Quality Of Service Preferences Research Articles

Research of 5G HUDN network selection algorithm based on Dueling-DDQN

Due to the dense deployment and the diversity of user service types in the 5G HUDN environment, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user’s quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning. Firstly, the state, action space and reward function of the user-selected network are designed. Then, by calculating the network selection benefits for different types of services initiated by users, the analytic hierarchy process is used to establish the weight relationship between the different user services and the network attributes. Finally, a deep Q neural network is used to solve and optimize the proposed target and obtain the user’s best network selection strategy and long-term network selection benefits. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times.

CollOR: Distributed collaborative offloading and routing for tasks with QoS demands in multi-robot system

Multi-access edge computing (MEC) offers prospective opportunities for robots that have various computational tasks to execute in Industry 4.0, smart cities and many other fields. Computational offloading, as the key of this emerging paradigm, can leverage resourceful edge infrastructure nearby to provide computational support for these resource-limited robots. However, existing computational offloading schemes not only rarely consider that robots are geo-distributed, but also ignore the diversified quality of service (QoS) of robotic applications. Thus in this paper, the collaborative computational offloading problem for multi-robot system in MEC is studied, and a distributed scheme is proposed to jointly optimize offloading and routing for computational tasks of robots. First, we introduce the collaborative communication model of robots, construct the multi-criteria utility function related to both the QoS attributes of applications and the energy efficiency of robot, and formulate the joint offloading and routing problem. Afterwards, the QoS preference analysis mechanism is established to characterize the heterogeneous requirements of robotic applications. Next, by designing a novel collaborative robotics framework, we realize the distributed processing and adaptive strategy-making of multi-robots, and integrate the game-theoretic approach to propose a utility-aware collaborative computational offloading algorithm. Finally, simulation results validate that the proposed algorithm has significant performance improvement over the representative algorithms in different evaluation metrics, and can achieve stable and scalable performance in the scenario of the increasing demand for QoS and the growing scale of robots.

QoS‐aware web service recommendation via exploring the users' personalized diversity preferences

AbstractWith the popularity and wide adoption of SOA (service‐oriented architecture), a massive amount of Web services emerge on the Internet. It is difficult for users to find the desired services from a large number of services. Thus, service recommendation becomes an effective means to improve the efficiency of using service. Considering that the users' QoS (quality of service) preferences are often unknown or uncertain, the recent QoS‐aware service recommendation methods recommend QoS‐diversified services for users to increase the probability of fulfillment of the service list with a limited number of services on users' potential QoS preferences. However, the existing QoS‐diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this article proposes a service diversity adjustment algorithm, which selects more diversified services outside of the original service recommendation list to replace the services in the present recommendation list to approximate the QoS diversity preference of the active user. In this way, the probability of meeting the user's potential QoS preference requirements is improved. Comprehensive experimental results show that the proposed approach can not only provide personalized and diversified services but also ensure the overall accuracy of the recommendation results.

QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results.

Quality of experience aware network selection model for service provisioning in heterogeneous network

Heterogeneous wireless networks (HWNs) are capable of integrating the different radio access technologies that make it possible to connect mobile users based on the performance parameters. Further quality of service (QoS) is one of the major topics for HWNs, moreover existing radio access technology (RAT) methodology are designed to provide network QoS criteria. However, limited work has been carried out for the RAT selection mechanism considering user QoS preference and existing models are developed based on the multi-mode terminal under a given minimal density network. For overcoming research issues this paper present quality of experience (QoE) RAT (QOE-RAT) selection methodology, incorporating both network performance criteria and user preference considering multiple call and multi-mode HWNs environment. First, this paper presents fuzzy preference aware weight (FPAW) and multi-mode terminal preference aware TOPSIS (MMTPA-TOPSIS) for choosing the best RAT for gaining multi-services. Experiment outcomes show the QOE-RAT selection method achieves much superior packet transmission outcomes when compared with state-of-art Rat selection methodologies.

Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

With the development of the mobile Internet of Things, people have higher and higher requirements for services, and how to provide high-quality services that meet the needs of users has also become a hot issue of current research. This article is also in the context of the mobile Internet of Things, researching the solution to the service matching problem. In order to provide high-quality grid services, the grid system needs the support of quality of service (QoS). Based on the existing QoS matching strategy, a service matching strategy based on dynamic grid QoS is proposed. This paper combines semantic information, considers service quality QoS and user preferences, and improves the success rate of service matching through hierarchical matching, so as to provide users with high-quality services. At the same time, the classic Max-Min algorithm in grid task scheduling is analysed, and on this basis, the impact of QoS on task scheduling is fully considered, and combined with the concept of execution cost, a grid task based on QoS constraints (MOS) is proposed. Through simulation verification and analysis, the strategy in this paper not only achieved higher matching performance, but also improved the success rate of task execution, and the effect of system load balancing was also significantly improved.

Performance Analysis of a Clustering Model for QoS-Aware Service Recommendation

The numbers of web services are growing rapidly in recent years. One of the most challenging issues in service computing is the personalized recommendation of Web services. Most of the current research recommends services based on Quality of Service (QoS)-aware data with few considerations of service-side factors, such as service functions. In this paper, a new QoS-aware Web service recommendation model based on user and service clustering (RMUSC) is proposed to gain an advance in recommended accuracy. Firstly, similar users are clustered together by a Top-N similarity algorithm through the user QoS records. Secondly, a K-means++ based filtering service cluster is established. Finally, a user and services collaborative scheme is exploited and obtains potential user QoS preferences to generate recommendations. The experimental results show that when the density of the service invocation matrix is 5%, 10% and 20%. the average absolute error (MAE) and root mean square error (RMSE) of RMUSC are lower than those of other methods.

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.

Pricing the cloud: a QoS-based auction approach

ABSTRACTCloud computing is an evolutionary technology that offers on-demand resources and elastic services through the Internet. Most providers adopt fixed-price mechanisms (e.g. pay-as-you-go). However, a few providers have recently employed auction-like approaches to price cloud services. Meanwhile, cloud consumers pay more attention to Quality of Service (QoS) such as availability, which measures how well a service is performed. This paper proposes a novel auction approach that can efficiently allocate resources according to customers’ QoS preferences. The QoS-based pricing can generate more revenue than a fixed-price strategy. This research lies at the intersection of cloud computing, economics, and information systems.

Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.

Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.

An analytical solution based on mobility and multicriteria optimization for access selection in heterogeneous environment

Providing global connectivity with high speed and guaranteed quality at any place and any time is now becoming a reality due to the integration and co-ordination of different radio access technologies. The internetworking of existing networks with diverse characteristics has been considered attractive to meet the incredible development of interactive multimedia services and ever-growing demands of mobile users. Due to the diverse characteristics of heterogeneous networks, several challenges have to be addressed in terms of quality of service (QoS), mobility management and user preferences. To achieve this goal, an optimal network selection algorithm is needed to select the target network for maximizing the end user satisfaction. The existing works do not consider the integration of utility function with mobile terminal mobility characteristics to minimize ping-pong effects in the integrated networks. An integrated multicriteria network selection algorithm based on multiplicative utility function and residual residence time (RRT) estimation is proposed to keep the mobile users always best connected. Multiplicative weighted utility function considers network conditions, application QoS and user preferences to evaluate the available networks. In this paper, the proposed scheme is implemented with two mainstreams (pedestrian users and high-velocity users). For high-velocity users, RRT and adaptive residence time threshold are also considered to keep the probability of handover failures and unnecessary handovers within the limits. Monte-Carlo simulation results demonstrate that the proposed scheme outperforms against existing approaches.

