Pygration : Workload-Aware Live Migratable Cloud Instance Detector for Python Runtime

Abstract

Supporting live migration in the cloud can be beneficial to dynamically build a reliable and cost-optimal environment, especially when using spot instances. When a spot instance interruption event occurs, users can apply live migration using the Checkpoint/Restore In Userspace (CRIU) to a more reliable instance. In the process of migration, ensuring the compatibility of the CPU features between the source and target hosts is crucial for flawless execution after migration. However, the standard approach implemented by CRIU is workload-agnostic and overly conservative, comparing the full CPU feature sets of the hosts, which results in unnecessarily restricting the pool of viable migration targets. To mitigate this limitation, a workload-aware analysis can be employed to identify the precise set of CPU features that an application utilizes at runtime. However, it can be very challenging for Python workloads due to their layers of abstraction between bytecode and invoked native libraries, obscuring the true hardware dependencies. To overcome the challenge, this paper presents Pygration, a novel workload-aware migratable cloud instance detection system for Python runtimes. Pygration implements a hybrid analysis pipeline that combines Python bytecode tracking to build a precise call graph with a native code execution path tracking heuristic to identify the minimal set of required CPU features. A comprehensive evaluation on 522 AWS instance types shows that Pygration achieves perfect precision while improving recall by over 5× compared to the CRIU baseline. In a practical spot instance scenario, this increased recall translates to a 16% improvement in median cost savings while enhancing reliability.