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drl-cloud-management-list

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A list of DRL cloud management papers.

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## DRL in Cloud Computing

List of publications about autonomous management of cloud computing resources.

Keywords:

* Cloud Computing
* Deep Reinforcement Learning
* Autonomous Management
* Autonomic Managenent
* Resource Provisioning

## Papers list

### 2021

* Jose Santos, Tim Wauters, Bruno Volckaert and Filip De Turck: "Resource Provisioning in Fog Computing through Deep Reinforcement Learning"
  * http://dl.ifip.org/db/conf/im/im2021mini/210942.pdf
  * source code: https://github.com/jpedro1992/gym-fog
  * keywords: simulation, Open AI Gym, fog computing, DQN, MILP
  * use-case:

* Guangyao Zhou, Wenhong Tian, Rajkumar Buyya "Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions"
  * https://arxiv.org/pdf/2105.04086.pdf
  * keywords: review paper

* S. Kardani-Moghaddam, R. Buyya, and K. Ramamohanarao, “ADRL: A hybrid anomaly-aware deep reinforcement learning-based resource scaling in clouds,” IEEE Trans. Parallel Distributed Syst., vol. 32, no. 3, pp. 514–526, 2021.
  * keywords: DQN, CloudSim, simulation, ebay workload
  * use-case: cloud resources scaling (both vertical and horizontal)

#### to read

* Zhenjie Yao, Lan Chen, He Zhang: "Deep Reinforcement Learning for Job Scheduling on Cluster"
  * https://link.springer.com/chapter/10.1007%2F978-3-030-86380-7_50
  * source code: 
  * keywords: 
  * use-case:

* Hye-Young Kim: "An Efficient Load Balancing Scheme for Gaming Server Using Proximal Policy Optimization Algorithm "
  * http://jips-k.org/digital-library/2021/17/2/297
* X. Zhou, R. Wang, Y. Wen and R. Tan, "Joint IT-Facility Optimization for Green Data Centers via Deep Reinforcement Learning," in IEEE Network, doi: 10.1109/MNET.011.2100101.
* Junxiong Wang, Immanuel Trummer, Debabrota Basu: "UDO: Universal Database Optimization using Reinforcement Learning"
  * https://arxiv.org/pdf/2104.01744.pdf 
* Yisel Garí, David A. Monge, Elina Pacini, Cristian Mateos, Carlos García Garino, Reinforcement learning-based application Autoscaling in the Cloud: A survey, Engineering Applications of Artificial Intelligence, Volume 102, 2021, 104288, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2021.104288.
  * https://www.sciencedirect.com/science/article/abs/pii/S0952197621001354
  * keywords: 
* Zong, Qing and Zheng, Xiangwei and Wei, Yi and Sun, Hongfeng: "A Deep Reinforcement Learning Based Resource Autonomic Provisioning Approach for Cloud Services"
  * keywords:
* Tania Lorido-Botran, Muhammad Khurram Bhatti "Adaptive Container Scheduling in Cloud Data Centers: A Deep Reinforcement Learning Approach"
  * keywords:
* Tania Lorido-Botran, Muhammad Khurram Bhatti: "ImpalaE: Towards an optimal policy for efficient resource management at the edge"
  * http://ceur-ws.org/Vol-2850/paper5.pdf
  * keywords:
* Qin, Y., Wang, H., Yi, S. et al. A multi-objective reinforcement learning algorithm for deadline constrained scientific work
ow scheduling in clouds. Front. Comput. Sci. 15, 155105 (2021). https://doi.org/10.1007/s11704-020-9273-z
  * keywords:
* N. Zhao, H. Wu, F. R. Yu, L. Wang, W. Zhang and V. C. M. Leung, "Deep Reinforcement Learning-Based Latency Minimization in Edge Intelligence over Vehicular Networks," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3078480.
  * keywords:
* Yuping Fan, Zhiling Lan, DRAS-CQSim: A reinforcement learning based framework for HPC cluster scheduling, Software Impacts, Volume 8, 2021, 100077, ISSN 2665-9638, https://doi.org/10.1016/j.simpa.2021.100077.
  * keywords:
* Nabi, S., Ahmed, M. OG-RADL: overall performance-based resource-aware dynamic load-balancer for deadline constrained Cloud tasks. J Supercomput (2021). https://doi.org/10.1007/s11227-020-03544-z
  * keywords:
* Ahmed Yassine Kamri, Pham Tran Anh Quang, Nicolas Huin, Jeremie Leguay: "Constrained Policy Optimization for Load Balancing"
  * http://jeremie.leguay.free.fr/files/DRCN_DRL-LB_2021.pdf
  * keywords:
* Zhuangwei Kang, Yogesh D. Barve, Shunxing Bao, Abhishek Dubey, Aniruddha Gokhale: "Configuration Tuning for Distributed IoT Message Systems Using Deep Reinforcement Learning", Conference on Internet-of-Things Design and Implementation (IoTDI ’21), May 18–21, 2021, Charlottesvle, VA, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3450268.3453517
  * https://scopelab.ai/files/iotdi21kang.pdf
  * keywords:
* Verma, S., Bala, A. Auto-scaling techniques for IoT-based cloud applications: a review. Cluster Comput (2021). https://doi.org/10.1007/s10586-021-03265-9
  * keywords:
* Ahmad, W., Alam, B. & Atman, A. An energy-efficient big data workflow scheduling algorithm under budget constraints for heterogeneous cloud environment. J Supercomput (2021). https://doi.org/10.1007/s11227-021-03733-4
  * keywords:
* J. Du et al., "Resource Pricing and Allocation in MEC Enabled Blockchain Systems: An A3C Deep Reinforcement Learning Approach," in IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2021.3068340.
  * keywords:
* Santos, G.L., Lynn, T., Kelner, J. et al. Availability-aware and energy-aware dynamic SFC placement using reinforcement learning. J Supercomput (2021). https://doi.org/10.1007/s11227-021-03784-7
  * keywords:
* W. Chen, X. Qiu, T. Cai, H. -N. Dai, Z. Zheng and Y. Zhang, "Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey," in IEEE Communications Surveys & Tutorials, doi: 10.1109/COMST.2021.3073036.
  * keywords:
* Shuaibing Lu, Jie Wu, Ning Wang, Yubin Duan, Haiming Liu, Jiayue Zhang, Juan Fang: Resource Provisioning in Collaborative Fog Computing for Multiple Delay-Sensitive Users
  *  https://cis.temple.edu/~jiewu/research/publications/Publication_files/lsb2021spe.pdf
  * keywords:
* Miaojiang Chena, Tian Wangb, Shaobo Zhangc, Anfeng Liua: "Deep reinforcement learning for computation offloading in mobile edge computing environment"
  * https://www.sciencedirect.com/science/article/abs/pii/S0140366421001729

