Robust-PAC time-critical workflow offloading in edge-to-cloud continuum among heterogeneous resources

Abstract

Abstract Edge-to-cloud continuum connects and extends the calculation from edge side via network to cloud platforms, where diverse workflows go back and forth, getting executed on scheduled calculation resources. To better utilize the calculation resources from all sides, workflow offloading problems have been investigating lately. Most works focus on optimizing constraints like: latency requirements, resource utilization rate limits, and energy consumption bounds. However, the dynamics among the offloading environment have hardly been researched, which easily results in uncertain Quality of Service(QoS) on the user side. Any part of the workload change, resource availability change or network latency could incur dynamics in an offloading environment. In this work, we propose a robust PAC (probably approximately correct) offloading algorithm to address this dynamic issue together with optimization. We train an LSTM-based sequence-to-sequence neural network to learn how to offload workflows in edge-to-cloud continuum. Comprehensive implementations and corresponding comparison against state-of-the-art methods demonstrate the robustness of our proposed algorithm. More specifically, our algorithm achieves better offloading performance regarding dynamic heterogeneous offloading environment and faster adaptation to newly changed environments than fine-tuned state-of-the-art RL-based offloading methods.

Keywords

• Workflow offloading
• Meta learning
• Time-critical
• Robustness
• Heterogeneous resources
• MEC