On Learning Adaptive Service Compositions
On Learning Adaptive Service Compositions
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DOI：
10.1007/s11518-021-5498-0
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发表时间：
2021-08
影响因子：
1.2
通讯作者：
Ahmed Moustafa
中科院分区：
文献类型：
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作者：
Ahmed Moustafa
服务组合是一种重要而有效的技术，它使原子服务能够组合在一起，形成更强大的服务，即组合服务。随着互联网的普及和互联计算设备的激增，服务组合必须包含自适应服务提供的观点。强化学习已经成为在开放和动态的环境中组合和适应Web服务的有力工具。然而，最常见的强化学习算法在使用交互体验数据方面效率相对较低，当部署到云环境中时，可能会影响学习过程的稳定性。特别是，他们只为每个互动体验进行一次学习更新。本文介绍了一种新的方法，旨在通过保存经验数据并将其用于对学习的策略进行更新来实现更高的数据效率。该方法设计了一种云服务组合的离线学习方案，将在线学习任务转化为一系列有监督的学习任务。为了在不同的策略和不同的场景下促进和支持云中高效的服务组合，在该方案下提出了一套算法。实验结果表明，与在线学习方法相比，该方法能够有效地组合和适配云服务，特别是在动态环境下。
Service composition is an important and effective technique that enables atomic services to be combined together to forma more powerful service, i.e., a composite service. With the pervasiveness of the Internet and the proliferation of interconnected computing devices, it is essential that service composition embraces an adaptive service provisioning perspective. Reinforcement learning has emerged as a powerful tool to compose and adapt Web services in open and dynamic environments. However, the most common applications of reinforcement learning algorithms are relatively inefficient in their use of the interaction experience data, whichmay affect the stability of the learning process when deployed to cloud environments. In particular, they make just one learning update for each interaction experience. This paper introduces a novel approach that aims to achieve greater data efficiency by saving the experience data and using it in aggregate to make updates to the learned policy. The proposed approach devises an offline learning scheme for cloud service composition where the online learning task is transformed into a series of supervised learning tasks. A set of algorithms is proposed under this scheme in order to facilitate and empower efficient service composition in the cloud under various policies and different scenarios. The results of our experiments show the effectiveness of the proposed approach for composing and adapting cloud services, especially under dynamic environment settings, compared to their online learning counterparts.