Multi-dimensional QoS Research Articles

Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT: A Survey

Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT: A Survey

QoE-Oriented Dependent Task Scheduling Under Multi-Dimensional QoS Constraints Over Distributed Networks

QoE-Oriented Dependent Task Scheduling Under Multi-Dimensional QoS Constraints Over Distributed Networks

Web Service Recommendation via Integrating Heterogeneous Graph Attention Network Representation and FiBiNET Score Prediction

The rapid growth in the number and diversity of Web service, coupled with the myriad of similar Web service in functionality, makes it challenging to find most suitable Web service for users to accelerate and accomplish Mashup development. Therefore, this paper proposes a Web service recommendation method via integrating heterogeneous graph attention network representation and FiBiNET (Feature Importance and Bilinear feature Interaction NETwork) score prediction. In this method, firstly, a heterogeneous information service network is constructed by using composite service information, atomic service information, and their respective attribute information. Secondly, the meta-paths are defined according to different semantic information and service similarity matrixes are built by using commuting matrix and meta-path-based similarity measurement technology. A two-layer attention model is designed to calculate the node level attention and meta-path-level attention of the services respectively, and generate the feature representation of Web service. Thirdly, for the Web services in the service cluster, combining their feature representations with multi-dimensional QoS attributes, the FiBiNET is exploited to dynamically learn the importance of features and complex feature interactions, and predict the score of Web services. Finally, the experiments are performed on the real Web service dataset. The experimental results show that the proposed method is better than the other nine methods in terms of accuracy, recall, F1, and AUC, and achieves better classification and recommendation quality.

Multi-dimensional QoS Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Algorithm

This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load.

QoS-Aware Dynamic MAP Selection in HMIPv6 Architectures

The main problem dealt with in this paper is the creation of a protocol for improved QoS-aware mobility management support in cellular all-IP networks, whereby we propose a new algorithm for QoS-aware mobility management, based on multidimensional QoS metrics. An analytical framework for performance evaluation was presented as well. The proposed algorithm for QoS-aware dynamic MAP selection relies on multidimensional QoS metrics, defined in QoS-preference spaces of the mobile node and QoS-ability spaces of MAP candidates, in the decision-making process. The metric is chosen to achieve the desired QoS level through three parameters: bandwidth, delay, and reliability, while retaining the balance of MAP’s loads in the entire network. For purposes of performance evaluation of the proposed model, we used: algorithm convergence, traffic class distribution by MAP’s, and handover delay. Results showed that the standard deviation for each component of the QoS-ability vector is two orders of magnitude smaller than the deviation in the static MAP selection scenario. We achieved a total handover delay decrease from 20 ms to several hundred milliseconds, by simplifying DAD procedures preserving the simplicity of architecture.

QoS evaluation for web service recommendation

Web service recommendation is one of the most important fields of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown QoS property values and the evaluation of overall QoS according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown QoS property values were predicted by modeling the high-dimensional QoS data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these QoS values. Our method, which considers all QoS dimensions integrally and uniformly, allows us to predict multi-dimensional QoS values accurately and easily. Second, the overall QoS was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users’ ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall QoS. The experimental results showed our proposed methods to be more efficient than existing methods.

Immune optimization of task scheduling on multidimensional QoS constraints

Aiming at the sensitive issues of service quality in cloud computing, a task scheduling tactic with multidimensional QoS constraints is studied. Based on cluster service and user QoS preference, this article constructs an immune optimization model to make a description through formulas and quantify the performance constraints; the utility function of multidimensional QoS is given and then the immune optimization operation is performed with the antibodies. It is beneficial to increase the prediction accuracy of the equality evaluation, and the search for a Pareto optimal set of multiobjective optimization problems is implemented. Finally, the optimum node distribution structure with the highest utility value is obtained. It's shown that the approach gives sufficient consideration of multidimensional user QoS requirements. The results from the test show a significant improvement in average rate of equipment utilization, service time and response time compared to similar algorithms.

Efficiently Compositing and Optimizing the Quality of Heterogeneous Services

The emerging service-oriented computing bring various services together to a ‘market' for clients to request, enabling the integration of services across the distributed, heterogeneous, and wide area networks. By encapsulating diverse implementations of physical and software resources in the interface that standardizes the functions, loosely coupled services have show great potential to support a versatile integration platform for complex applications. Existing techniques can successfully support integrating services from different providers to provide applications with more complex user requirements. However, to automatically composite services with guaranteed QoS is not trivial. In this paper, the authors propose Q-SAC, a QoS optimized service automatic composition model. Two main features of Q-SAC include automatic service composition, and multidimensional QoS optimization for the composition plan. The authors show that the problem of Q-SAC QoS optimization is NP-hard, and design a heuristic simulated annealing algorithm. Through simulation the authors show that this design is practical and efficient.

