领域对抗自适应的短任务负载预测模型

doi:10.3778/j.issn.1002-8331.2211-0036

摘要/Abstract

摘要： 负载预测的精度是影响云平台弹性资源管理的主要因素之一。而云平台中存在着大量的短任务负载序列，其历史信息不足和不平滑的特性导致难以选择合适的模型进行精准预测。对此提出了一种领域对抗自适应的短任务负载预测模型。该模型采用奇异谱分析（singular spectrum analysis，SSA）对样本进行平滑处理；联合第四版本的Mueen相似度搜索算法（the fourth version of Mueen’s algorithm for similarity search，MASS_V4）与时间特征进行域间相似性计算，获得合适的源域数据来辅助迁移预测；将门控循环单元（gated recurrent unit，GRU）作为基准器构建网络，并利用Y差异定义新的损失函数，通过对抗过程建立出表征能力强的短任务负载预测模型。将所提方法在两个真实的云平台数据集上与其他常用的云负载预测算法对比，均表现出较高的预测精度。

关键词: 云计算, 负载预测, 域对抗迁移学习, MASS_V4

Abstract: The accuracy of workload prediction is one of the main factors affecting the elastic resource management of cloud platforms. And there are a large number of short task workload sequences with insufficient historical information and unsmooth characteristics in the cloud, which makes it difficult to select appropriate models for accurate prediction. In this paper, a domain adversarial workload prediction model is proposed. The model uses SSA（singular spectrum analysis） to smooth the workload and solve the problem of irregularity. Similarity calculations are performed by combining MASS_V4（the fourth version of Mueen’s algorithm for similarity search） with temporal features to obtain suitable source-domain data-assisted migration prediction. The GRU（gated recurrent unit） is used as the reference to construct the network, a new loss function defined is with Y-discrepancy, and a prediction model is constructed with strong short-workload feature representation ability in the adversarial process. The proposed method is compared with other commonly used cloud workload prediction algorithms on two real cloud platform datasets and both show higher prediction accuracy.

Key words: cloud computing, workload prediction, domain antagonism transfer learning, the fourth version of Mueen’s algorithm for similarity search（MASS_V4）

刘春红, 焦洁, 王敬雄, 李为丽, 张俊娜. 领域对抗自适应的短任务负载预测模型[J]. 计算机工程与应用, 2023, 59(24): 289-297.

LIU Chunhong, JIAO Jie, WANG Jingxiong, LI Weili, ZHANG Junna. Domain Adversarial Adaptive Short-Term Workload Forecasting Model[J]. Computer Engineering and Applications, 2023, 59(24): 289-297.

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