A Fuzzy Load Balancer for Adaptive Fault Tolerance Management in Cloud Platforms
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To achieve high levels of reliability, availability and performance in cloud environments, a fault tolerance approach to handle failures effectively is needed. In most existing research, the primary focus has been on explicit specication-driven solutions which requires too much effort for application developers, and leads to infexibility. We propose a fuzzy job distributor (load balancer) for fault tolerance management to reduce levels of management complexity for the user. The proposed approach aims to reduce the possibility of fault occurrences in the system by a fair distribution of user job requests among available resources. In our self-adaptive approach, the system manages anomalous situations that might lead to failure by distributing the incoming job request based on the reliability of processing nodes, i.e., virtual machines (VMs). The reliability of VMs is a variable parameter and changes during its life-time. Our approach is implemented and comparatively analysed using OpenStack. The experimental results show a signicant reduction in the occurrence of faults in comparison with other load balancing algorithms.