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.

该论文提出了一种新的低耦合、高内聚的集成策略以解决无服务器云堆栈基础结构和功能级别的冷启动问题，并提出了一种基于时间卷积网络（TCN）的深度学习方法，用于预测未来 10 至 15 分钟内功能实例的到来，并在实际数据集上进行了基准测试，证明 TCN 在时序机器学习算法中表现出更好的性能。