Cognitive Neural Network Classifier for Fault Management in Cloud Data Center
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 8
Abstract
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.
Authors and Affiliations
S. Indirani, C. Jothi Venkateswaran
Keywords
Related Articles
- EP ID EP626822
- DOI 10.14569/IJACSA.2019.0100866
- Views 113
- Downloads 0
Firefly Algorithm for Adaptive Emergency Evacuation Center Management
Flood disaster is among the most devastating natural disasters in the world, claiming more lives and causing property damage. The pattern of floods across all continents has been changing, becoming more frequent, intense...
Enhanced Textual Password Scheme for Better Security and Memorability
Traditional textual password scheme provides a large number of password combinations but users generally use a small portion of available password space. Complex textual passwords are difficult to remember, therefore mos...
OntoDI: The Methodology for Ontology Development on Data Integration
The implementations of data integration in current days have many issues to be solved. Heterogeneity of data with non-standardization data, data conflicts between various data sources, data with a different representatio...
Big Data Knowledge Mining
Big Data (BD) era has been arrived. The ascent of big data applications where information accumulation has grown beyond the ability of the present programming instrument to catch, manage and process within tolerable shor...
A Hybrid Approach for Co-Channel Speech Segregation based on CASA, HMM Multipitch Tracking, and Medium Frame Harmonic Model
This paper proposes a hybrid approach for co-channel speech segregation. HMM (hidden Markov model) is used to track the pitches of 2 talkers. The resulting pitch tracks are then enriched with the prominent pitch. The enr...