Quality Of Service For Users Research Articles

Social-Aware Collaborative Caching Based on User Preferences for D2D Content Sharing

With rapid growth of content demands, device-to-device (D2D) content sharing is exploited to effectively improve the service quality of users. Considering the limited storage space and various content demands of users, caching schemes are significant. However, most of them ignore the influence of the asynchronous content reuse and the selfishness of users. In this work, the user preferences are defined by exploiting the user-oriented content popularity and the current caching situation, and further, we propose the social-aware rate, which compre-hensively reflects the achievable contents download rate affected by the social ties, the caching indicators, and the user preferences. Guided by this, we model the collaborative caching problem by making a trade-off between the redundancy of caching contents and the cache hit ratio, with the goal of maximizing the sum of social-aware rate over the constraint of limited storage space. Due to its intractability, it is computationally reduced to the maximi-zation of a monotone submodular function, subject to a matroid constraint. Subsequently, two social-aware collaborative caching algorithms are designed by leveraging the standard and continuous greedy algorithms respectively, which are proved to achieve different approxima-tion ratios in unequal polynomial-time. We present the simulation results to illustratethe performance of our schemes.

A Survey of Profit Optimization Techniques for Cloud Providers

As the demand for computing resources grows, cloud computing becomes more and more popular as a pay-as-you-go model, in which the computing resources and services are provided to cloud users efficiently. For cloud providers, the typical goal is to maximize their profits. However, maximizing profits in a highly competitive cloud market is a huge challenge for cloud providers. In this article, a survey of profit optimization techniques is proposed to increase cloud provider profitability through service quality improvement, service pricing, energy consumption reduction, and virtual network function (VNF) deployment. The strategy of improving user service quality is discussed first, followed by the pricing strategy for cloud resources to maximize revenue. Then, this article summarizes the techniques for cloud data centers to reduce server power consumption. Finally, various heuristic algorithms for VNF deployment in the cloud are further described to reduce the cost of cloud providers while maintaining performance. We classify research works based on components of profit and methods used to demonstrate similarities and differences in these studies. We hope this survey will provide researchers with insights into cloud profit optimization techniques.

Interference Management and Duplex Mode Selection in In-Band Full Duplex D2D Communications: A Stochastic Geometry Approach

In this article, we present a new approach for managing the interference on cellular users through optimum mode selection between half-duplex (HD) and in-band full-duplex (IBFD) in such a way that device-to-device (D2D) throughput is maximized, while the quality of service (QoS) of the cellular users is guaranteed in terms of delay. To present a comprehensive view and analyse of the proposed approach in a large network, we use Poisson point process that enables us to model a large network with random parameters in such a way that is highly compatible with reality. Also, to model the cellular users' delay, we use the queuing theory and Markov processes. Unlike other related works, the mode selection between HD and IBFD is considered as a decision variable and its optimal value is obtained. The results show that in comparison with the related works, our proposed approach leads to improvements in the D2D throughput, while through proper interference management, the impact on the cellular users' throughput is negligible. Moreover, the QoS of the cellular users is guaranteed by keeping the delay below a certain threshold.

Tracking area list allocation scheme based on overlapping community algorithm

Reducing the singling overhead for tracking and mobile paging devices is a challenging issue in the study of location management of cellular networks. Cellular networks have become massive generators of data, and in the forthcoming years, this data is expected to increase drastically. Big data-based intelligence and analytics can improve network operational efficiency and user service quality. This work proposes to exploit massive handover and paging data from cellular networks to minimize singling due to user mobility. In this paper, we offer a new holistic tracking area lists (TAL) management methodology, considering group user mobility behavior and paging characteristics. Firstly, a series of graphs showing the evolution of user mobility and traffic is built from handover and paging statistics in the network management system (NMS). Then, the TAL allocation problem is formulated as a classical graph partitioning problem, which is then solved by detecting overlapping communities algorithm based on game theory. Results show that the proposed method can effectively reduce the location management singling overhead and improve the TAL configuration efficiency.

