Hierarchical decomposition method and combination forecasting scheme for access load on public map service platforms
Hierarchical decomposition method and combination forecasting scheme for access load on public map service platforms
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公共地图服务平台访问负载分层分解方法及组合预测方案
DOI：
10.1016/j.future.2018.03.031
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发表时间：
2018-10
影响因子：
7.5
通讯作者：
Jiang Jie
中科院分区：
文献类型：
--
作者：
Li Rui;Liu Zhaohui;Wu Huayi;Li Ru;Dong Guangsheng;Jiang Jie
互联网和移动网络不断发展壮大，公共地图服务已经渗透到人们的日常生活中，从而对服务于海量用户的平台产生了高强度的访问负载。云计算可以用来提高服务性能，但其定价模式是基于需求的商业服务。提前估计用户请求的规模，对于及时满足用户需求，在云中分配合适的资源具有重要意义。在本研究中，我们设计了有效的方法来描述和预测公共地图服务平台(PMSP)的访问负载。我们提出了一种新的层次分解模型来描述访问负载的组成和结构，该模型适应了访问负载的非平稳、可变强度和周期性的特点。在此基础上，提出了一种小波变换-时间序列分解-自回归综合滑动平均(WT-TSD-ARIMA)预测方案，基于单层小波变换、TSD和简单线性预测模型(如ARIMA)的组合，捕获和预测各种不同频率和特征的数据分量。实验结果表明，该预测方案在保持线性预测模型长期预测能力稳定的同时，显著提高了预测性能。该方法有助于PMSP对云计算资源的可靠、自适应、可扩展的实时管理。
The Internet and mobile networks have grown and expanded, and public map services have penetrated into people’s daily lives, thereby generating high-intensity access loads on the platforms that service massive amounts of users. Cloud computing can be used to improve the service performance, but its pricing mode is demand-based as a commercial service. Estimating the scale of user requests in advance is important for meeting the demands of users in a timely manner and allocating appropriate resources in the cloud. In this study, we designed effective methods for describing and predicting the access load on public map service platforms (PMSPs). We propose a novel hierarchical decomposition model for describing the components and structure of access loads, where this model accommodates the non-stationary, variable intensity, and periodic nature of access loads. Furthermore, we propose a wavelet transform–time series decomposition–autoregressive integrated moving average (WT–TSD–ARIMA) prediction scheme to capture and predict data components with various different frequencies and characteristics based on a combination of one-level WT, TSD, and simple linear prediction models (such as ARIMA). Our experimental results show that this prediction scheme significantly improves the prediction performance for linear prediction models while maintaining their stable long-term prediction capacity. The proposed method may facilitate the reliable and adaptive scalable management of cloud computing resources for PMSPs in real time.
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DOI：
10.3724/sp.j.1001.2013.04216
发表时间：
2013-12
期刊：
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影响因子：
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DOI：
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影响因子：
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作者：
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DOI：
10.2139/ssrn.1089403
发表时间：
2007-06
期刊：
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影响因子：
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