Research Of Failure Detection Algorithm Based On Wavelet Transform And Combinational Models

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalablecloud networks. In cloud-based networks, services may be virtualized with specific server hosting detailsabstracted, with some servers active and available, others busy or heavily loaded, and others are offline forvarious reasons. Therefore, in order to provide an effective control scheme and parameter guidance for serviceconditions and cloud resources, failure detection is essential to meet user service expectations. Effective failuredetection is a fundamental factor in addressing possible performance bottlenecks in providing virtual service. Itis advisable to design and implement an acceptable failure detector (FD) for cloud-based networks.Most existing FD schemes do not automatically adjust their detection service parameters for dynamic networkconditions. Given that networks are dynamic and unexpected, this paper explores FD properties with relationto actual and automatic fault-tolerant cloudbased network systems, so as to find a general non-manual analysismethod to self-tune corresponding parameters to satisfy user requirements. Based on this general self-tuningmethod, we propose a dynamic and automatic Self-tuning Failure Detector scheme, called SFD, as animprovement over existing schemes. Extensive experiments are carried out to compare Quality of Service(QoS) metrics of several adaptive FDs. Experimental results demonstrate that our scheme has goodperformance and can automatically adjust corresponding parameters to satisfy user requirements. Such a SFDcan be significantly beneficial in cloud-based environments.