3D Drone Base Station Placement and Resource Allocation With FSO-Based Backhaul in Hotspots

Deploying a Drone Base Station (DBS) over a hotspot area is a promising solution to improve the user Quality of Service (QoS) by helping the Macro Base Station (MBS) transmit traffic to the users. Essentially, the DBS, which works as a relay node between the users and the MBS, can increase the users' data rates by virtue of more likely short-distance Line of Sight (LoS) communication links. Furthermore, deploying DBS is more cost-effective and flexible as compared to deploying small cells. The DBS can employ Free Space Optical (FSO) links for backhauling between the DBS and the MBS. In this paper, we study the 3D deployment and resource allocation of a DBS in a given hotspot area with the objective of maximizing the throughput in the access link under the constraint of user QoS, the capacity of the backhaul link, and total available bandwidth and power. To solve the problem, we first decompose the primal problem into two subproblems, i.e., the 3D DBS placement problem and the resource allocation problem. Second, we propose a cyclic iterative algorithm to solve the two sub-problems separately and use the output of one as the input of the other. The performance of the algorithm is demonstrated via extensive simulations.

A Dynamic Deep-Learning-Based Virtual Edge Node Placement Scheme for Edge Cloud Systems in Mobile Environment

Edge node placement is a key topic to edge cloud systems for that it affects their service performances significantly. Previous solutions based on the existing information are not suitable for the mobile environment due to the mobility and random Internet access of end users. In this article, we propose a dynamic virtual edge node placement scheme, in which the edge node placement strategy is generated based on the prediction information. Our placement scheme applies the pay-as-you-go and Spot Instance model of cloud computing, which may allocate the service resources with low cost conveniently and flexibly. What’s more, Long Short-Term Memory (LSTM) is implemented to predict the information of end users’ requests and the resources’ prices, endowing the generated placement strategy with the adaptability to the change of end users. At last, a set of hierarchical-clustering-based placement algorithms are proposed, which not only locate virtual edge nodes and allocate their corresponding service resources actively, but also guarantee the service quality of end users with low time complexity. The simulation with trace data shows that compared with K-means-clustering-based placement schemes, our virtual edge node placement scheme can provide users with high-quality service in terms of network delay with relatively low placement cost time-efficiently.

Research on the application of spatial partial differential equation in user oriented information mining

In this paper, the application of spatial partial differential equation in user intent oriented information mining is proposed. The hybrid mining algorithm is used to avoid the defects of a single algorithm, which makes the discovery and identification of user intent efficient and practical. The v-svc algorithm can suppress the classification of errors, the hierarchical generation based on attributes can easily get the focus of services, and the fuzzy clustering method can get the service level or service quality of users. This method can be used for any complex object research similar to user intention and the evaluation mechanism of a research object. In view of the problems of traditional methods, this paper puts forward the technology of association mining based on the mathematical model of partial differential classification, and makes an experimental comparative analysis. The results show that the convergence, stability and performance of this method are good.

Recommending Mobile Services with Trustworthy QoS and Dynamic User Preferences via FAHP and Ordinal Utility Function

Due to ubiquitous Internet connectivity, widely available cloud services, and popular mobile devices, mobile networks have become service delivery and consumption platforms for many industries worldwide. To recommend optimal mobile Web services with trustworthy Quality-of-Service (QoS) and dynamic user preferences, this paper proposes a novel service recommendation model based on Fuzzy Analytic Hierarchy Process (FAHP) and ordinal utility function. First, a Multi-QoS vector is defined, and to take into account the trustworthiness of QoS, the fidelity of QoS is modeled as one component of the Multi-QoS vector. Then, a fuzzy hierarchy including dual attributes of QoS (objective attribute and subjective evaluation) is established to fully consider the objective and subjective attributes’ impact on optimal service recommendation. Furthermore, a FAHP-based weighting mode is developed, in which the resolution ratio of weight can be adjusted dynamically by decision-maker according to user preferences. Finally, the optimal service is obtained through the calculation of ordinal utility function of candidate service. Experimental results and method comparison illuminate the feasibility and efficiency of the proposed model.

Load balancing mechanism in the cloud environment using preference alignments and an optimisation algorithm

Cloud computing platforms propose applied directional information technology services to its clients. It provides services according to demands by which shared resources, data, software, and other tools are to be made available based on the client's request. Load balancing is considered as one of the main issues in cloud computing. The quality of service (QoS) factors such as resources application, answering time, process time, scalability, throughput, the durability of the system, and energy consumption are improved by the balanced load. The cloud tasks scheduling and load balancing are considered as an NP-hard issue. Therefore, in this study, a new energy-aware cloud load balancing method is proposed by usage of preference alignments and ant colony optimisation algorithm. This approach aims at balancing the virtual machines workload and energy consumption of cloud data centres and also assures QoS for users. CloudSim simulator is used to confirm the performance of the method. It shows that the proposed method reduces waiting time and energy consumption on a virtual machine.

