QoS-aware Service Composition Research Articles

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

Cloud manufacturing (CMfg) has drawn extensive attentions from industrial community and academia. Quality of service (QoS)-aware service composition is critical to the on-demand use of distributed manufacturing resources and capabilities in CMfg systems. However, most previous work plainly composed composite services by the approach of one-to-one mapping-based service composition (OOM-SC), which leads to drawbacks to both the overall QoS of composite services and the success rate of service composition. To circumvent this, an approach of synergistic elementary service group-based service composition (SESG-SC) is proposed in this paper. It releases the assumption of one-to-one mapping between elementary services and subtasks, allowing a free combination of multiple functionally equivalent elementary services into a synergistic elementary service group (SESG) to perform each subtask collectively, thereby bettering the overall QoS and achieving more acceptable success rate. To introduce an optimal construction of SESGs into the optimization model of QoS-aware service composition, three kinds of redundant structures within SESGs are discussed and the corresponding QoS evaluation formulas are also proposed. To deal with the increasing computing complexity of the optimization model, an algorithm named matrix-coded genetic algorithm with collaboratively evolutional populations (MCGA-CEP) is designed in the current study. The experimental results indicate that the proposed SESG-SC approach significantly outperforms the previous approaches, and the proposed MCGA-CEP is sound on performance-wise.

QoS-aware service composition with user preferences and multiple constraints

With the growing application of services on the basis of physical networks and Internet of Things devices, service composition based on quality of service (QoS) has attracted wide attention. However, QoS-aware service composition with multiple QoS constraints is time consuming work. Given the evident user preference for QoS, we propose a service composition framework and a QoS-aware method incorporating local and global optimization models. Candidate services are preprocessed and assigned different priority levels based on local optimization. The priority weight of quality attributes is obtained by a fuzzy analytic hierarchy process and the approximate optimal composite service is derived by global optimization for multiple QoS constraints. Simulations verify the proposed method outperforms traditional integer programming (IP) and genetic algorithm, particularly for large scale datasets, providing near optimal solutions with significantly less time cost.

Service composition with quality of service management in environmental sensor networks

Environmental sensor networks consist of geographically distributed resource-constrained computers that use sensors to measure environmental parameters. They typically collect and distribute environmental data in near real-time using different types of web services. These services are consumed by users and applications that expect appropriate quality of service QoS levels. However, acceptable and stable QoS levels are difficult to maintain in environmental sensor networks due to limited computing resources and hostile deployment conditions. We developed a new approach to efficiently manage QoS levels in these networks using two separate tasks: online QoS estimation and QoS-aware service composition. This paper presents our first prototype of a web service composition system that leverages Bayesian classification techniques to perform online QoS estimation by constantly monitoring key environmental and network performance parameters. This system has been tested in a live noise pollution environmental sensor network deployed in the Flanders region in Belgium.

Design and analysis of the artificial bee colony algorithms for domain-oriented QoS-aware service composition

Recently, various evolutionary algorithms have been successfully applied to acquire approximately optimal solutions for QoS-aware service composition problems. Especially, artificial bee colony algorithm (ABC) stands out due to its advantages of few parameters, strong robustness and search capability. In addition, for these algorithms, domain features are widely utilised as heuristic. However, how to combine these two points to achieve more optimal solutions is becoming a challenge. To address this critical challenge, this paper summarises three domain features (priori, similarity and correlation) and proposes the artificial bee colony algorithms for domain-oriented service composition named S-ABCsc paradigm. Besides, the framework of the paradigm is presented, and its seven configurable points are identified. Moreover, two types of algorithms are derived from the paradigm. To apply the paradigm, a support tool is realised, which helps users obtain a concrete algorithm. Furthermore, several comparison experiments are conducted, which have proved the effectiveness of the paradigm.

Design and analysis of the artificial bee colony algorithms for domain-oriented QoS-aware service composition

Recently, various evolutionary algorithms have been successfully applied to acquire approximately optimal solutions for QoS-aware service composition problems. Especially, artificial bee colony algorithm (ABC) stands out due to its advantages of few parameters, strong robustness and search capability. In addition, for these algorithms, domain features are widely utilised as heuristic. However, how to combine these two points to achieve more optimal solutions is becoming a challenge. To address this critical challenge, this paper summarises three domain features (priori, similarity and correlation) and proposes the artificial bee colony algorithms for domain-oriented service composition named S-ABCsc paradigm. Besides, the framework of the paradigm is presented, and its seven configurable points are identified. Moreover, two types of algorithms are derived from the paradigm. To apply the paradigm, a support tool is realised, which helps users obtain a concrete algorithm. Furthermore, several comparison experiments are conducted, which have proved the effectiveness of the paradigm.

