# CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments

arXiv: https://arxiv.org/abs/2302.05630 · HTML: https://mlsanity.com/paper/2302.05630/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 75 references that are in the map.
- First posted on arXiv 11 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction](https://mlsanity.com/paper/1703.01030/) (2017)
- [COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments](https://mlsanity.com/paper/2104.14392/) (2021)
- [PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing](https://mlsanity.com/paper/2112.02292/) (2021)
- [HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing](https://mlsanity.com/paper/2110.05529/) (2021)
- [DRAGON: Decentralized Fault Tolerance in Edge Federations](https://mlsanity.com/paper/2208.07658/) (2022)
- [An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers](https://mlsanity.com/paper/1712.10114/) (2017)

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review](https://mlsanity.com/paper/2501.01007/) (2025)

## Abstract (arXiv)

Intelligent Virtual Machine (VM) provisioning is central to cost and resource efficient computation in cloud computing environments. As bootstrapping VMs is time-consuming, a key challenge for latency-critical tasks is to predict future workload demands to provision VMs proactively. However, existing AI-based solutions tend to not holistically consider all crucial aspects such as provisioning overheads, heterogeneous VM costs and Quality of Service (QoS) of the cloud system. To address this, we propose a novel method, called CILP, that formulates the VM provisioning problem as two sub-problems of prediction and optimization, where the provisioning plan is optimized based on predicted workload demands. CILP leverages a neural network as a surrogate model to predict future workload demands with a co-simulated digital-twin of the infrastructure to compute QoS scores. We extend the neural network to also act as an imitation learner that dynamically decides the optimal VM provisioning plan. A transformer based neural model reduces training and inference overheads while our novel two-phase decision making loop facilitates in making informed provisioning decisions. Crucially, we address limitations of prior work by including resource utilization, deployment costs and provisioning overheads to inform the provisioning decisions in our imitation learning framework. Experiments with three public benchmarks demonstrate that CILP gives up to 22% higher resource utilization, 14% higher QoS scores and 44% lower execution costs compared to the current online and offline optimization based state-of-the-art methods.