Multipath transmission control protocol–based multi-access traffic steering solution for 5G multimedia-centric network: Design and testbed system implementation

Various technologies have been developed for the efficient use of the multiple radio access technologies resource at the radio access network level or other network levels to improve user service quality in mobile communication networks. In long-term evolution, mobile carriers are commercializing radio access network-level traffic aggregation technologies such as licensed-assisted access-long-term evolution, long-term evolution-unlicensed, and long-term evolution-wireless local area network aggregation, which use the multi-accesses of the 3rd Generation Partnership Project and WiFi, and the multipath transmission control protocol–based traffic aggregation technologies at the L3 network level. The standardization of 3rd Generation Partnership Project Release 16, which is scheduled to be completed by 2020, is under progress to support the traffic aggregation technology at the L3 network level through a multi-access 5G network. Multipath transmission control protocol is also considered as a traffic aggregation technology. However, it is difficult to apply the multipath transmission control protocol employment model used in long-term evolution to the 5G network structure as it is due to the change to a common core architecture that accommodates multiple radio access technologies through one common interface. Therefore, this article proposes an optimal 5G system architecture and a multipath transmission control protocol adaptation method to support the access traffic steering function based on multipath transmission control protocol in a 3rd Generation Partnership Project 5G mobile communication network. We have verified the development of the multipath transmission control protocol–based multi-access traffic steering technology by implementing the proposed solution in a commercial server on a testbed based on the 5G system standard of 3rd Generation Partnership Project Release 15. Furthermore, this article defines problems that occur when implementing the multipath transmission control protocol–based multi-access traffic steering system and proposes relevant solutions. Based on the implementation results, it is demonstrated that the proposed multipath transmission control protocol–based multi-access traffic steering system can perform traffic steering in the 3rd Generation Partnership Project 5G network.

An intelligent resource dynamic allocation method for UAV wireless mobile network which supports QoS

The development of Unmanned Aerial Vehicle (UAV) business requires that the wireless mobile network can provide the highest possible data rate with limited wireless resources and harsh channel environment, while ensuring the requirements of different quality of service (QoS). The present algorithm only considers one or two spatial sub channels with the maximum eigenvalue, and does not consider how to satisfy multiple requirements with limited resources, nor does it consider the problem of admission control. In order to solve the above problems, this paper proposes an intelligent resource dynamic allocation method based on QoS. In this paper, resources are managed effectively through resource reservation mechanism and selective QoS adjustment mechanism. Then, all non-zero eigenvalue space sub channels are used for data transmission to maximize channel capacity under the condition that the total transmission power is limited and user QoS requirements are met. The simulation results show that the algorithm can effectively improve the throughput of the system by making full use of multi-user diversity on the basis of satisfying the QoS requirements of users.

Analysis of Methods for Determining the Optimal Number of Taxi Cars in Megacities

In modern large cities, taxi fleet is a significant component of the urban transport system. The degree of development and impact of taxi services on quality of transport services for residents and users traveling in the city depends on many factors. But one of the basic ones is the optimal ratio of the number of taxi cars and the demand for transportation by them. The objective of the article was an analytical review of scientific research and practical work in the field of methods for solving the problems of organizing and operating taxi services, in particular, determining the existing population demand for taxi transportation and the required number of taxi cars. For analysis, the research used the method of content analysis of the works of authors of several countries, including Russian ones. The conducted analysis of existing approaches to determining the demand of the population for taxi transportation and to calculating the required number of taxi cars showed that at present there is no single universally recognized method for this. The methods used are based on various methods which comprise statistical, sociological (surveys, observations), mathematical methods. The analysis of big data obtained using information systems of megacities is increasingly being used. It is concluded that there is an objective need for research in the field of developing a universal methodology for determining the required (limit) number of taxi cars in metropolitan areas.