### 2020

* Wenxia Guo; Wenhong Tian; Yufei Ye; Lingxiao Xu; Kui Wu: Cloud Resource Scheduling with Deep Reinforcement Learning and Imitation Learning
  * keywords: imitation learning, CNN, simulation
  * use-case: allocating jobs in a cluster

* N. Shan, X. Cui, and Z. Gao, “"drl + fl": An intelligent resource allocation model based on deep reinforcement learning for mobile edge computing,” Comput. Commun., vol. 160, pp. 14–24, 2020.
  * keywords: Double DQN, DQN, Federal Learning, 
  * use-case: allocate resources in a MEC (Mobile Edge Computing) environment, minimize energy consumption, service delay

* M. Li, F. R. Yu, P. Si, W. Wu, and Y. Zhang, “Resource optimization for delaytolerant data in blockchain-enabled iot with edge computing: A deep reinforcement learning approach,” IEEE Internet Things J., vol. 7, no. 10, pp. 9399–9412,
  * keywords: Dueling DQN, simulation
  * use-case: optimizing caching and data processing in an edge computing env

* Sisheng Liang, Zhou Yang, Fang Jin, Yong Chen: "Data Centers Job Scheduling with Deep Reinforcement Learning"
  * link: https://link.springer.com/chapter/10.1007/978-3-030-47436-2_68
  * keywords: Advantage Actor-critic (A2C), simulation test (using synthetic and pre-collected traces)
  * use-case: schedule processing jobs in a data-center

