Abstract
Due to the fact that the resource is prone to be wrong during tasks execution in cloud, which leads to failed tasks, in view of the recent research, the Primary-Backup model (PB model) is mostly used to deal with fault-tolerant tasks, but the selection of passive scheme and active scheme is assumed in advance, and the advantages between the two schemes are not fully utilized. Based on the deep reinforcement learning, this paper proposes an adaptive PB model selection algorithm, Active-Passive Scheme DQN (APSDQN). The process of faulty task tolerance is regarded as a Markov decision process, taking the passive scheme and active scheme as the action spaces, the shortest completion time of the task and the highest resource utilization as the reward feedback, combine with the real environment state information, select the most suitable fault-tolerant scheme for faulty tasks to save resources and improve the robustness of cloud system. The experimental results show that APSDQN has certain advantages in the total task finish time of task allocation, and significantly improves the resource utilization and the task success rate in the cloud.
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Tang, H., Tang, Z., Dong, T., Hai, Q., Xue, F. (2022). Fault-Tolerant Scheme of Cloud Task Allocation Based on Deep Reinforcement Learning. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_5
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