QoS-aware management of monotonic service orchestrations

We study QoS-aware management of service orchestrations, specifically for orchestrations having a data-dependent workflow. Our study supports multi-dimensional QoS. To capture uncertainty in performance and QoS, we provide support for probabilistic QoS. Under the above assumptions, orchestrations may be non-monotonic with respect to QoS, meaning that strictly improving the QoS of a service may strictly decrease the end-to-end QoS of the orchestration, an embarrassing feature for QoS-aware management. We study monotonicity and provide sufficient conditions for it. We then propose a comprehensive theory and methodology for monotonic orchestrations. Generic QoS composition rules are developed via a QoS Calculus, also capturing best service binding--service discovery, however, is not within the scope of this work. Monotonicity provides the rationale for a contract-based approach to QoS-aware management. Although function and QoS cannot be separated in the design of complex orchestrations, we show that our framework supports separation of concerns by allowing the development of function and QoS separately and then weaving them together to derive the QoS-enhanced orchestration. Our approach is implemented on top of the Orc script language for specifying service orchestrations.

Web Services Selection Based on Mixed Context and Quality of Service Ontology

Intensive efforts are made in the Web services selection literature by these days mainly because of the overwhelming interest of the community in the services provided over the Web. In selecting best services, the non functional properties of such services are proven to be more and more important; these describe the Quality of service that is the key factor in the selection process.In this paper, we discuss a Web service selection based on both the context and the QoS ontology. We propose an architecture that makes an automatic selection of best service provider that is based on mixed context and QoS ontology for a given set of parameters of QoS. The key idea is to rely on multi dimensional QoS. Finally, some experiments are run so to demonstrate consistency and effectiveness of the proposed method.

Multidimensional QOS Oriented Task Scheduling in Grid Environments

High availability of resources is the key requirement in grids where computational and network resources are highly dynamic and unpredictable. QoS (Quality of Service) based resource management and scheduling is an important issue in such dynamic and heterogeneous grid environments. The computational resources in the grid vary from dedicated clusters to non-dedicated desktops which are connected over communication networks of diverse bandwidths. The task-resource mapping in such a non-deterministic computing environment leads to concerns over QoS constraints imposed by application tasks to identify suitable resources. In this paper, we consider bandwidth requirement as a QoS of a network and availability requirement as a QoS of a compute resource. An Availability aware QoS oriented Algorithm (AQuA) for task scheduling in grids has been proposed. The algorithm is evaluated and compared with QGMM (QoS Guided Min-Min) algorithm in the simulated grid environment. The experimental analysis and results show that the AQuA algorithm is able to utilize the highly available resources in grid and therefore increases the reliability to successfully execute applications without adversely affecting the makespan.

Multi-group QoS consensus for web services

QoS has been considered as a significant factor for web service marketing and selection. The interpretation of QoS value from web service consumers and providers would be very different. However, a large group of web service participants with different backgrounds may have difficulties in reaching consensus on the values of multi-dimensional web service QoS, so they may have to be clustered in multi-groups in order to improve effectiveness and efficiency. The similarity of clustered fuzzy QoS dispositions as well as their preference order over these attributes should be analyzed to form a multi-groups consensus framework. A soft multi-groups clustering approach could be adopted to prevent opinions from being excluded unintentionally. The group boundaries and similarity thresholds which are used for clustering and analyzing fuzzy QoS opinions can be moderated dynamically according to the feedback from the internal learning mechanism and the web service consumers. As a result, a model for marketing web services based on multi-group consumers' QoS consensus, the FMG-QCMA (Fuzzy Multi-Groups based QoS Consensus Moderation Approach), is proposed to meet the above requirements. The proposed FMG-QCMA is also evaluated through a case study to demonstrate its effectiveness and efficiency in relation to an existing framework, QCMA (QoS Consensus Moderation Approach).

Multiphase Scalable Grid Scheduler Based on Multi-QoS Using Min-Min Heuristic

In scheduling, the main factor that affects searching speed and mapping performance is the number of resources or the size of search space. In grid computing, the scheduler performance plays an essential role in the overall performance. So, it is obvious the need for scalable scheduler that can manage the growing in resources (i.e. scalable). With the assumption that each resource has its own specifications and each job has its own requirements; then searching the whole search space (all the resources) can waste plenty of scheduling time. In this paper, we propose a two-phase scheduler that uses min-min algorithm to speed up the mapping time with almost the same efficiency. The scheduler is also based on the assumption that the resources in grid computing can be classified into clusters. The scheduler tries first to schedule the jobs to the suitable cluster (i.e. first phase) and then each cluster schedule the incoming jobs to the suitable resources (i.e. second phase). The scheduler is based on multidimensional QoS to enhance the mapping as much as it can. The simulation results show that the use of two-phase strategy can support the scalable scheduler.