#### to read

* Funika, Wlodzimierz and Koperek, Pawel and Kitowski, Jacek: "Automatic Management of Cloud Applications with Use of Proximal Policy Optimization"
  * keywords:
* Funika, Wlodzimierz and Koperek, Pawel: Evaluating the Use of Policy Gradient Optimization Approach for Automatic Cloud Resource Provisioning"
  * keywords:
* Luca Caviglione and Mauro Gaggero and Massimo Paolucci and Roberto Ronco"Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters"
  * keywords:
* Zhiping Peng and Jianpeng Lin and Delong Cui and Qirui Li and Jieguang He "A multi-objective trade-off framework for cloud resource scheduling based on the Deep Q-network algorithm"
  * keywords:
* Zhang, S., et al. "A-SARSA: a predictive container auto-scaling algorithm based on reinforcement learning."
  * keywords:
* J. Santos, T. Wauters, B. Volckaert, and F. De Turck, “Reinforcement learning for service function chain allocation in fog computing,” Book Chapter in revision, Submitted to Communications Network and Service Management In the Era of Artificial Intelligence and Machine Learning, IEEE Press, 2020.
  * keywords:
* I. John, A. Sreekantan and S. Bhatnagar, "Auto-scaling Resources for Cloud Applications using Reinforcement learning," 2019 Grace Hopper Celebration India (GHCI), 2019, pp. 1-5, doi: 10.1109/GHCI47972.2019.9071835.
  * keywords:
* S. Deng et al., "Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems: A Reinforcement Learning Method," in IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 6103-6113, Sept. 2020, doi: 10.1109/TII.2020.2974875.
  * keywords:
* Hongshuai Ren, Yang Wang, Chengzhong Xu, Xi Chen: "SMig-RL: An Evolutionary Migration Framework for Cloud Services Based on Deep Reinforcement Learning"
  * link: https://dl.acm.org/doi/abs/10.1145/3414840
  * keywords:

### 2018

* Bingqian, D., Chuan, W., Huang, Z.: Learning resource allocation and pricing for cloud profit maximization. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33(01), pp. 7570–7577 (2019)
  * https://i.cs.hku.hk/~zhiyi/publications/DHW19-AAAI.pdf
  * keywords: DDPG, LSTM, DRL, simulation (?)
  * use-case: maximize service provider profit

* Cheng, Mingxi and Li, Ji and Nazarian, Shahin "DRL-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers"
  * keywords: DQN, simulation
  * use-case: allocating VMs to servers

* C. Bitsakos, I. Konstantinou, and N. Koziris, “DERP: A deep reinforcement learning cloud system for elastic resource provisioning,” in 2018 IEEE International
Conference on Cloud Computing Technology and Science, CloudCom 2018, Nicosia,
Cyprus, December 10-13, 2018. IEEE Computer Society, 2018, pp. 21–29.
  * keywords: MLP, DQN, Double DQN, simulation, real-world environment
  * use-case: adding/removing VMs
  * link: https://www.researchgate.net/publication/329959054_DERP_A_Deep_Reinforcement_Learning_Cloud_System_for_Elastic_Resource_Provisioning

* Chen, Li and Lingys, Justinas and Chen, Kai and Liu, Feng "AuTO: Scaling Deep Reinforcement Learning for Datacenter-Scale Automatic Traffic Optimization"
  * https://conferences.sigcomm.org/events/apnet2018/papers/auto.pdf
  * keywords: real-world testbed test, DRL, DDPG, actor-critic
  * use-case: load balancing, traffic/flow (web search and data mining requests) optimization

#### to read

* H. Liu and Shiwen Liu and K. Zheng "A Reinforcement Learning-Based Resource Allocation Scheme for Cloud Robotics"
  * keywords:
* Zhang, Q., et al.: A double deep Q-learning model for energy-efficient edge scheduling. IEEE Trans. Serv. Comput. 12(5), 739–749 (2018)
  * keywords:

### 2017

* Wang, Zhiguang and Gwon, Chul and Oates, Tim and Iezzi, Adam: "Automated Cloud Provisioning on AWS using Deep Reinforcement Learning"
  * https://arxiv.org/abs/1709.04305
  * keywords: DQN, D3QN, Q-learning, simulator, real cloud, AWS
  * use-case: provision resources for a workload, minimize cost

* Y. Zhang and J. Yao and H. Guan "Intelligent Cloud Resource Management with Deep Reinforcement Learning"
  * keywords: DQN, SAQN, Q-learning, AWS, evaluation on 4 servers, system trained in the live environment
  * use-case: allocate resources for tasks

* N. Liu, Z. Li, J. Xu, Z. Xu, S. Lin, Q. Qiu, J. Tang, and Y. Wang, “A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning,” in 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, Atlanta, GA, USA, June 5-8, 2017. IEEE Computer Society, 2017, pp. 372–382.
  * keywords: continuous-time Q-learning for SMDP (DQN derived) , baseline comparison: round-robin, simulation
  * use-case: power consumption and energy usage optimization without significantly degrading service latency (action definition: `action at each decision epoch is simply the target server for VM allocation,`)
  
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