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.
Personalized quality centric service recommendation
- Authors: Zhang, Yiwen , Ai, Xiaofei , He, Qiang , Zhang, Xuyun , Dou, Wanchun , Chen, Feifei , Chen, Liang , Yang, Yun
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 15th International Conference on Service-Oriented Computing, ICSOC 2017; Malaga, Spain; 13th-16th November 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10601 LNCS, p. 528-544
- Full Text: false
- Reviewed:
- Description: The broad application of service-oriented architecture (SOA) has fueled the rapid growth of web and cloud services and service-based systems (SBSs). Tremendous web and cloud services have been deployed all over the world. Finding the right services becomes difficult and critical. Thus, service recommendation has become of paramount research and practical importance. Existing web service recommendation approaches employ utility functions or skyline techniques. However, those approaches have not addressed a critical and fundamental problem: how to recommend services according to a system engineer’s quality constraints, e.g., response time, failure rate, etc. To address this issue, we first propose two basic personalized quality centric approaches for service recommendation, which employ the k-nearest neighbours and the dynamic skyline techniques respectively. To overcome the respective limitations of the two basic approaches, we propose two hybrid approaches, namely KNN-DSL and DSL-KNN. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches. © Springer International Publishing AG 2017.
- Description: The broad application of service-oriented architecture (SOA) has fueled the rapid growth of web and cloud services and service-based systems (SBSs). Tremendous web and cloud services have been deployed all over the world. Finding the right services becomes difficult and critical. Thus, service recommendation has become of paramount research and practical importance. Existing web service recommendation approaches employ utility functions or skyline techniques. However, those approaches have not addressed a critical and fundamental problem: how to recommend services according to a system engineer’s quality constraints, e.g., response time, failure rate, etc. To address this issue, we first propose two basic personalized quality centric approaches for service recommendation, which employ the k-nearest neighbors and the dynamic skyline techniques respectively. To overcome the respective limitations of the two basic approaches, we propose two hybrid approaches, namely KNN-DSL and DSL-KNN. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches. © Springer International Publishing AG 2017.