A formal method to model and analyse QoS-aware fault tolerant service composition

In this paper, we address the fault tolerant service composition with particular attention to QoS. A fault tolerant strategy for improving the performance of service composition is proposed. The strategy is composed of invocation mechanism, synchronisation mechanism and exception mechanism. Petri nets are used to observe the behaviours of basic components, and to describe their interrelationship. The transaction attributes, reliability, and time of service are also articulated. The composition mechanism systematically integrates these schemas into a fault tolerant model. Based on this, the analysis technology and its enforcement method are proposed, which can guarantee the correct behaviour of service composition while meeting the required reliability. The related theories of Petri nets help prove the correctness of the strategy. Finally, the method is realised by a simplified travel service and is tested by several experiments.

Optimising web service composition based on differential fruit fly optimisation algorithm

With the emergence of a large number of web services with similar functional but different non-functional attributes, how to select appropriate web services from massive candidate services and assemble them into service composition which can complete the complex value-added business process is of great concern. The problem of QoS-aware service composition belongs to the multi-objective decision optimisation problem. Its goal is to optimise QoS of the entire service composition. To solve this problem, this paper presents a differential fruit fly optimisation algorithm DFOA. Firstly, make fast global optimisation through FOA. Then, perform the local search in the neighbourhoods of global optimisation solution based on fruit fly swarm differential mutation and crossover operation. Finally, update the global optimal solution according to the two optimisation results, thereby obtaining the optimal service composition. The experiment verifies the feasibility of the algorithm.

An interval-based fuzzy ranking approach for QoS uncertainty-aware service composition

Web services are a major technique for building loosely coupled distributed systems. QoS-aware service composition concerns the selection of candidate services while maximizing the overall Quality of Service (QoS). However, these approaches fail to take the uncertainty of QoS into account. The resultant QoS of composite service observed by users is therefore unstable and subject to risks of failing composition by end users. In this paper, we propose a QoS Uncertainty-aware Web Service Composition approach based on a novel QoS interval model. It performs evaluation under a trade-off between profit and stability of QoS in a fuzzy way, and selects out composite service among the numerous solutions with an overall more stable QoS. A Multi-criteria Decision Analysis (MCDA) method and improved Genetic Algorithm is introduced in this paper. Based on real-world dataset QWS, simulations validate the correctness of the interval model and demonstrate the performance of composite framework. A comparative study with traditional tentative model is also discussed briefly.

BNQM: A Bayesian Network based QoS Model for Grid service composition

The QoS attributes of Grid services play important roles in several tasks in Grid computing such as QoS-aware service composition, service negotiation, resource management, service discovery and scheduling. By considering the dynamic aspects of the Grid environments and also the uncertainty related to Grid services, in this paper, we present BNQM, a Bayesian network based probabilistic QoS Model for Grid service composition. Application of Bayesian network in QoS management makes it possible to indicate the conditional independence relationships among QoS attributes and to provide an effective probabilistic approach to predict new values for some QoS attributes while others are changed. Furthermore, we propose a framework for QoS-aware Grid service composition algorithms to use BNQM. This framework enables the QoS-aware Grid service composition algorithms to use up-to-date QoS values in the composition process. Several experiments conducted using the proposed framework and the achieved results indicate that BNQM is efficient in predicting the QoS values. Also, experiments reveal that using BNQM allows the QoS-aware Grid service composition approaches to use more precise and accurate QoS values, resulting in more precise composite Grid services from the QoS points of view.

