Keyword search for building service-based systems
- Authors: He, Qiang , Zhou, Rui , Zhang, Xuyun , Wang, Yanchun , Ye, Dayong , Chen, Feifei , Grundy, John , Yang, Yun
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Transactions on Software Engineering Vol. 43, no. 7 (2017), p. 658-674
- Full Text: false
- Reviewed:
- Description: With the fast growth of applications of service-oriented architecture (SOA) in software engineering, there has been a rapid increase in demand for building service-based systems (SBSs) by composing existing Web services. Finding appropriate component services to compose is a key step in the SBS engineering process. Existing approaches require that system engineers have detailed knowledge of SOA techniques which is often too demanding. To address this issue, we propose Keyword Search for Service-based Systems (KS3), a novel approach that integrates and automates the system planning, service discovery and service selection operations for building SBSs based on keyword search. KS3 assists system engineers without detailed knowledge of SOA techniques in searching for component services to build SBSs by typing a few keywords that represent the tasks of the SBSs with quality constraints and optimisation goals for system quality, e.g., reliability, throughput and cost. KS3 offers a new paradigm for SBS engineering that can significantly save the time and effort during the system engineering process. We conducted large-scale experiments using two real-world Web service datasets to demonstrate the practicality, effectiveness and efficiency of KS3. © 1976-2012 IEEE.
Localising runtime Anomalies in Service-Oriented Systems
- Authors: He, Qiang , Xie, Xiaoyuan , Wang, Yanchun , Ye, Dayong , Chen, Feifei , Jin, Hai , Yang, Yun
- Date: 2016
- Type: Text , Conference proceedings
- Relation: IEEE Transactions on Services Computing ( Volume: 10, Issue: 1, Jan.-Feb. 1 2017 ) Vol. 10, p. 94-106
- Full Text: false
- Reviewed:
- Description: In a distributed, dynamic and volatile operating environment, runtime anomalies occurring in service-oriented systems (SOSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly and inspecting the entire SOS upon a runtime anomaly are impractical due to excessive resource and time consumption required, especially in large-scale scenarios. We present a spectrum-based approach that goes through a five-phase process to quickly localize runtime anomalies occurring in SOSs based on end-to-end system delays. Upon runtime anomalies, our approach calculates the similarity coefficient for each basic component (BC) of the SOS to evaluate their suspiciousness of being faulty. Our approach also calculates the delay coefficients to evaluate each BC's contribution to the severity of the end-to-end system delays. Finally, the BCs are ranked by their similarity coefficient scores and delay coefficient scores to determine the order of them being inspected. Extensive experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach. The results indicate that our approach significantly outperforms random inspection and the popular Ochiai-based inspection in localizing single and multiple runtime anomalies effectively. Thus, our approach can help save time and effort for localizing runtime anomalies occuring in SOSs.