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
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- 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.
Spectrum-Based Runtime Anomaly Localisation in Service-Based Systems
- Authors: He, Qiang , Xie, Xiaoyuan , Chen, Feifei , Vasa, Rajesh , Yang, Yun , Jin, Hai
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE International Conference on Services Computing (SCC)
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- Description: Runtime anomalies occurring to service-based systems (SBSs) 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 is impractical due to excessive resource consumption. Inspecting all component services upon anomalies is time-consuming and thus also impractical. In this work, we propose a novel approach that employs spectrum-based fault localisation techniques to locate runtime anomalies in SBSs. Large-scale experiments are conducted and experimental results are presented to demonstrate the effectiveness and efficiency of the proposed approach.
QoS-aware service selection for customisable multi-tenant service-based systems : Maturity and approaches
- Authors: He, Qiang , Han, Jun , Chen, Feifei , Wang, Yanchun , Vasa, Rajesh , Yang, Yun , Jin, Hai
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE 8th International Conference on Cloud Computing (CLOUD) p. 237-244
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- Description: Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar and yet customised functionalities with potentially different quality-of service (QoS) values. Thus, existing approaches to service selection for single-tenant SBSs are no longer suitable. Furthermore, the target multi-tenancy maturity level also needs to be considered in the service selection approach for an SBS. In this paper, we propose three novel QoS-aware service selection approaches for composing multi-tenant SBSs that achieve three different multi-tenancy maturity levels. Extensive and comprehensive experiments are conducted and the experimental results show that our approaches outperform the existing approach in both effectiveness and efficiency.
StressCloud : A tool for analysing performance and energy consumption of cloud applications
- Authors: Chen, Feifei , Grundy, John , Schneider, Jean-Guy , Yang, Yun , He, Qiang
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE)
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- Description: Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and deployment configurations. This paper demonstrates StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports 1) the modelling of realistic cloud application workloads, 2) the automatic generation and running of load tests, and 3) the profiling of system performance and energy consumption.
An energy consumption model and analysis tool for Cloud computing environments
- Authors: Chen, Feifei , Schneider, Jean-Guy , Yang, Yun , Grundy, John , He, Qiang
- Date: 2012
- Type: Text , Conference paper
- Relation: 2012 First International Workshop on Green and Sustainable Software (GREENS) : Part of the 34th International Conference on Software Engineering (ICSE) p. 45-50
- Full Text: false
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- Description: Cloud computing delivers computing as a utility to users worldwide. A consequence of this model is that cloud data centres have high deployment and operational costs, as well as significant carbon footprints for the environment. We need to develop Green Cloud Computing (GCC) solutions that reduce these deployment and operational costs and thus save energy and reduce adverse environmental impacts. In order to achieve this objective, a thorough understanding of the energy consumption patterns in complex Cloud environments is needed. We present a new energy consumption model and associated analysis tool for Cloud computing environments. We measure energy consumption in Cloud environments based on different runtime tasks. Empirical analysis of the correlation of energy consumption and Cloud data and computational tasks, as well as system performance, will be investigated based on our energy consumption model and analysis tool. Our research results can be integrated into Cloud systems to monitor energy consumption and support static or dynamic system-level optimisation.