1 PROBLEM AND MOTIVATION. The run-time performance of modern applications deployed within containers in the cloud critically depends on the amount of provi-.
Nov 20, 2019 · Tania Lorido-Botran, Jose Miguel-Alonso, and Jose A Lozano. A review of autoscaling techniques for elastic applications in cloud environments.
In this paper, we formulate and innovatively apply model-based LQR to perform data-driven adaptive resource allocation in an online setting, for ...
Nov 20, 2019 · Time series forecasting uses historical data to forecast the evolution of a time series to assist decision making in several domains, ...
Linear quadratic regulator for resource-efficient cloud services. Y Park, K Mahadik, RA Rossi, G Wu, H Zhao. Proceedings of the ACM Symposium on Cloud Computing ...
2017. Linear quadratic regulator for resource-efficient cloud services. Y Park, K Mahadik, RA Rossi, G Wu, H Zhao. Proceedings of the ACM Symposium on Cloud ...
Linear Quadratic Regulator for Resource-Efficient Cloud Services. Park, Y., Mahadik, K., Rossi, R., Wu, G., Zhao, H. (Nov. 21, 2019). ACM Symposium on Cloud ...
A novel method using the cloud to implement a variable horizon model predictive controller is presented. In case of sudden long delays and downtime, ...
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Dec 1, 2020 · We introduce a new algorithm, RichID, which learns a near-optimal policy for the RichLQR with sample complexity scaling only with the dimension of the latent ...
Jul 13, 2020 · Linear quadratic regulator (LQR) is one of the most popular frameworks to tackle continuous. Markov decision process tasks. With its funda-.