Agent-based fuzzy constraint-directed negotiation for service level agreements in cloud computing

Establishing a service level agreement (SLA) between a cloud provider and a cloud consumer is becoming increasingly critical: consumers expect a specified quality of service (QoS) for their cloud applications, and providers must be able to guarantee that the agreed-upon QoS will be maintained. These concepts require the SLA negotiation to be performed in a manner whereby a provider and a consumer can effectively bargain on various QoS preferences, such as price, response time and service level. This paper presents a novel agent-based fuzzy constraint-directed negotiation (AFCN) model for SLA negotiation. It provides a framework for integrating time, resource (market) and behavioral factors into the decision making process for service level agreements and cloud load balancing. The proposed AFCN model supports an iterative many-to-many bargaining negotiation infrastructure that is a fully distributed and autonomous approach and that does not require a broker to coordinate the negotiation process. The novelty of the proposed model is that it uses the concept of a fuzzy membership function to represent imprecise QoS preferences. This added information sharing is critical for the effectiveness of distributed coordination. It can not only speed up the convergence but also enforce global consistency through iterative exchanges of offers and counter-offers with limited information sharing and without privacy breaches. To consider the behavior of different agents, the AFCN model can also flexibly adopt different negotiation strategies such as the competitive, win-win, and collaborative strategies in different cloud computing environments. The experimental results demonstrate that the proposed model consistently outperforms other agent-based SLA negotiation models in terms of the degree of satisfaction, the ratio of successful negotiation, the buying price of the consumer agent (CA), the revenue of the provider agent (PA), and the convergence speed. Consequently, the proposed AFCN is both flexible and useful for fully distributed SLA negotiations.

How High Should We Go? Determining Reservation Values to Negotiate Successfully for Composite Software Services

In the paradigm of service-centric computing, new value-added applications can be developed dynamically and flexibly by combining and integrating existing services. While software applications are traditionally specified and implemented as a set of functions uniform to all users, this new paradigm allows the same software service to be delivered with a different price, response time, availability, and other nonfunctional attributes to accommodate different modes of use. These nonfunctional attributes together are referred to as Quality of Service (QoS). When creating a new composite service, negotiation makes it possible for a service provider to offer the service with the QoS properties customized to the needs of a user. Automated negotiation tactics require the specification of reservation values for the QoS attributes. We present a methodology that determines the reservation values a user (or broker) should use for each component service based on the user’s minimum requirements for the composite service. Our methodology maximizes the chance of reaching a successful negotiation outcome while staying within the user’s reservation values for the composite service. We show that the problem of determining the user’s reservation values for component services can be modeled as a multiobjective optimization problem and then transformed to a single-objective optimization problem using a max-min approach. The formulation can incorporate providers' different QoS preferences to increase the chance of negotiation success. We identify problem instances for which closed-form solutions can be found for the reservation values. We show how the method of setting reservation values can be incorporated into a negotiation process that uses extant concession and trade-off tactics. Simulation experiments demonstrate the effectiveness of the proposed approach. If some providers accept offers before the negotiation process deadline, we show that dynamically changing the reservation values for the remaining providers makes the overall negotiation process more likely to succeed. The online appendix is available at https://doi.org/10.1287/isre.2016.0678 .

PHAT: A Preference and Honesty Aware Trust Model for Web Services

Trust is one of the most critical factors for a service requestor when selecting a service from a large pool of candidate services. However, existing web service trust models either do not consider users' preferences for different quality of service (QoS) attributes, or ignore the impact of vicious ratings on trust evaluation. To address these gaps, PHAT, a dynamic trust evaluation model with dual consideration of users' preferences and false ratings, is proposed in this paper. The model introduces an approach to automatically mine users' preferences from their requirements. The preferences are then used to determine the weight of each QoS attribute when integrating trust into multi-dimensional QoS attributes. The “local” trust on a service is derived by combining the trust on QoS attributes and the user's subjective ratings. Then, the users are divided into different groups according to their QoS preferences, and the honesty of each group is assessed by filtering out dishonest users based on a hybrid approach combining rating consistency clustering with an average method. Finally, the weight on ratings is dynamically adjusted according to the results of honesty assessment when calculating the global trustworthiness, namely, the reputation of a service. The proposed model is evaluated with real-world QoS data, and the results indicate that PHAT works well on personalized evaluation of trust, and can effectively dilute the influence of malicious ratings.

