As application migration to the cloud becomes the mainstream way of application deployment, application run-time management presents a significant need for large-scale workload prediction technology. Existing large-sc...
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ISBN:
(纸本)9798350368567;9798350368550
As application migration to the cloud becomes the mainstream way of application deployment, application run-time management presents a significant need for large-scale workload prediction technology. Existing large-scale workload approaches commonly construct training sets based on all the training samples generated from the original data to generate prediction models. However, due to the similar behavior between different container instances of the microservice application, training and modeling in this way results in a huge number of redundant samples, which produces a significant redundant training overhead. Therefore, this paper proposes Less, a large-scale workload forecasting model based on multiple sequence compression. First, based on the grouping results of similar containers, a container workload feature recognition algorithm is proposed to determine the common and individual features of container workloads in each prediction period, so as to guide the compression of workload sequences within each group;second, a fitness function that takes into account the common features, individual features, and the number of sequences are designed, and the optimal compressed sequences are solved by the Whale Optimization Algorithm to efficiently reduce the number of redundant training workload sequences, and then the Bidirectional Gated Recurrent Unit model is built and trained based on the compressed sequences, which effectively reduces the model complexity and overhead while ensuring the accuracy. Finally, we validate the comprehensive advantages of Less in terms of accuracy and overhead based on public datasets and verify the effectiveness of each subpart of our model through ablation experiments.
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