A multi-objective trade-off framework for cloud resource scheduling based on the Deep Q-network algorithm

Cloud computing, now a mature computing service, provides an efficient and economical solution for processing big data. As such, it attracts a lot of attention in academia and plays an important role in industrial applications. With the recent increase in the scale of cloud computing data centers, and improvements in user service quality requirements, the structure of the whole cloud system has become more complex, which has also made the resource scheduling management of these systems more challenging. Thus, the goal of this research was to resolve the conflict between cloud service providers (CSPs) who aim to minimize energy costs and those who seek to optimize service quality. Based on the excellent environmental awareness and online adaptive decision-making ability of deep reinforcement learning (DRL), we proposed an online resource scheduling framework based on the Deep Q-network (DQN) algorithm. The framework could make a trade-off of the two optimization objectives of energy consumption and task makespan by adjusting the proportion of the reward of different optimization objectives. Experimental results showed that this framework could effectively be used to make a trade-off of the two optimization objectives of energy consumption and task makespan, and exhibited obvious optimization effects compared with the baseline algorithm. Therefore, our proposed framework can dynamically adjust the optimization objective of the system according to the different requirements of the cloud system.

Improved Teaching Learning-Based QoS-Aware Services Composition for Internet of Things

The emergence of Internet of Things (IoT) paradigm has led to a continuous increase in the number of services that have similar functionalities but different quality of service (QoS). The IoT services composition, therefore, becomes an important challenge that aims at creating added-value services by combining several services offered by thousands of smart devices in the IoT environment to meet the user's QoS requirements. In this context, an improved teaching learning-based QoS-aware services composition algorithm (ITL-QCA) is proposed to find a very close to the optimal composition in a reasonable amount of time. Unlike the evolutionary computation-based and swarm intelligence-based approaches, the proposed services composition algorithm is characterized by a few tuning parameters and a high exploration capability of the search space of compositions. This allows obtaining compositions with a high optimality in terms of QoS and without requiring some hard tuning parameters. The simulation results show clearly that the ITL-QCA algorithm performs better in terms of the composition optimality and has a reasonable execution time in a large-scale environment compared to other services composition approaches proposed in the literature.

The correlation analysis between the service quality of intelligent library and the behavioral intention of users

PurposeThe purpose of this study was to explore the effect of innovative service mode of intelligent library on improving the service quality and a series of impacts on user behavior. With the rapid development of information technology, internet of things has become an important carrier of people’s “intelligent life”. The emergence of intelligent library will no longer be limited by space; it is affecting people’s lives and work imperceptibly. This new service mode was studied here, and the relationship between the service quality of intelligent library and users’ behavior was analyzed from the perspective of user acceptance and use behavior of intelligent library. Moreover, this study explores how to optimize the service quality to let users accept this technology and service mode and thus realize the original idea.Design/methodology/approachThrough 800 questionnaires issued to the users in the Zhejiang Provincial AI Library, the authors obtained the study data. Among the received questionnaires, 676 copies are valid, and 124 responses are either incomplete or not answered, and so, the efficient rate is 84.5 per cent.FindingsThere is a significantly positive correlation between service innovation and service quality. There is a significantly positive correlation between service quality and behavioral intention. There is a significantly positive correlation between service innovation and behavioral intention.Originality/valueFrom the point of view of innovative service, this paper analyzes the effect of innovative service mode of intelligent library on improving the service quality and a series of impacts on user behavior. This study confirms that intelligent library is a relatively new service innovation. Users’ curiosity and exploration will make them access some relevant information. As a result, a reasonably innovative service is an important factor in users’ acceptance behavior.

Energy-aware caching and collaboration for green communication systems

Social networks and mobile applications tend to enhance the need for high-quality content access. To meet the growing demand for data services in 5G cellular networks, it is important to develop effective content caching and distribution techniques, to reduce redundant data transmission and thereby improve network efficiency significantly. It is anticipated that energy harvesting and self-powered Small Base Stations' (SBS) are the rudimentary constituents of next-generation cellular networks. However, uncertainties in harvested energy are the primary reasons to opt for energy-efficient (EE) power control schemes to reduce SBS energy consumption and ensure the quality of services for users. Using edge collaborative caching, such EE design can also be achievable via the use of the content cache, decreasing the usage of capacity limited SBSs backhaul and reducing energy utilisation. Renewable energy (RE) harvesting technologies can be leveraged to manage the huge power demands of cellular networks. To reduce carbon footprint and improve energy efficiency, we tailored a more practical approach and propose green caching mechanisms for content distribution that utilise the content caching and renewable energy concept. Simulation results and analysis provide key insights that the proposed caching scheme brings a substantial improvement regarding content availability, cache storage capacity at the edge of cellular networks, enhances energy efficiency, and increases cache collaboration time up to 24%. Furthermore, self-powered base stations and energy harvesting are an ultimate part of next-generation wireless networks, particularly in terms of optimum economic sustainability and green energy in developing countries for the evolution of mobile networks.

