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

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.

Cybonto: Towards Human Cognitive Digital Twins for Cybersecurity

Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response to the expanding ever-more-complex threat landscape, Digital Twins (DTs) and particularly Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple knowledge domains. Simulated results may offer insights into adversaries' behaviors and tactics, resulting in better proactive cyber-defense strategies. For the first time, this paper solidifies the vision of DTs and HDTs for cybersecurity via the Cybonto conceptual framework proposal. The paper also contributes the Cybonto ontology, formally documenting 108 constructs and thousands of cognitive-related paths based on 20 time-tested psychology theories. Finally, the paper applied 20 network centrality algorithms in analyzing the 108 constructs. The identified top 10 constructs call for extensions of current digital cognitive architectures in preparation for the DT future.