Serverless cloud is an innovative cloud service model that frees customers from most cloud management duties. It also offers the same advantages as other cloud models but at much lower costs. As a result, the serverless cloud has been increasingly employed in high-impact areas such as system security, banking, and health care. A big threat to the serverless cloud's performance is cold-start, which is when the time of provisioning the needed cloud resource to serve customers' requests incurs unacceptable costs to the service providers and/or the customers. This paper proposes a novel low-coupling, high-cohesion ensemble policy that addresses the cold-start problem at infrastructure- and function-levels of the serverless cloud stack, while the state of the art policies have a more narrowed focus. This ensemble policy anchors on the prediction of function instance arrivals, 10 to 15 minutes into the future. It is achievable by using the temporal convolutional network (TCN) deep-learning method. Bench-marking results on a real-world dataset from a large-scale serverless cloud provider show that TCN out-performs other popular machine learning algorithms for time series. Going beyond cold-start management, the proposed policy and publicly available codes can be adopted in solving other cloud problems such as optimizing the provisioning of virtual software-defined network assets.

翻译：无服务器云是一种创新的云服务模型，可免除用户大部分云管理职责，同时以更低成本提供与其他云模型相同的优势。因此，无服务器云已越来越多地应用于系统安全、银行和医疗等关键领域。无服务器云性能面临的重大威胁是冷启动问题，即供应所需云资源以服务用户请求的时间会为服务提供商和/或用户带来不可接受的成本。本文提出了一种新颖的低耦合、高内聚集成策略，在无服务器云堆栈的基础设施层和函数层解决冷启动问题，而现有技术策略的关注范围更为狭窄。该集成策略基于对未来10至15分钟函数实例到达的预测，可通过使用时序卷积网络（TCN）深度学习方法实现。基于大规模无服务器云提供商真实数据集的基准测试结果表明，TCN在时间序列预测中优于其他主流机器学习算法。除冷启动管理外，所提出的策略及公开代码还可用于解决其他云问题，例如优化虚拟软件定义网络资产的供应。