The Taub Faculty of Computer Science Events and Talks

Containers, originally designed for cloud environments, are increasingly popular for provisioning workers outside the cloud, for example in mobile and edge computing. These settings, however, bring new challenges: high latency links, limited bandwidth, and resource-constrained workers. The result is longer provisioning times when deploying new workers or updating existing ones, much of it due to network traffic. Our analysis shows that current piecemeal approaches to reducing provisioning time are not always sufficient, and can even make things worse as round-trip times grow. Rather, we find that the very same layer-based structure that makes containers easy to develop and use also makes it more difficult to optimize deployment. Addressing this issue thus requires rethinking the container deployment pipeline as a whole. Based on our findings, I will present Starlight: an accelerator for container provisioning. Starlight decouples provisioning from development by redesigning the container deployment protocol, filesystem, and image storage format. Our evaluation using 21 popular containers shows that, on average, Starlight deploys and starts containers 3x faster than the current industry standard implementation while incurring no runtime overhead and negligible storage overhead. Finally, it requires no changes to the deployed application, is backwards compatible with existing workers, and uses standard container registries. Starlight is open source and available at https://github.com/mc256/starlight. Bio: Moshe (Mickey) Gabel is an assistant professor in the Department of Electrical Engineering and Computer Science at York University. Before joining York, he spent four years as a limited-term assistant professor in the Department of Computer Science at the University of Toronto. He earned his PhD in Computer Science from the Technion – Israel Institute of Technology, where he also got his MSc and BSc. Moshe’s research lies in the intersection of distributed algorithms, systems, and machine learning. His current research interest is edge computing, specifically making geo-distributed data analysis more practical and accessible to typical software developers. He has also worked extensively on machine learning applications in pervasive health monitoring and in computer systems. Moshe’s work appeared in top venues for systems and data science, including SIGMOD, NSDI, SIGKDD, VLDB, and ICML.