DeepScaling

Author:

Wang Ziliang1,Zhu Shiyi2,Li Jianguo2,Jiang Wei2,Ramakrishnan K. K.3,Zheng Yangfei2,Yan Meng1,Zhang Xiaohong1,Liu Alex X.2

Affiliation:

1. Chongqing University, Chongqing, China

2. Ant Group, Hangzhou, China

3. University of California

Funder

the Natural Science Foundation of Chongqing

the research fund from Ant Group

the Postdoc Foundation of Chongqing

the National Key Research and Development Project

Publisher

ACM

Reference47 articles.

1. Muhammad Abdullah , Waheed Iqbal , Josep Lluis Berral, et al . 2020 . Burst-aware predictive autoscaling for containerized microservices. IEEE Transactions on Services Computing ( 2020). Muhammad Abdullah, Waheed Iqbal, Josep Lluis Berral, et al. 2020. Burst-aware predictive autoscaling for containerized microservices. IEEE Transactions on Services Computing (2020).

2. Muhammad Abdullah , Waheed Iqbal , and Faisal Bukhari . 2018 . Containers vs virtual machines for auto-scaling multi-tier applications under dynamically increasing workloads . In International Conference on Intelligent Technologies and Applications. 153--167 . Muhammad Abdullah, Waheed Iqbal, and Faisal Bukhari. 2018. Containers vs virtual machines for auto-scaling multi-tier applications under dynamically increasing workloads. In International Conference on Intelligent Technologies and Applications. 153--167.

3. Rie Kubota Ando and Tong Zhang. 2007. Learning on graph with Laplacian regularization. In Advances in neural information processing systems (NIPS). Rie Kubota Ando and Tong Zhang. 2007. Learning on graph with Laplacian regularization. In Advances in neural information processing systems (NIPS).

4. A mobile crowd sensing ecosystem enabled by CUPUS: Cloud-based publish/subscribe middleware for the Internet of Things

5. AWS. 2022 . AWS auto scaling documentation . Retrieved June, 2022 from https://docs.aws.amazon.com/autoscaling/index.html AWS. 2022. AWS auto scaling documentation. Retrieved June, 2022 from https://docs.aws.amazon.com/autoscaling/index.html

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1. TopFull: An Adaptive Top-Down Overload Control for SLO-Oriented Microservices;Proceedings of the ACM SIGCOMM 2024 Conference;2024-08-04

2. Graph Neural Network-Based SLO-Aware Proactive Resource Autoscaling Framework for Microservices;IEEE/ACM Transactions on Networking;2024-08

3. Bayesian-Driven Automated Scaling in Stream Computing With Multiple QoS Targets;IEEE Transactions on Parallel and Distributed Systems;2024-07

4. DCScaler: Spatiotemporal Prediction Aided Distributed Collaborative Autoscaling of Microservices;2024 IEEE 10th International Conference on Edge Computing and Scalable Cloud (EdgeCom);2024-06-28

5. SPRIGHT: High-Performance eBPF-Based Event-Driven, Shared-Memory Processing for Serverless Computing;IEEE/ACM Transactions on Networking;2024-06

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