Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning. A common approach used in both commercial and open-source computing platforms is workload-based automatic scaling, which expands instances by increasing the number of incoming requests. Concurrency is a request-based policy that has recently been proposed in the evolving microservice framework; in this policy, the algorithm can expand its resources to the maximum number of configured requests to be processed in parallel per instance. However, it has proven difficult to identify the concurrency configuration that provides the best possible service quality, as various factors can affect the throughput and latency based on the workloads and complexity of the infrastructure characteristics. Therefore, this study aimed to investigate the applicability of an artificial intelligence approach to request-based auto-scaling in the microservice framework. Our results showed that the proposed model could learn an effective expansion policy within a limited number of pods, thereby showing an improved performance over the underlying auto expansion configuration.
As the resource management systems continues to grow, the resource distribution system is expected to expand steadily. The demand response system enables producers to reduce the consumption costs of an enterprise during fluctuating periods in order balance the supply grid and resell the remaining resources of the product to generate revenue. Q-learning, a reinforcement learning algorithm based on a resource distribution compensation mechanism, is used to make optimal decisions to schedule the operation of smart factory appliances. In this paper, we proposed an effective resource management system for enterprise demand response using a Quad Q Network algorithm. The proposed algorithm is based on a Deep Q Network algorithm that directly integrates supply-demand inputs into control logic and employs fuzzy inference as a reward mechanism. In addition to using uses the Compare Optimizer method to reduce the loss value of the proposed Q Network Algorithm, Quad Q Network also maintains a high accuracy with fewer epochs. The proposed algorithm was applied to market capitalization data obtained from Google and Apple. Also, we verified that the Compare Optimizer used in Quad Q Network derives the minimum loss value through the double operation of Double Q value.
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