A Class of Practical Self-tuning Failure Detection Schemes for Distributed Networks
A Class of Practical Self-tuning Failure Detection Schemes for Distributed Networks
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发表时间：
2011
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通讯作者：
N. Xiong
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作者：
N. Xiong
云计算是一种日益重要的解决方案，用于提供部署在动态可扩展云网络中的服务。云计算网络中的服务可以用托管抽象细节的特定服务器来虚拟化。其中一些服务器处于活动状态且可用，而其他服务器处于繁忙或负载过重状态，其余服务器由于各种原因处于脱机状态。用户期望合适且可用的服务器能够满足他们的应用程序要求。因此，为了为云资源服务提供有效的参数引导控制方案，故障检测是满足用户服务期望的关键。它可以解决为云计算网络提供虚拟服务时可能出现的性能瓶颈。现有的故障检测(FD)方案大多不能根据动态网络条件自动调整检测服务参数，不能用于实际应用。本文结合实际的自动容错云计算网络，探讨了FD的性质，并找到了一种通用的非人工分析方法来自动调整相应的参数，以满足用户的需求。基于这种通用的自动化方法，我们提出了一种特定的动态自校正故障检测器，称为SFD，作为对现有方案的重大突破。我们进行了大量的实际实验，比较了SFD和其他几种现有FDs的服务质量性能。实验结果表明，该方案能够在保持良好性能的同时，自动调整SFD控制参数，获得相应的服务，满足用户需求。这样的SFD可以广泛应用于工业和商业用途，也可以显著受益于云计算网络。
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 a 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.