A Partial Selection Methodology for Efficient QoS-Aware Service Composition

As web service has become a popular way for engineering software on the Internet, quality of service (QoS) which describes non-functional characteristics of web services is often employed in service composition. Since QoS is an aggregated concept consisting of several attributes, service composition on enormous candidate sets is a challenging multi-objective optimization problem. In this paper, we study the problem from a general Pareto optimal angle, seeking to reduce search space in service composition. Pareto set model for QoS-aware service composition is introduced, and its relationship with the widely used utility function model is theoretically studied, which proves the applicability of our model. QoS attributes are systematically studied according to their different types of aggregation patterns in service composition, and QoS-based dominance relationships between candidates and between workflows are defined. Taking advantage of pruning candidates by dominance relationships and constraint validations at candidate level, a service composition algorithm using partial selection techniques is proposed. Furthermore, a parallel approach is designed, which is able to significantly reduce search space and achieve great performance gains. A careful analysis of the optimality of our approach is provided, and its efficacy is further validated by both simulation experiments and real-world data based evaluations.

Aggregated Quantified Response Time Matrix Formulation (ARMF) - A New Quality of Service Paradigm Technique

Workflow management system, a technique that manages different types of processes/jobs gathered as a workflow, ensures that each task in the workflow is accomplished to proceed further. When the process happens between two parties, the client and the server, a binding agreement or contract is defined specifying the Quality of Service (QoS) metrics such as products/services to be delivered, the deadlines and the cost of services. The goal of QoS is to select the appropriate web services for the customers achieve their goals. The existing service applications pretend to be a self-contained unit of functionalities that requests and retrieve a qualified service using service oriented architecture. But they face difficulties in identifying the right quality of a service based on functionality. Hence the major factor in QoS specification is QoS modeling, which involves the task of identifying the factors suitable for a functional requirement. QoS-aware service composition significantly affects the quality of a composite service and hence enormous research has been performed in this area. However, existing methods are restricted to predefined workflows, which can sustain the limitations. The limitations include the lack of guarantee for the optimality on overall QoS, satisfaction of customers and the completeness of finding a composite service solution. The proposed approach uses an Aggregated Quantified Response time Matrix Formulation (ARMF) methodology. The ARMF predicts the unknown QoS values by implementing two algorithms, the Aggregated Quantified Matrix Formulation (AQMF) Technique and the Optimal Incremental Multi-Attributes property Filtering (OIMF). It provides the user-response matrix where every entry in the matrix depicts the QoS value of the service opted by the user. The experimental results show that the proposed approach largely outperforms the existing techniques in terms of quality and efficiency required for the customer services.

Broker-based SLA-aware composite service provisioning

QoS-aware service composition aims to satisfy users’ quality of services (QoS) needs during service composition. Traditional methods simply attempt to maximize user satisfaction by provisioning the composite service instance with the best QoS. These “best-effort” methods fail to take into account that there also exist other consumers competing for the service resources and their decisions of service selection/composition can impact on QoS. Since user's QoS needs can be met once the demanded level is reached, in this paper, we propose an “on-demand” strategy for QoS-aware service composition to replace the traditional “best-effort” strategy. The service broker is introduced to facilitate implementation of this strategy: it first purchases a number of service instances for each component from providers and then provisions the composite services with different QoS classes to consumers. This paper focuses on how the broker follows the service level agreement (SLA) to provision composite services in the “on-demand” manner. This problem is formally expressed as the minimization of the QoS distance function between SLA and QoS of composite service instances, under a series of constraints. Heuristic approaches are proposed for the problem and experiments are conducted at last to verify their effectiveness and efficiency.

QoS aware descriptions for RESTful service composition: security domain

Current research on QoS aware service composition focuses on a WSDL/RPC service paradigm, characterized by a centralized, synchronous, and stateful approach. In this paper, we explore QoS aware RESTful services composition, which is characterized by a decentralized, stateless and hypermedia-driven environment. We focus particularly on the security domain since current security practices on the Web illustrate the differences between both the centralized, function-based approach and the decentralized, hypermedia and resource-based approach. We rely on ReLL (a REST service description) that can be processed by machine-clients in order to interact with RESTful services. Our approach identifies key security domain elements as an ontology. Elements serve to model hypermedia-based, decentralized security descriptions supporting simple and complex interaction such as protocols and callbacks. In this paper, we propose an extension to ReLL that considers security constraints (ReLL-S) and allows a machine-client to interact with secured resources, where security conditions may change dynamically. A case study illustrates our approach.

