Global Quality Of Service Constraints Research Articles

Mobility and energy efficient services composition algorithm with QoS guarantee for large scale Cyber–Physical–Social Systems

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user’s movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user’s mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user’s mobility traces, avoiding the random generation of users’ traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices’ batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider.

Flexible QoS-aware services composition for service computing environments

The rapid development of service computing environments has led to an increase in the number of functionally equivalent services that differ according to their QoS (Quality of Service) attributes values. Accordingly, a challenging issue in the context of services composition is how to select the most appropriate candidate services for each user’s requirements such that the obtained composite service satisfies the user’s non-functional requirements in terms of global QoS constraints. In this paper, a Flexible QoS-aware Services Composition (FQSC) algorithm is proposed to increase the composition feasibility and reduce the composition time, while the composition optimality is maintained as high as possible. The proposed algorithm deals with the limitations of Pareto dominance-based and QoS decomposition-based services composition approaches. Indeed, the number of compositions increases exponentially with the number of services and QoS attributes in the case of Pareto dominance-based approaches, whereas the QoS decomposition-based approaches can lead to a decrease in the composition feasibility when the user’s global QoS constraints are highly restrictive. The simulation results show that the proposed approach is effective compared to other services composition approaches in terms of composition time, optimality and search space.

Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition

As there are more and more available Web services with the same or similar functionalities but different Quality of Service (QoS), the challenge of QoS-aware service composition is to efficiently select appropriate component services to achieve maximum utility and meet the global QoS constraints with low time cost. In this paper, we propose a dynamic service selection approach based on adaptive global QoS constraints decomposition. Fuzzy logic technology and Cultural Genetic Algorithm are used to adaptively decompose global QoS constraints into near-optimal local constraints. According to the near-optimal local constraints, the optimal service is selected for each service class during the running time efficiently. Experimental results show that the proposed approach not only achieves the near-optimal solution, but also significantly reduces the computation time, and has good adaptability and scalability.

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.

QoS-aware optimal and automated semantic web service composition with user’s constraints

Automated semantic web service composition is one of the critical research challenges of service-oriented computing, since it allows users to create an application simply by specifying the inputs that the application requires, the outputs it should produce, and any constraints it should respect. The composition problem has been handled using a variety of techniques, from artificial intelligence planning to optimization algorithms. However no approach so far has focused on handling three composition dimensions simultaneously, producing solutions that are: (1) fully functional (i.e., fully executable) by using a mechanism of semantic matching between the services involved in the solutions, (2) are optimized according to non-functional quality-of-service (QoS) measurements, and (3) respect global QoS constraints. This paper presents a novel approach based on a Harmony Search algorithm that addresses these three dimensions simultaneously through a fitness function, to select the optimal or near-optimal solution in semantic web service composition. In our approach, the search space is modeled as a planning-graph structure which encodes all the possible composition solutions for a given user request. To improve the selection process we have compared the original Harmony Search algorithm with its recently developed variants Improved Harmony Search (IHS) algorithm and Global Best Harmony Search (GHS) algorithm. An experimentation of the approach conducted with an extended version of the Web Service Challenge 2009 dataset showed that: (1) our approach is efficient and effective to extract the optimal or near-optimal composition in diverse scenarios; and (2) both variants IHS and GHS algorithms have brought improvements in terms of fitness and execution time.

Distributed Optimization of Hierarchical Small Cell Networks: A GNEP Framework

Deployment of small cell base stations (SBSs) overlaying the coverage area of a macrocell BS (MBS) results in a two-tier hierarchical small cell network. Cross-tier and inter-tier interference not only jeopardize primary macrocell communication but also limit the spectral efficiency of small cell communication. This paper focuses on distributed interference management for downlink small cell networks. We address the optimization of transmit strategies from both the game theoretical and the network utility maximization (NUM) perspectives and show that they can be unified in a generalized Nash equilibrium problem (GNEP) framework. Specifically, the small cell network design is first formulated as a GNEP, where the SBSs and MBS compete for the spectral resources by maximizing their own rates while satisfying global quality of service (QoS) constraints. We analyze the GNEP via variational inequality theory and propose distributed algorithms, which only require the broadcasting of some pricing information, to achieve a generalized Nash equilibrium (GNE). Then, we also consider a nonconvex NUM problem that aims to maximize the sum rate of all BSs subject to global QoS constraints. We establish the connection between the NUM problem and a penalized GNEP and show that its stationary solution can be obtained via a fixed point iteration of the GNE. We propose GNEP-based distributed algorithms that achieve a stationary solution of the NUM problem at the expense of additional signaling overhead and complexity. The convergence of the proposed algorithms is proved and guaranteed for properly chosen algorithm parameters. The proposed GNEP framework can scale from a QoS constrained game to an NUM design for small cell networks by trading off signaling overhead and complexity.

QoS-Aware Multigranularity Service Composition: Modeling and Optimization

Quality of service (QoS)-aware optimal service composition aims to maximize the overall QoS value of the resulting composite service instance while meeting user-specified global QoS constraints. Traditional methods only consider as candidates service instances that implement one abstract service in the composite service and neglect those that could perform multiple abstract services. To overcome this shortcoming, this paper proposes the concept of generalized component services (GCSs), which is defined in a semantic manner, to expand the selection scope so as to achieve a better solution. A QoS-aware multigranularity service composition model is formulated and how to identify all the GCSs for a composite service is elaborated. A backtracking-based algorithm and an extended genetic algorithm are proposed to optimize the resulting composite service instance. Lastly, evaluation results of these algorithms are described.

