PRNN: Piecewise Recurrent Neural Networks for Predicting the Tendency of Services Invocation
PRNN: Piecewise Recurrent Neural Networks for Predicting the Tendency of Services Invocation
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DOI：
10.1109/icws.2018.00013
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Haozhe Lin;Yushun Fan;Jia Zhang;Bing Bai
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文献类型：
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作者：
Haozhe Lin;Yushun Fan;Jia Zhang;Bing Bai
在面向服务的体系结构（SOA）广泛应用的推动下，web服务的数量和用户数量在服务生态系统中不断增加。由于服务是由服务提供者托管的，因此预测服务提供者的服务调用趋势将非常有帮助，以便可以采取适当的操作来确保服务质量。然而，在预测服务调用的趋势方面存在两个主要挑战。首先，不同的服务调用序列可能具有不同且复杂的特征，难以进行一般建模。其二，服务调用序列之间错综复杂的关系虽有价值，但难以区分和利用。为了解决这些问题，我们开发了一种深度神经网络，称为分段递归神经网络（PRNN），它同时考虑了普遍性和针对性。为了提高通用性，PRNN通过LSTM单元提取所有服务调用序列的复杂特征。为了具有针对性，PRNN开发了一种分段机制，通过该机制可以自动聚类服务调用序列并进行判别预测。在实际数据集中的大量实验表明，PRNN在预测服务调用趋势方面优于基线方法。
Driven by the widespread application of Service-Oriented Architecture (SOA), the quantity of web services and their users keeps increasing in the service ecosystem. Since services are hosted by service providers, it will be very helpful to predict the tendency of services invocation for service providers, so that proper actions may be taken to ensure the quality of services. Two major challenges exist in predicting the tendency of services invocation, however. First, different service invocation sequences may bear different and complicated characteristics, which is hard to be modeled generally. Second, the intricate relations between service invocation sequences are valuable but hard to be discriminated and utilized. To address these issues, a deep neural network, named Piecewise Recurrent Neural Network (PRNN), is developed by taking both generality and pertinence into consideration. For generality, PRNN extracts complicated characteristics of all service invocation sequences through Long Short-Term Memory (LSTM) units. For pertinence, PRNN develops a piecewise mechanism, through which service invocation sequences can be clustered automatically and predicted discriminatingly. Extensive experiments in real-world dataset show that PRNN outperforms baseline methods in predicting the tendency of services invocation.