A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing
Introduction
Nowadays, cloud computing or service-oriented computing (SOC), where Web service is the essential element to support rapid, low-cost, and easy composition of distributed applications in heterogeneous environments; have become increasingly popular, and triggers a considerable amount of research efforts in both academia and industry (Papazoglou, Traverso, Dustdar, & Leymann, 2007). SOC provides a new opportunity to the modern manufacturing industry to do e-manufacturing and business collaborations for global manufacturing, which aims to align business processes across the organizations (Wu, Greer, Rosen, et al., 2013). Based on the trends in devices, growth of data, communication bandwidth, and growth of peer to peer sharing, it is argued that utilizing Web services offered by third-parties with a greatly reduced cost is emerging for manufacturing industries that will unlock the tremendous value in the massive amount of data being generated by the industrial manufactories (Tao & Zhang, 2012).
In most cases, individual Web services are combined to a composite service to solve a complex goal successfully. Fig. 1 shows an example of composite service used in warehouse fire alarm, where individual devices providing simple function can be combined to establish a more powerful alarm service. It involves different tasks including temperature sensors, smoke detectors, infrared sensors, liquid immersion sensors, alarm bells, camera, scupper valves and water sprinklers. Each task can be bound to various Web services providing the same functionality but with different quality properties.
As many Web services deliver the equivalent functionality, Quality of Service (QoS), such as the cost, response time, reliability, and availability are crucial concern to help users to select appropriate services satisfying their preferences, for instance, cost should be less than $20, and response time should be less than two hours. The concern with QoS introduces a challenging problem of QoS-aware Web service composition (QWSC), i.e., select the best composite service with optimal QoS performance while satisfying given end-to-end QoS constraints (Qi, Dou, Zhang, & Chen, 2012). Unfortunately, due to the increasing number of component services (tasks) in a composite service and the growing spread of candidates for each task, the size of possible composition plans built by selecting different Web service instance for each component service should be in general exponentially enlarging. In other words, the QWSC is a NP-Complete problem in a strong sense which cannot be solved within a reasonable time (Haddad, Manouvrier, & Rukoz, 2010).
Motivated by the large-scale QWSC problem, this study propose a flexible and efficient Web service selection method by multi-objective optimization to provide users enhanced information to choose appropriate services. A QoS model for describing vague concept of QoS with specific features is constructed. Furthermore, traditional optimization methods for QWSC tend to find the best composite service fulfilling user’s overall QoS constraint. They may fail to provide a possible solution if unsuitable QoS constraints are submitted. Unfortunately, it is impossible that users are always well-informed to submit reasonable QoS requirements. From this perspective, then the problem of QWSC is formulated into a multi-objective optimization model where either QoS performance or variance from the user’s QoS constraints (i.e. QoS risk) is an independent objective. Moreover, a ε-dominance multi-objective evolutionary algorithm (EDMOEA) is developed to solve the presented model. Finally, experimental studies demonstrate the effectiveness and the efficiency of the proposed method.
The remainder of this paper is organized as follows. Section 2 gives an overview of related work. Section 3 presents a QoS model of Web service that defines the QoS criteria including cost, execution time, latency time, reliability and availability; and the QoS aggregation for a composite service. The multi-objective optimization model for QWSC and the solving algorithm are specified in Sections 4 Multi-objective optimization model for service selection, 5 Multi-objective optimization solving algorithm design respectively, Section 6 presents the experimental studies, while Section 7 offers the concluding remarks and outlines future study.
Section snippets
Related work
As stated, service composition is recognized as a procedure where individual Web services are choreographed into a complex composite service. The main approach for Web service composition is the model-based approach, that uses a composition schema to express the abstract functionalities of component services and the integration logics of component services (Kwon and Lee, 2012, Strunk, 2010, Tao et al., 2012). Composition engine schedules and executes concrete component services according to the
QoS computing model
In this section, we give the details on how to evaluate QoS of Web service and aggregate QoS of composite service based on workflow patterns.
Multi-objective optimization model for service selection
The goal of a multi-objective optimization problem (MOP) is to find a set of compromise solutions regarding different objectives rather than the best one as in single-objective optimization problems. A multi-objective optimization model can be defined as following (Chinchuluun & Pardalos, 2007):Therein, Ω ∊ Rn is the feasible solutions set that is nonempty, f(x): Ω → Rk is a vector-valued function, k is the number of objectives.
As stated, the problem of finding
Multi-objective optimization solving algorithm design
For MOP (shown in Eq. (1)), solution x1 dominates solution x2 if and only if f(x1) is partially less than f(x2), i.e. ∀i ∊ {1,2, … ,k}, fi(x1) ⩽ fi(x2) ∧ ∃i ∊ {1,2, … ,k}, fi(x1) − εi < fi(x2),denoted as . The result of MOP is a set of non-dominated solutions known as Pareto optimal solutions or Pareto frontier. Convergence and distribution (or diversity) are the main concerns in the MOEAs. Though some multi-objective evolutionary algorithms (MOEAs) have been proposed to solve the general MOP (Deb,
Experiment studies
In order to verify the proposed method, we performed simulation experiments on an illustrated web service composition scenario. The algorithm was coded in Matlab7.1 and ran on an Intel Core 8 × 3.4 MHz PC with 4 GB RAM under Windows 7.
Conclusions and future work
In order to offer flexible alternative choice to users, we propose a novel QWSC method based on multi-objective optimization. The problem of QWSC is transformed to a MOP where both the QoS performance and the QoS risk are the optimization objective. Moreover, addressing the large-scale QWSC, an efficient EDMOEA is developed. The proposed method tries to find the Pareto optimal services, which supports users to select suitable services with the tradeoff of the QoS performance and the QoS risk.
Acknowledgements
The work was supported by the General Program of the National Natural Science Foundation of China (No. 71101103, No. 71201115) and the National Natural Science Fund for Distinguished Young Scholars of China (No. 70925005). We sincerely appreciate Shanshan Hao, who used to be a master student in college of management and economics in Tianjin University, provided preliminary experimental validation in this study.
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