Service Composition Pattern Generation for Cloud Migration: A Graph Similarity Analysis Approach

Wan, Zhitao; Meng, Fan Jing; Xu, Jing; Wang, Ping

doi:10.1109/icws.2014.54

Cited by 11 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following works are devoted to optimizing the existing service composition based on mining patterns from existing data. A mining algorithm based on statistical techniques to discover composite web service patterns from execution logs is proposed by [23] to better understand, control, and eventually redesign the composite services while [24] proposed an approach to generate service composition pattern for cloud migration from a set of service composition solutions by a graph similarity analysis approach. In [25], an event-based monitoring approach for service composition infrastructures is presented to provide a holistic monitoring approach by leveraging Complex Event Processing techniques.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Process Mining Based Service Composition Approach for Mobile Information Systems

Huang

Cai

et al. 2017

Mobile Information Systems

View full text Add to dashboard Cite

Due to the growing trend in applying big data and cloud computing technologies in information systems, it is becoming an important issue to handle the connection between large scale of data and the associated business processes in the Internet of Everything (IoE) environment. Service composition as a widely used phase in system development has some limits when the complexity of relationship among data increases. Considering the expanding scale and the variety of devices in mobile information systems, a process mining based service composition approach is proposed in this paper in order to improve the adaptiveness and efficiency of compositions. Firstly, a preprocessing is conducted to extract existing service execution information from server-side logs. Then process mining algorithms are applied to discover the overall event sequence with preprocessed data. After that, a scene-based service composition is applied to aggregate scene information and relocate services of the system. Finally, a case study that applied the work in mobile medical application proves that the approach is practical and valuable in improving service composition adaptiveness and efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In [25], an event-based monitoring approach for service composition infrastructures is presented to provide a holistic monitoring approach by leveraging Complex Event Processing techniques. In summary, the works [23][24][25] use data mining instead of process mining.…”

Section: Related Workmentioning

confidence: 99%

A Process Mining Based Service Composition Approach for Mobile Information Systems

Huang

Cai

et al. 2017

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…More and more business applications are migrated to the Cloud inspired by the booming cloud computing [2]. As an example, in the service industry, programmableweb.com 1 (PW) has collected a repository containing over 12k APIs up to now, and the number has been nearly doubled year by year.…”

Section: Introductionmentioning

confidence: 99%

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

Wang

et al. 2018

IEEE Trans. Serv. Comput.

View full text Add to dashboard Cite

As the number of Cloud services is growing at a tremendous speed, there is an increasing number of service providers offering similar functionalities. Selecting services with user desired non-functional properties (NFPs) becomes of significant importance but triggers a number of Big Data related research issues. First, the selection decision should deal with a large volume of service NFPs data. Second, service selection needs to reflect diverse user preferences, including both qualitative and quantitative ones. Third, the uncertainty of the network and service load leads to high variability in NFPs. Fourth, as the trust values of service NFPs are collected via historic user's feedbacks, it brings the veracity dimension to the NFPs of services. Fifth, multiple and sometimes conflicting decision objectives for optimal service selection should be balanced. An effective service selection mechanism is in demand that can tackle all the above Big Data challenges in an integrated way to handle the highly diverse QoS with significant variability along with the trust related issues giving rise to data veracity. Existing investigations focus on either users' QoS preferences or their trust concerns but fail to provide a systematic solution to integrate both criteria in the selection process. In this paper, we tackle heterogeneous preferenceand trust-based service selection by developing a novel multi-objective optimization approach to make trade-off decision between service's trust value and user's QoS preference to rank candidate Cloud services based on their match degrees with users' requirements. We conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed approach.

show abstract

Leveraging Process Mining on Service Events Towards Service Composition

Cai

Huang

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Service Composition Pattern Generation for Cloud Migration: A Graph Similarity Analysis Approach

Cited by 11 publications

References 23 publications

A Process Mining Based Service Composition Approach for Mobile Information Systems

A Process Mining Based Service Composition Approach for Mobile Information Systems

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

Leveraging Process Mining on Service Events Towards Service Composition

Contact Info

Product

Resources

About