A Framework for JRRM with Resource Reservation and Multiservice Provisioning in Heterogeneous Networks

Inter-working and convergence of heterogeneous wireless networks are paving the way to scenarios in which end users will be capable of using simultaneously services through different Radio Access Technologies (RATs), by means of reconfigurable mobile terminals and different network elements. In order to exploit the potential of these heterogeneous networks scenarios, optimal RAT selection and resource utilization mechanisms are required. As a result, the heterogeneous networks are introducing a new dimension to the Radio Resource Management (RRM) problem, so that new algorithms dealing with the dissimilarities and complementarities of the multiple RATs from a joint perspective have to be considered. In this sense, this paper proposes a Joint Radio Resource Management (JRRM) strategy in a multi-RAT, multicellular and multiservice scenario. An approach based on Fuzzy Neural methodology is presented. Firstly, the way how the proposed Fuzzy Neural framework deals with the multiservice allocation in a heterogeneous scenario is presented. A reinforcement learning algorithm based on neural networks allows guaranteeing a multidimensional QoS focusing on those QoS requirements which mainly affect the user perception of the service. In addition to this, the performances obtained by the Fuzzy Neural JRRM for both real-time and non real-time services, are compared to the ones offered by alternative JRRM strategies. Secondly, special attention is paid to real-time services and to mechanisms to improve their performances. An approach based on predicting future JRRM decisions and on accordingly reserving radio resources for handoff calls is presented. Simulation results will show improvements in terms of both new connection blocking and handoff call dropping probabilities. Finally, the full set of results provides the sufficient insight into the problem to allow stating that the present Fuzzy Neural framework can be a firm candidate for JRRM.

A flexible multi-dimensional QoS performance measure framework for distributed heterogeneous systems

When users' tasks in a distributed heterogeneous computing environment (e.g., cluster of heterogeneous computers) are allocated resources, the total demand placed on some system resources by the tasks, for a given interval of time, may exceed the availability of those resources. In such a case, some tasks may receive degraded service or be dropped from the system. One part of a measure to quantify the success of a resource management system (RMS) in such a distributed environment is the collective value of the tasks completed during an interval of time, as perceived by the user, application, or policy maker. The Flexible Integrated System Capability (FISC) measure presented here is a measure for quantifying this collective value. The FISC measure is a flexible multi-dimensional measure such that any task attribute can be inserted and may include priorities, versions of a task or data, deadlines, situational mode, security, application- and domain-specific QoS, and task dependencies. For an environment where it is important to investigate how well data communication requests are satisfied, the data communication request satisfied can be the basis of the FISC measure instead of tasks completed. The motivation behind the FISC measure is to determine the performance of resource management schemes if tasks have multiple attributes that needs to be satisfied. The goal of this measure is to compare the results of different resource management heuristics that are trying to achieve the same performance objective but with different approaches.

Precomputation for intra-domain QoS routing

Quality-of-service routing (QoSR), seeking to find a feasible path with multiple constraints, is an NP-complete problem. We propose a novel precomputation approach to multi-constrained intra-domain QoS routing (PMCP). It is assumed that a router maintains the link state information of the entire domain. PMCP cares each QoS weight to several degrees, and computes a number of QoS coefficients uniformly distributed in the multi-dimensional QoS metric space. Based on each coefficient, a linear QoS function is constructed to convert the multiple QoS metrics to a single QoS value. We then create a shortest path tree with respect to the QoS value by Dijkstra’s algorithm. Finally, according to the multiple coefficients, different shortest path trees are calculated to compose the QoS routing table. We analyze linear QoS functions in the QoS metric space, and give a mathematical model to determine the feasibility of a QoS request in the space. After PMCP is introduced, we analyze its computational complexity and present a method of QoS routing table lookup. Extensive simulations evaluate the performance of the proposed algorithm and present a comparative study.

Modelling multi‐dimensional QoS: some fundamental constraints

AbstractIn this paper, we model multi‐dimensional QoS in a unified framework, and study some fundamental constraints from the network and the traffic on realizing multiple QoS goals. Multi‐dimensional QoS requirements are quantitatively represented using a QoS region. Based on the theory of effective bandwidths, the framework connects the throughput, the delay, and the loss rate in a uniform formula. Important traffic and network factors, namely, the burst size and the link speed, are involved. With this framework, it is found that the burst size sets hard limit on the QoS region that can be achieved, and that the matching between the link speed and the node processing power can greatly improve the limit. It is also made clear that while pure load imbalance among links does not affect the QoS region, the heterogeneities of burst size or link speed may severely degrade the QoS performance. Applying the theory to real‐time services in differentiated services architecture, we show it provides a useful tool for QoS prediction and network dimensioning. Copyright © 2004 John Wiley & Sons, Ltd.