Portkey: Hypervisor-assisted container migration in nested cloud environments
Derivative cloud service providers use nesting to provision virtual computational entities (VCE) within VCEs, e.g., containers runtimes within virtual machines. As part of resource management and ensuring application performance, migration of nested containers is an important and useful mechanism. Checkpoint Restore In Userspace (CRIU) is the dominant method for migration, used by Docker and other container technologies. While CRIU works well for container migration from host to host, it suffers from significant increase in resource requirements in nested setups. The overheads are primarily due to the high network virtualization overhead in nested environments. While techniques such as SR-IOV can mitigate the overheads, they require additional hardware features and tight coupling of network endpoints. Based on our insights of network virtualization being the main bottleneck, we present Portkey - a software-based solution for efficient nested container migration that significantly reduces CPU utilization at both the source and destination hosts. Our solution relies on interposing a layer that directly coordinates network IO from within a virtual machine with the hypervisor. A new set of hypercalls provide this interfacing along with a control loop that minimizes the hypercall path usage. Extensive evaluation of our solution shows that Portkey reduces CPU usage by up to 75% and 82% at the source and destination hosts, respectively.
Tue 1 MarDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:15 - 11:35
|Portkey: Hypervisor-assisted container migration in nested cloud environments
|Container-aware I/O Stack: Bridging the Gap between Container Storage Drivers and Solid State Devices
Song Wu Huazhong University of Science and Technology, China, Zhuo Huang Huazhong University of Science and Technology, Pengfei Chen Huazhong University of Science and Technology, Hao Fan Huazhong University of Science and Technology, Shadi Ibrahim Inria, Hai Jin Huazhong University of Science and Technology
|ClusterRR: A Record and Replay Framework for Virtual Machine Cluster
|EOP: Efficient Operator Partition for Deep Learning Inference Over Edge Servers
Yuanjia XU University of Chinese Academy of Sciences; Institute of Software, Chinese Academy of Sciences, Heng WU Institute of Software, Chinese Academy of Sciences, Wenbo ZHANG Institute of Software, Chinese Academy of Sciences; State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Yi HU University of Chinese Academy of Sciences; Institute of Software, Chinese Academy of Sciences
The Zoom room for Session 1 is at https://rochester.zoom.us/j/98375917164?pwd=ZHRvcy85elRVUWtDaGRZQkl6dENTQT09.