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

Managing Cold-start in The Serverless Cloud with Temporal Convolutional Networks