# MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments

arXiv: https://arxiv.org/abs/2205.10642 · HTML: https://mlsanity.com/paper/2205.10642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 19 of 19)

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- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
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- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [Algorithms for multi-armed bandit problems](https://mlsanity.com/paper/1402.6028/) (2014)
- [COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments](https://mlsanity.com/paper/2104.14392/) (2021)
- [Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks](https://mlsanity.com/paper/2009.02186/) (2020)
- [Temporal Locality in Today's Content Caching: Why it Matters and How to Model it](https://mlsanity.com/paper/1305.7114/) (2013)
- [Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges](https://mlsanity.com/paper/1911.01941/) (2019)
- [PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing](https://mlsanity.com/paper/2112.02292/) (2021)
- [FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing](https://mlsanity.com/paper/1811.11978/) (2018)
- [HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing](https://mlsanity.com/paper/2110.05529/) (2021)
- [GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments](https://mlsanity.com/paper/2112.08916/) (2021)
- [MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems](https://mlsanity.com/paper/2112.07269/) (2021)
- [CAROL: Confidence-Aware Resilience Model for Edge Federations](https://mlsanity.com/paper/2203.07140/) (2022)
- [EdgeLens: Deep Learning based Object Detection in Integrated IoT, Fog and Cloud Computing Environments](https://mlsanity.com/paper/1906.11056/) (2019)

## Abstract (arXiv)

Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedulers is the execution cost. In this regard, several data-driven deep neural networks (DNNs) based schedulers have been proposed in recent years to allow scalable and efficient resource management in dynamic workload settings. However, optimal scheduling frequently relies on sophisticated DNNs with high computational needs implying higher execution costs. Further, even in non-stationary environments, sophisticated schedulers might not always be required and we could briefly rely on low-cost schedulers in the interest of cost-efficiency. Therefore, this work aims to solve the non-trivial meta problem of online dynamic selection of a scheduling policy using a surrogate model called MetaNet. Unlike traditional solutions with a fixed scheduling policy, MetaNet on-the-fly chooses a scheduler from a large set of DNN based methods to optimize task scheduling and execution costs in tandem. Compared to state-of-the-art DNN schedulers, this allows for improvement in execution costs, energy consumption, response time and service level agreement violations by up to 11, 43, 8 and 13 percent, respectively.