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Optimizing resource allocation using proactive scaling with predictive models and custom resources
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DOI：10.1016/j.compeleceng.2024.109419.png)
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摘要
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Kubernetes-based containerized applications heavily rely on distributing network workloads among cluster applications, primarily because of the frequent resource requests and limited set of pods and containers. Kubernetes scaling manages many containerized services using either reactive or proactive autoscaling. Reactive autoscaling cannot foresee future workload and thus cannot compete with proactive autoscaling. In addition to this, reactive autoscaling has several quality of service issues such as high latency, inability to manage workload fluctuations frequently, and insufficient service resource usage. To address these issues, the custom resource utilizes a predictive artificial intelligence scaling method, including Autoregressive Integrated Moving Average (ARIMA), Long Short -Term Memory (LSTM), Bidirectional Long Short -Term Memory (Bi-LSTM), and Transformer models. This custom resource is integrated with the operator reconciliation process and the model control system, utilizing coroutines to asynchronously manage workloads and allocate resources as pods within clusters. To evaluate effectiveness, the NASA-HTTP dataset is utilized to scale containerization as an application of resources and assess the accuracy of under -provisioning and over -provisioning as well in resource management. According to the model control system, the Transformer result predictive model ranks among the top min -heap models, exhibiting the lowest performance metric values compared to other predictive models. Specifically, its mean squared error is 77.3363, its root mean squared error is 8.7941, and its mean absolute error is 6.5930. Finally, facilitated through Docker based resource management, integrated with Kubernetes, this service significantly manages workload and enhances resource utilization, efficiency, and performance using the proposed model. Incorporating Explainable artificial intelligence into the system improves clarity and comprehension of the decision -making process of predictive models, especially in model control and operator reconciliation.
Keyword:
Custom resource definition
Cloud computing
Docker
Kubernetes
Operator reconciliation process
Proactive auto-scaling
Resource allocation
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4.9
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6.7K
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