An Improved Interference Alignment Algorithm With User Mobility Prediction for High-Speed Railway Wireless Communication Networks

The enhancement of the carrying capacity of high-speed railway and the acceleration of train speed lead to the proliferation of signal interference in the communication network, which leads to the degradation of network performance and user service quality. To address the issue, we first classify the cell users in high-speed railway wireless communication environment based on Fuzzy C-Means algorithm and user mobility prediction model, and divide communicating users on trains into cell center users and edge users. Then, differential power distribution schemes are implemented for center users and edge users according to the classification results. Finally, the Max-SINR based interference alignment algorithm is used to realize the interference management. Simulation results show that the proposed algorithm fully takes into account the mobility of high-speed train users and effectively manages interference, which improves the high-speed railway wireless communication network performance significantly.

Green Deployment Method of Micro Base Station for Ultra-Dense Heterogeneous Cellular Networks Based on Constrained Dolphin Swarm Algorithm

This paper proposes a green deployment method for micro base stations for ultra-dense heterogeneous cellular networks to balance network energy efficiency and electromagnetic radiation and meet certain user service quality. Firstly, a constrained multi-objective mathematical model for the green deployment of the micro base station is established for the two-dimensional communication scenario, with the user rate as the constraint, aiming at maximizing the network energy efficiency and minimizing the average electromagnetic radiation. Then, a multi-objective dolphin swarm algorithm which considering the evolutionary advantages of excellent infeasible solutions and feasible solutions, improving the individual search mechanism in the dolphin group algorithm, combined with the improved two-population strategy is proposed and tested on the CTP test set. It shows that compared with the other three methods, the method has certain advantages in convergence and distribution. Finally, a green deployment method for micro base stations based on constrained multi-objective dolphin swarm algorithm is established. Experiments on nine communication scenarios show that the proposed method can balance network energy efficiency and electromagnetic radiation.

Research on Aided Reading System of Digital Library Based on Text Image Features and Edge Computing

With the rapid development of library informatization level, traditional text information retrieval service methods have been difficult to meet the needs of users. Therefore, personalized assisted reading technology attracted wider attention, which can discover hidden associations by mining user information, book information, and user operation logs, and recommend the acquired knowledge to users. In order to improve the service quality and experiences of users, this paper presents a digital library assisted reading system that combines text image features and edge computing. Firstly, the digital library reading assistance system framework is proposed based on edge computing technology, which will improve the service quality and information transmission level of the digital library. And the auxiliary reading model of the digital library is presented based on text image features and RBF neural network. Detailed simulation results verify the applicability and efficiency of the proposed scheme.

Bandwidth, Power and Trajectory Optimization for UAV Base Station Networks With Backhaul and User QoS Constraints

This paper considers an unmanned aerial vehicle (UAV) base station (BS) network with delay-sensitive users and delay-tolerant users on the ground, which have different quality-of-service (QoS) requirements. In the network, the backhaul link connecting the backhaul gateway and the UAV-BS shares the same spectrum with the data links connecting the UAV-BS and the users due to spectrum scarcity. To improve the rate performance of the delay-tolerant users and to guarantee the QoS of the delay-sensitive users, we aim to maximize the minimum rate of the delay-tolerant users by jointly optimizing the bandwidths of the backhaul link and the data links, the transmit power allocated to different users and the trajectory of the UAV-BS, subject to the constraints on UAV mobility, total bandwidth, total transmit power, backhaul data rate, and minimum rate requirements of the delay-sensitive users. Although the formulated problem is non-convex and difficult to solve optimally, we propose an efficient algorithm to find a suboptimal solution to it. Simulation results show that the proposed joint optimization algorithm achieves significantly higher minimum user rate than the benchmark schemes.