Robust Service Composition Research Articles

An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm

The smooth execution of tasks in the cloud manufacturing (CMfg) process can be significantly affected by various real-world uncertainties, resulting in inefficiencies or even failures of the composite manufacturing service (CMS). Nevertheless, current research primarily concentrates on post-exception emergency handling methods, heavily dependent on the planned CMS and real-time status of manufacturing resources, often leading to suboptimal temporary adjustments or even the inability to find a viable solution. Hence, there is an urgent necessity to enhance the robustness of the planned CMS during the service composition and optimal selection (SCOS) phase. In pursuit of this goal, the paper initially presents an adaptive robust service composition and optimization selection (ARSCOS) model. This model dynamically determines whether to await exception resolution or invoke an alternative service, considering the circumstances when a service exception arises and employing task delay time to characterize the robustness of a specific CMS. Subsequently, an enhanced multi-objective artificial hummingbird algorithm (EMOAHA) is devised for this model. Finally, the efficacy of EMOAHA is demonstrated by applying it to nine different scales of civilian unmanned aerial vehicle manufacturing SCOS problems and 17 benchmark functions. The results illustrate that the proposed EMOAHA exhibits superior convergence and diversity in comparison to the other 11 classical intelligent optimization algorithms.

Digital Thread-Driven Proactive and Reactive Service Composition for Cloud Manufacturing

This article develops a proactive and reactive service composition (PRSC) method for CMfg based on digital thread. First, the digital thread-driven information interaction architecture is established, based on which the detailed process of PRSC and the constitution of digital thread are designed. Then, the strategy of proactive SC and the process of reactive service adjustment are proposed: the former selectively allocate alternative services for subtasks to balance the quality of service (QoS) and robustness of the composite manufacturing service, the latter implements the decision-making for service exception handling based on digital thread. Finally, a group of simulation experiments and a practical case study are conducted. The results show that the proposed digital thread-driven PRSC method integrates the advantages of proactive planning and reactive adjustment, effectively reduces the service reservation cost of robust service composition and improves the actual QoSs under uncertainties based on the support of near real-time information interaction.

A robust service composition and optimal selection method for cloud manufacturing

ABSTRACT During the process of cloud manufacturing, various uncertainties in the real world could have a significant impact on the smooth execution of task, and could render the planned composite manufacturing service (CMS) inefficient or even ineffective. Therefore, this paper proposes an optimal selection method to enhance the robustness of CMS during the planning stage. Firstly, the structure of robust CMS is proposed by arranging the preferred and alternative services for each subtask, and a robust service composition and optimal selection (rSCOS) model of cloud manufacturing is constructed by defining the expected Quality of Service. Then, the gABC-GWO (guiding artificial bee colony – grey wolf optimisation) algorithm is proposed to solve the rSCOS model efficiently, in which three improvement strategies for ABC algorithm are designed according to the characteristics of GWO. Finally, two experiments are implemented and the results show that QoS of the preferred scheme of robust CMS is approximately 1.29% lower than that of CMS on average, while its robustness is improved by 1.81% and 13.14% depending on the two robustness indexes. Compared with other commonly-used intelligence optimisation algorithms, gABC-GWO algorithm possesses better search performance without significantly increasing time consumption, which makes it more suitable for solving rSOCS problems.

ARC: Anomaly-aware Robust Cloud-integrated IoT service composition based on uncertainty in advertised quality of service values

From the IoT perspective, each intelligent device can be considered as a potential source of service. Since several services perform the same function, albeit with different quality of service (QoS) parameters, service composition becomes a crucial problem to find an optimal set of services to automate a typical business process. The majority of prior research has investigated the service composition problem with the assumption that advertised QoS values are deterministic and do not change over time. However, factors like sensors failure and network topology changes cause uncertainty in the advertised QoS values. To address this challenge, we propose a novel Anomaly-aware Robust service Composition (ARC) to deal with the problem of uncertainty of QoS values in a dynamic environment of Cloud and IoT. The proposed approach uses Bertsimas and Sim mathematical robust optimization method, which is independent of the statistical distribution of QoS values, to compose services. Moreover, our approach exploits a machine learning-based anomaly detection technique to improve the stability of the solution with a fine-grained identification of abnormal QoS records. The results demonstrate that our approach achieves 14.55% of the average improvement in finding optimal solutions compared to the previous works, such as information theory-based and clustering-based methods.

Modeling and analysis of robust service composition for network functions virtualization

Fault tolerance is critical for constructing a reliable service in Network Functions Virtualization (NFV). In this paper, we propose novel models and algorithms that provide the resilience of NFV services from multiple node and link failures. We first design an optimization model and the PAR protection algorithm that can efficiently protect an NFV service demand from network failures without any action from a controller due to the diversity of flow assignment. We then develop an optimization model for total demand protection with a guarantee of recovering the whole demand volume. Further, a new restoration algorithm, namely UNIT, is proposed for the design of large survivable NFV-based networks with the recovery of the affected bandwidth under the uncertainty of multiple network failures. We analytically prove the performance guarantee of UNIT in comparison with the optimal static solution. The results of our experimental study in a Mininet-based environment with the Ryu controller show that a combination of PAR and UNIT efficiently protects NFV-based networks from failures in terms of both resource restoration and recovery time.

Robust Service Compositions with Functional and Location Diversity

Service composition provides a means of customized and flexible integration of service functionalities. Quality-of-service (QoS) optimization algorithms select services to adapt workflows to the non-functional requirements of the user. With increasing number of services in a workflow, previous approaches fail to achieve a sufficient reliability. Moreover, expensive ad-hoc replanning is required to deal with service failures. The major problem with such sequential application of planning and replanning is that it ignores the potential costs during the initial planning and they consequently are hidden from the decision maker. Our idea to overcome this problem is to compute a QoS optimized selection of service clusters that includes a sufficient number of backup services for each service employed. These backup services should be sufficiently distributed to prevent a task failure in case of, e.g., a network failure. To support the decision maker in the selection task, our multi-objective approach considers the possible repair costs directly in the initial composition. Our graphical user interface visualizes the resulting QoS of the workflow and the location of the services to enable the decision maker to select compositions in line with risk preferences. We prove the benefits of our approach in our detailed evaluation.

A formal modeling platform for composing web services

Building a robust service composition is increasingly problematic as large scales of Web service operations can now be possibly achieved through the Internet. This article proposes an effective composing platform based on a formal modeling language. In particular, Petri Nets patterns are developed for enhancing service composition modeling, workflow quality measurement and scripting productivity. By providing a visual editing module, this platform allows business application developers to exploit its composing capabilities without much requiring design time or skills.

The next step in Web services

How three specifications support creating robust service compositions.