# Performance Optimization for Edge-Cloud Serverless Platforms via Dynamic Task Placement

arXiv: https://arxiv.org/abs/2003.01310 · HTML: https://mlsanity.com/paper/2003.01310/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions](https://mlsanity.com/paper/2106.12739/) (2021)
- [DeF-DReL: Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning](https://mlsanity.com/paper/2110.15702/) (2021)
- [Managing Cold-start in The Serverless Cloud with Temporal Convolutional Networks](https://mlsanity.com/paper/2304.00396/) (2023)
- [Cloud Workflow Scheduling Based on Graph Attention-Driven Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2609.14952/) (2026)

## Abstract (arXiv)

We present a framework for performance optimization in serverless edge-cloud platforms using dynamic task placement. We focus on applications for smart edge devices, for example, smart cameras or speakers, that need to perform processing tasks on input data in real to near-real time. Our framework allows the user to specify cost and latency requirements for each application task, and for each input, it determines whether to execute the task on the edge device or in the cloud. Further, for cloud executions, the framework identifies the container resource configuration needed to satisfy the performance goals. We have evaluated our framework in simulation using measurements collected from serverless applications in AWS Lambda and AWS Greengrass. In addition, we have implemented a prototype of our framework that runs in these same platforms. In experiments with our prototype, our models can predict average end-to-end latency with less than 6% error, and we obtain almost three orders of magnitude reduction in end-to-end latency compared to edge-only execution.