Intuitionistic Fuzzy Set Based Task Scheduling with QoS Preference Awareness

Existing task scheduling algorithms in cloud computing lack the ability to be aware of users' quality of service(QoS) preference. In order to address this problem, intuitionistic fuzzy analysis is introduced to determine users' QoS preference. In addition, the optimal sequence decision helps experts use their professional knowledge to decide the weights of QoS classes. Using these methods, we propose the Intuitionistic Fuzzy Set Based Task Scheduling with QoS Preference Awareness: IFS-QoS PA algorithm. By considering both user and expert experience, the method can determine users' QoS preference and reflect the characteristic of cloud storage system. The simulation results show that this method offers acceptable user satisfaction rate, has low computation complexity and more suitable for large scale task scheduling as compare to particle swarm optimization based ones.

A broker-based quality evaluation system for service selection according to the QoS preferences of users

The quality offered by web services is becoming the utmost priority and various studies to search and select web services according to their Quality of Service (QoS) as well as functionality have been proposed. However, most studies have applied each attribute independently as a constraint to select a web service. Each attribute has a different priority weight that represents the degree of influence on the overall quality and the priority weight can be changed depending on the purpose of the quality evaluation or the subjectivity of the user.Here, we propose a system that calculates the priority weights for each quality attribute according to the QoS preference of a user easily and consistently and reflects the weights in selecting the web service.To calculate the weights, we apply a pairwise comparison matrix and an eigenvector of the matrix. Through the proposed algorithms and system, users can easily perform the process calculating the weights and select the web services according to their QoS preference. We implement the broker-based quality evaluation system and the simulation results show that the weights of the quality attributes and the quality score ranking of the web services are changed according to users’ preferences.

A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database

The architecture of cognitive decision engine should enable fast decision making, long-term knowledge accumulating based on past operating experience, and capabilities of knowledge updating to adapt to new situations. In this paper, a hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database is proposed. Considering the user’s quality of service preferences and the wireless situations, how to determine the radio’s link parameters such as modulation type, symbol rate, and transmit power can be formulated as a multi-objective optimization problem. In the architecture, this problem is solved by using particle swarm optimization algorithms, which make cognitive radio have the fast decision-making ability when facing unknown wireless situations. The case database, which stores the past running experiences of the cognitive radio is also integrated into the proposed architecture to improve the radio’s response speed and endows the radio with the ability of learning from its previous operating experiences. Simulation results show the effectiveness of the architecture, and the proposed cognitive decision engine can dynamically and properly reconfigure the radio according to the changes in wireless environment.

QoS-aware SaaS Services Selection with Interval Numbers for Group User

The integration of SaaS service for group user in cloud is challenging, notably because the service's QoS and member's personalization QoS preference in group are uncertain. This is a timely and importance problem with the advent of the SaaS model of service delivery. Therefore, before the SaaS service been utilized, the service alternatives must be ranked for group user based on services' QoS and QoS preference expressed by interval numbers. So, QoS- aware SaaS services selection with interval numbers for group user (QSSSIN_GU) is proposed, in order to identify the pros and cons of alternatives. This approach can obtain the group optimal service when the member's QoS preference in group is personalized and the QoS of service alternatives expressed by interval numbers. Finally, four experiments are given to demonstrate the benefits and effectiveness of QoS-aware SaaS services selection with interval numbers for group user. That is a feasible and supplementary manner in selecting the SaaS services for group user with interval numbers. Index Terms—software as a service (SaaS), cloud service, service selection, quality of service (QoS), technique for order preference by similarity to an ideal solution (TOPSIS), group user

Towards optimal discovery of web services for multiple QoS constraints and preferences

Web service discovery (WSD) is the task of matchmaking a set of relevant web services. Quality of service (QoS) has recently been applied to represent non-functional properties of web services. Therefore, when those services provide the same functionality but have different QoS values, how to effectively filter out the services that cannot satisfy the QoS constraints and rank the remaining services is still an open research issue. In this paper, we propose an integrated approach that allows a service requester to specify a functionality request, multiple QoS constraints and their preferences, and our method discovers a set of the most appropriate ranked services with QoS utility aggregation. By conducting empirical experiments on simulated web services, we validate the feasibility of our service discovery approach. The running example shows that, our approach can find more appropriate services by the satisfiability of multiple QoS constraints and the ranking of aggregated QoS.