QoS-Aware Scheduling of Services-Oriented Internet of Things

The Internet of Things (IoT) contains a large number of different devices and heterogeneous networks, which make it difficult to satisfy different quality of service (QoS) requirements and achieve rapid services composition and deployment. In addition, some services in service-oriented IoT are required to be reconfigurable and composable for QoS-aware services. This paper proposed a three-layer QoS scheduling model for service-oriented IoT. At application layer, the QoS schedule scheme explores optimal QoS-aware services composition by using the knowledge of each component service. At network layer, the model aims at dealing with scheduling of heterogeneous networks environment; at sensing layer, it deals with the information acquisition and resource allocation scheduling for different services. The proposed QoS-aware scheduling for service-oriented IoT architecture is able to optimize the scheduling performance of IoT network and minimize the resource costs.

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.

An efficient and reliable approach for quality-of-service-aware service composition

With the rapidly increasing number of independently developed Web services that provide similar functionalities with varied quality of service (QoS), service composition is considered as a problem in the selection of component services that are in accordance with users’ QoS requirements; a practice known as the QoS-aware service composition problem. However, current solutions are unsuitable for most real-time decision-making service composition applications required to obtain a relatively optimal result within a reasonable amount of time. These services are also unreliable (or even risky) given the open service-oriented environment. In this paper, we address these problems and propose a novel heuristic algorithm for an efficient and reliable selection of trustworthy services in a service composition. The proposed algorithm consists of three steps. First, a trust-based selection method is used to filter untrustworthy component services. Second, convex hulls are constructed to reduce the search space in the process of service composition. Finally, a heuristic global optimization approach is used to obtain the near-optimal solution. The results demonstrate that our approach obtains a close-to-optimal and reliable solution within a reasonable computation time.

QoS decomposition for service composition using genetic algorithm

Recently, a lot of research has been dedicated to optimizing the QoS-aware service composition. This aims at selecting the optimal composed service from all possible service combinations regarding user's end-to-end quality requirements. Existing solutions often employ the global optimization approach, which does not show promising performance. Also, the complexity of such methods extensively depends on the number of available web-services, which continuously increase along with the growth of the Internet. Besides, the local optimization approaches have been rarely utilized, since they may violate the global constraints. In this paper, we propose a top-down structure, named quality constraints decomposition (QCD) here, to decompose the global constraints into the local constraints, using the genetic algorithm (GA). Then the best web service for each task is selected through a simple linear search. In contrast to existing methods, the QCD approach mainly depends on a limited set of tasks, which is considerably less complex, especially in the case of dynamically distributed service composition. Experimental results, based on a well-known data set of web services (QWSs), show the advantages of the QCD method in terms of computation time, considering the number of web services.

QoS-aware service composition for distributed video surveillance

Quality of Service (QoS) is essential for the ubiquitous access of media services in real-time distributed video surveillance applications. To have ubiquitous access of desired media with emergency officials' handheld devices, appropriate media transcoding services are required. Currently, it is challenging to select and compose these services for each of the devices to satisfy the desired QoS demand. To compose these media services so that video stream is available for target pervasive and smart devices, a composition algorithm is required. Thus, this paper presents a QoS-aware service composition algorithm to select the best composition for the target ubiquitous client so that it can optimally provide QoS to heterogeneous users. We have implemented a video surveillance prototype to demonstrate the performance of the proposed QoS-aware composition algorithm. Results from this prototype reveal that the approach is suitable for real-time video surveillance.

Selecting skyline services for QoS-aware composition by upgrading MapReduce paradigm

With the development of web technologies and cloud computing, more and more services which provide similar functionality but differ in QoS are deployed on the Internet via cloud platforms. Recently, skyline analysis is adopted to select candidate services with better QoS to facilitate the process of QoS-aware service composition. However, the fast increasing number of services, multiple QoS attributes to be considered, and dynamic service environment pose a big challenge to skyline service selection. In this paper, we present a parallel skyline service selection method to improve the efficiency by upgrading the MapReduce paradigm. An angle-based dataspace partitioning approach is employed in our MapReduce based skyline service selection. In particular, we explore the dominance power of local skyline services to improve the efficiency of selection, and present two detailed algorithms. To handle the dynamic nature of service environment, we employ Paper-Tape (PT) model which is used to rapidly locate varying services, and present a dynamic skyline service selection algorithm based on PT model. By experimenting over both real and synthetical datasets, we demonstrate the efficiency of our proposed methods.

Modeling and Algorithms for QoS-Aware Service Composition in Virtualization-Based Cloud Computing

Modeling and Algorithms for QoS-Aware Service Composition in Virtualization-Based Cloud Computing