delivery service that should guarantee Quality of Service (QoS) to deliver television contents over IP for their customers. However, providing such QoS regarding service level agreements (SLA) requires frequent service monitoring and adaptive configuration mechanisms. Nowadays, Software Defined Networks (SDNs) provide capabilities to deploy and manage networks dynamically and can maintain QoS. In this paper, a novel IPTV service framework (OpenIPTV) is proposed, which utilizes SDN as an underlying technology for providing QoS for IPTV customers in a shared backbone network. OpenIPTV is implemented in a well-known OpenDayLight controller and strictly followed a modular design for the sake of efficiency. OpenIPTV comprises all service requirements such as resource monitoring, channel changing, multicast group managing and dynamic QoS multicast traffic engineering. The performance of OpenIPTV is evaluated under different scenarios and experimental results confirmed the effectiveness of the proposed framework in terms of QoS metrics. Furthermore, experimental results show that OpenIPTV is a feasible and practical solution to deliver IPTV services with high level of QoS over SDN.

Summary Modern software systems are required to dynamically adapt to changing workloads, scenarios, and objectives and to achieve a certain Quality of Service (QoS). Guaranteeing QoS requirements is not trivial, as run-time uncertainty might invalidate the design-time rationale, where software components have been selected by means of off-line analysis. In this work, we propose a QoS-based feedback approach that makes a combined use of design-time predictions and run-time measurements to manage QoS data over time and support software architects while selecting software components that best fit QoS requirements. We illustrate the feasibility and efficacy of the approach on a case study, where the quantitative evaluation shows how the analysis effectively identifies the sources of QoS violations and indicates possible solutions to achieve QoS requirements.

With the fast increase of online services of all kinds, users start to care more about the Quality of Service (QoS) that a service provider can offer besides the functionalities of the services. As a result, QoS-based service selection and recommendation have received significant attention since the mid-2000s. However, existing approaches primarily consider a small number of standard QoS parameters, most of which relate to the response time, fee, availability of services, and so on. As online services start to diversify significantly over different domains, these small set of QoS parameters will not be able to capture the different quality aspects that users truly care about over different domains. Most existing approaches for QoS data collection depend on the information from service providers, which are sensitive to the trustworthiness of the providers. Some service monitoring mechanisms collect QoS data through actual service invocations but may be affected by actual hardware/software configurations. In either case, domain-specific QoS data that capture what users truly care about have not been successfully collected or analyzed by existing works in service computing. To address this demanding issue, we develop a statistical learning approach to extract domain-specific QoS features from user-provided service reviews. In particular, we aim to classify user reviews based on their sentiment orientations into either a positive or negative category. Meanwhile, statistical feature selection is performed to identify statistically nontrivial terms from review text, which can serve as candidate QoS features. We also develop a topic models-based approach that automatically groups relevant terms and returns the term groups to users, where each term group corresponds to one high-level quality aspect of services. We have conducted extensive experiments on three real-world datasets to demonstrates the effectiveness of our approach.

Data center networks (DCNs), which serve as the infrastructural backbone of big data, have been receiving increasing attention recently. To improve the service quality of data centers, researchers have been working on congestion control, network monitoring, and performance optimization. However, most such works focus on user-centric service quality, which means that the quality of network service itself is not factored into the quality of service (QoS) problem. In this study, we illustrate the problem of data center operations and management as a new type of QoS that is the foundation of user-centric QoS implementation. Inspired by traditional works on network performance optimization, we define the quality of network service in a software-defined networking (SDN)-based DCN and develop a framework called Horizon as the architecture of our QoS solution. This framework comprises a Markov-process-based method to predict link popularity, and we use SDN technology to monitor network status. We implement the proposed method, and the experimental results indicate that Horizon can relieve congestion in DCNs to meet QoS requirements. The experimental results show that our approach has a similar performance to the optimal solution. When compared with the ECMP approach, our approach has a much lower latency. The results also show that the proposed approach is effective in terms of network congestion control.

With the fast development of wireless technologies, wireless applications have invaded various areas in people’s lives with a wide range of capabilities. Guaranteeing quality-of-service (QoS) is the key to the success of those applications. One of the QoS requirements, service frequency, is very important for tasks including multimedia transmission in the Internet of Things. A service frequency constraint denotes the length of the time period during which a link can transmit at least once. Unfortunately, it has not been well addressed yet. Therefore, this paper proposes a new framework to schedule multitransmitting flows in wireless networks considering service frequency constraint for each link. In our model, the constraints for flows are heterogeneous due to the diversity of users’ behaviors. We first introduce a new definition for network stability with service frequency constraints and demonstrate that the novel scheduling policy is throughput-optimal in one fundamental category of network models. After that, we discuss the performance of a wireless network with service frequency constraints from the views of capacity region and total queue length. Finally, a series of evaluations indicate the proposed scheduling policy can guarantee service frequency and achieve a good performance on the aspect of queue length of each flow.

QoS-aware web service composition requires multiple simultaneous QoS attributes.Having conflicting QoS properties requires computationally efficient approaches.A comparative experimental study of multi and- many-objective algorithm is presented.Many-objective proposals can promote certain QoS properties while keeping trade-off. Web service based applications often invoke services provided by third-parties in their workflow. The Quality of Service (QoS) provided by the invoked supplier can be expressed in terms of the Service Level Agreement specifying the values contracted for particular aspects like cost or throughput, among others. In this scenario, intelligent systems can support the engineer to scrutinise the service market in order to select those candidates that best fit with the expected composition focusing on different QoS aspects. This search problem, also known as QoS-aware web service composition, is characterised by the presence of many diverse QoS properties to be simultaneously optimised from a multi-objective perspective. Nevertheless, as the number of QoS properties considered during the design phase increases and a larger number of decision factors come into play, it becomes more difficult to find the most suitable candidate solutions, so more sophisticated techniques are required to explore and return diverse, competitive alternatives. With this aim, this paper explores the suitability of many-objective evolutionary algorithms for addressing the binding problem of web services on the basis of a real-world benchmark with 9 QoS properties. A complete comparative study demonstrates that these techniques, never before applied to this problem, can achieve a better trade-off between all the QoS properties, or even promote specific QoS properties while keeping high values for the rest. In addition, this search process can be performed within a reasonable computational cost, enabling its adoption by intelligent and decision-support systems in the field of service oriented computation.