While cloud systems deliver a larger amount of computing power, they do not guarantee full security and reliability. Focusing on improving successful job execution under resource constraints and security problems, this work proposes an enhanced, effective, integrated and novel approach to security and reliability. To apply a high level of security in the system, our novel approach uses cloud biometric authentication by splitting the biometric data into small chunks and spreading it over the cloud's resources. Reliability is enhanced through successful job execution by employing an adaptive reinforcement learning (RL) algorithm combined with a queuing theory. Our approach supports task schedulers to effectively adapt to dynamic changes in cloud environments. Based on the idea of reliability, we developed an adaptive action-selection, which controls the action selection dynamically by considering queue buffer size and the uncertainty value function. We evaluated the performance of our approach by several experiments conducted in terms of successful task execution and utilization rate and then compared our approach with other job scheduling policies. The experimental results demonstrated the efficiency of our method and achieved the objectives of the proposed system.

biometric authentication, security, reinforcement learning, Q-learning, reliability, queuing theory