A Self-tuning Failure Detection Scheme for Cloud Computing Service

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others ar...

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Veröffentlicht in:2012 IEEE 26th International Parallel and Distributed Processing Symposium S. 668 - 679
Hauptverfasser: Naixue Xiong, Vasilakos, A. V., Jie Wu, Yang, Y. R., Rindos, A., Yuezhi Zhou, Wen-Zhan Song, Yi Pan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2012
Schlagworte:
Application requirements
Cloud computing
Cloud computing service
Computational modeling
Computer crashes
Delay
Education
Fault tolerance
Heart beat
Quality of service
Self-tuning failure detection
ISBN:1467309753, 9781467309752
ISSN:1530-2075
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Zusammenfassung:Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.
ISBN:1467309753
9781467309752
ISSN:1530-2075
DOI:10.1109/IPDPS.2012.126