Cognitive Neural Network Classifier for Fault Management in Cloud Data Center

Pro-actively handling the fault in data center is a means to allocate the VM to Host before failures, so that SLA meets for the tasks running in the data center. Existing solution [1] on fault prediction in datacenter is based on a single parameter of temperature and the fault tolerance is implemented as a reactive solution in terms of VM replication. Different from these works, a proactive fault tolerance with fault prediction based on deep learning with multiple parameters is proposed in this work. In this work Cognitive Neural Network (CNN) is used to predict the failure of hosts and initiate migration or avoid allocation to the hosts which has high probability of failures. Hosts in the data center are scored on failure probability (FP-Score) based on parameters collected at various levels using CNN. VM placement and migration policies are fine-tuned using FP-Score to manage the failure proactively.

@article{Indirani2019,
  title     = {Cognitive Neural Network Classifier for Fault Management in Cloud Data Center},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {8},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {S Indirani and C.Jothi Venkateswaran},
  doi       = {10.14569/IJACSA.2019.0100866},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100866}
}