# COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

arXiv: https://arxiv.org/abs/2104.14392 · HTML: https://mlsanity.com/paper/2104.14392/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2021.

## Built on by (most cited first, 19 of 19)

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- [GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments](https://mlsanity.com/paper/2112.08916/) (2021)
- [CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework](https://mlsanity.com/paper/2212.03965/) (2022)
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- [CAROL: Confidence-Aware Resilience Model for Edge Federations](https://mlsanity.com/paper/2203.07140/) (2022)
- [Look-Ahead Task Offloading for Multi-User Mobile Augmented Reality in Edge-Cloud Computing](https://mlsanity.com/paper/2305.19558/) (2023)
- [PROMPT: Learning Dynamic Resource Allocation Policies for Network Applications](https://mlsanity.com/paper/2201.07916/) (2022)
- [DRAGON: Decentralized Fault Tolerance in Edge Federations](https://mlsanity.com/paper/2208.07658/) (2022)
- [DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model](https://mlsanity.com/paper/2212.01302/) (2022)
- [EdgeTran: Co-designing Transformers for Efficient Inference on Mobile Edge Platforms](https://mlsanity.com/paper/2303.13745/) (2023)
- [CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments](https://mlsanity.com/paper/2302.05630/) (2023)
- [TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference](https://mlsanity.com/paper/2303.14882/) (2023)
- [MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments](https://mlsanity.com/paper/2205.10642/) (2022)
- [RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments](https://mlsanity.com/paper/2304.04797/) (2023)

## Abstract (arXiv)

Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms.