ClusterRR: A Record and Replay Framework for Virtual Machine Cluster
The Record and Replay (RnR) technology provides the ability to reproduce past execution of systems deterministically. It has many prominent applications, including fault tolerance, security analysis, and failure diagnosis. In system virtualization, previous RnR researches focus on individual VM, including coherent replaying of multi-core systems, reducing performance penalty and storage overhead. However, with the emerging of distributed systems deployed in virtual machine clusters (VMC), the existing RnR technology of individual VM can not meet the requirements of analyzers and developers. The critical challenge for VMC RnR is to maintain the consistency of global states. In this paper, we propose ClusterRR, an RnR framework for VMC. To solve the inconsistency problem, we propose coordination protocols to schedule the record and replay process of VMs. Meanwhile, we employ a Hybrid RnR approach to reduce the performance penalty and storage costs caused by recording network events. Moreover, we implement ClusterRR on QEMU/KVM platform and utilize a network packets retransmission framework to guarantee the reproducibility of VMC replay. Last, we conduct a series of experiments to measure its efficiency and overhead. The results show that ClusterRR would efficiently replay the execution of the whole VMC with instruction-level accuracy.
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.