Two-stage approach for reliable dynamic Web service composition

Web service composition is a key technology for creating value-added services by integrating available services. With the rapid development of Service Computing, Cloud Computing, Big Data, and the Internet of Things, mass services with the same functionalities and different quality of service (QoS) values are available on the Internet. Moreover, due to the uncertainties of services’ application environment, a service’s QoS is highly dynamic; these two factors cause reliable dynamic Web service composition to be a challenge. To address this issue, this paper proposes a two-stage approach for reliable dynamic Web service composition. In the first stage, the top K Web service composition schemes based on each service’s historical QoS values are selected with the proposed algorithm named Culture Genetic Algorithm (CGA). Then, component services in the top K schemes are filtered out and employed as the candidate services for dynamic service composition. This operation can greatly reduce the number of available services and filter out better candidate services for dynamic service composition. Next, the global QoS constraints are decomposed into local QoS constraints with the CGA algorithm, and the global optimal problem of service composition is transformed into a local optimal service selection problem; this conversion increases the flexibility of dynamic service composition and provides a chance to predict QoS values of services before service selection. In the second stage, before selecting the best service for each task during the running of the service composition workflow, QoS values of each candidate service are predicted based on the improved case-based reasoning, and the best service is selected according to the predicted QoS values. Through QoS prediction, the reliability of the composite Web service can be greatly enhanced. Finally, experimental results show that the proposed method is feasible and effective.

Reliable Web service composition based on QoS dynamic prediction

Web service dynamic composition is a key technology for creating value-added services by composing available services and applications. With the rapid development of web service, cloud computing, big data and internet of things, more and more services with identical functionality and different Quality of Service (QoS) are available; moreover, QoS of Web services are highly dynamic, so, how to create composite Web services reliably and efficiently is still an open issue. For this problem, this paper proposes a reliable Web service composition method based on global QoS constraints decomposition and QoS dynamic prediction. The approach includes two critical phases: firstly, before service composition, global QoS constraints are decomposed into local constraints, and the problem of Web service dynamic composition is transformed to a local optimization problem; secondly, during the running time, optimal Web service is selected for the current abstract service based on predicted QoS values. Experiment results show that our approach can greatly enhance the reliability of composite Web service.

QoS-Aware Dynamic Composition of Web Services Using Numerical Temporal Planning

Web service composition (WSC) is the task of combining a chain of connected single services together to create a more complex and value-added composite service. Quality of service (QoS) has been mostly applied to represent nonfunctional properties of web services and differentiate those with the same functionality. Many research has been done on QoS-aware service composition, as it significantly affects the quality of a composite service. However, existing methods are restricted to predefined workflows, which can incur a couple of limitations, including the lack of guarantee for the optimality on overall QoS and for the completeness of finding a composite service solution. In this paper, instead of predefining a workflow model for service composition, we propose a novel planning-based approach that can automatically convert a QoS-aware composition task to a planning problem with temporal and numerical features. Furthermore, we use state-of-the-art planners, including an existing one and a self-developed one, to handle complex temporal planning problems with logical reasoning and numerical optimization. Our approach can find a composite service graph with the optimal overall QoS value while satisfying multiple global QoS constraints. We implement a prototype system and conduct extensive experiments on large web service repositories. The experimental results show that our proposed approach largely outperforms existing ones in terms of solution quality and is efficient enough for practical deployment.

Web service dynamic composition based on decomposition of global QoS constraints

Virtual enterprise is a temporary network of independent companies or enterprises that can quickly bring together to fulfill a value-added task. With the development of Web service technologies, the enterprise business systems can be encapsulated as Web services. Establishing a virtual enterprise is actually a process of Web service composition. As more and more Web services with the same functionalities and different Quality of Service (QoS) are available, QoS-aware Web service dynamic composition becomes an active research issue. Although several solutions have been provided for this issue, most of these methods based on global optimization, their poor performance render them inappropriate for applications with dynamic and real-time requirements. Moreover, these methods do not consider the commercial agreements and historical contact information between Web services with combination relationship. In this paper, we propose an approach for Web service dynamic composition based on global QoS constraints decomposition. The proposed method consists of three steps: Firstly, global QoS constraints are decomposed into local constraints optimally by a new algorithm named Culture Genetic Algorithm. Secondly, QoS values determination rules are designed to determine QoS values of candidate services. Thirdly, best Web services that can satisfy the local constraints are selected for each task during the running time. Experimental results show that our approach has better performance in solving the problem of QoS-aware Web service dynamic composition.

An improved discrete immune optimization algorithm based on PSO for QoS-driven web service composition

An improved discrete immune optimization algorithm based on particle swarm optimization (IDIPSO) is proposed for Quality of Service (QoS)-driven web service composition with global QoS constraints. A series of effective strategies are presented for this problem, which include an improved local best first strategy based on mathematical analysis for candidate service selection, a perturbing global best strategy along the global best particle. The improved local best first strategy has equivalent effects on the local fitness of a candidate service and the fitness of a composite web service. Empirical comparisons with recently proposed algorithms on various scales of composite web service instances with global QoS constraints indicate that IDIPSO is highly competitive in terms of powerful searching capability, high stability and well trade-off between population diversity and selection pressure, especially when the size of the composite web service problem is large.

Quick convergence of genetic algorithm for QoS-driven web service selection

A novel quickly convergent population diversity handling genetic algorithm (CoDiGA) is presented for web service selection with global Quality-of-Service (QoS) constraints. CoDiGA is characterized by good stability and quick convergence. In CoDiGA, an enhanced initial population policy and an evolution policy are proposed based on population diversity and a relation matrix coding scheme. The integration of the two policies overcomes shortcomings resulting from randomicity of genetic algorithm, such as slow convergence, great variance among the running results, soaring overhead along with increasing size of composition. The simulation results on web service selection with global QoS constraints have shown that prematurity was overcomed effectively, and convergence and stability of genetic algorithm were improved greatly.