Resilient Cloud Control System: Dynamic Frequency Adaptation via Q-learning
Akbarian, Fatemeh LU ; Tärneberg, William LU ; Fitzgerald, Emma LU
and Kihl, Maria
LU
(2024)
27th Conference on Innovation in Clouds, Internet and Networks, ICIN
- Abstract
- Traditional control systems face challenges in managing high data loads and computing power, prompting the evolution of Cloud Control Systems (CCS)-a fusion of Networked Control Systems (NCS) and cloud computing. Despite offering manifold advantages, CCS encounters hurdles in navigating the dynamic cloud environment characterized by fluctuating workloads, rendering static frequency settings inefficient. Moreover, the optimal utilization of cloud resources poses a pivotal challenge within CCS operations. To address these, the paper proposes a resilient CCS by adapting system frequency dynamically. Leveraging Q-learning, the approach measures Round Trip Time (RTT) and system output errors, dynamically adjusting the system's frequency to... (More)
- Traditional control systems face challenges in managing high data loads and computing power, prompting the evolution of Cloud Control Systems (CCS)-a fusion of Networked Control Systems (NCS) and cloud computing. Despite offering manifold advantages, CCS encounters hurdles in navigating the dynamic cloud environment characterized by fluctuating workloads, rendering static frequency settings inefficient. Moreover, the optimal utilization of cloud resources poses a pivotal challenge within CCS operations. To address these, the paper proposes a resilient CCS by adapting system frequency dynamically. Leveraging Q-learning, the approach measures Round Trip Time (RTT) and system output errors, dynamically adjusting the system's frequency to minimize control costs, optimize performance within the dynamic cloud environment, and achieve resource frugality, minimizing resource usage. Through real testbed experiments, this paper evaluates and analyzes the effectiveness of the proposed method, aiming to establish an adaptive and efficient control framework aligned with evolving cloud dynamics. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/bbc1f526-ea47-42b7-9f20-c02bfceb106a
- author
- Akbarian, Fatemeh
LU
; Tärneberg, William
LU
; Fitzgerald, Emma
LU
and Kihl, Maria
LU
- organization
- publishing date
- 2024
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 27th Conference on Innovation in Clouds, Internet and Networks, ICIN
- conference location
- Paris, France
- conference dates
- 2024-03-11 - 2024-03-14
- external identifiers
-
- scopus:85191263766
- ISBN
- 979-8-3503-9377-4
- 979-8-3503-9376-7
- DOI
- 10.1109/ICIN60470.2024.10494429
- project
- Cyber Security for Next Generation Factory (SEC4FACTORY)
- language
- English
- LU publication?
- yes
- id
- bbc1f526-ea47-42b7-9f20-c02bfceb106a
- date added to LUP
- 2024-04-18 16:04:07
- date last changed
- 2024-06-17 14:00:11
@inproceedings{bbc1f526-ea47-42b7-9f20-c02bfceb106a,
abstract = {{Traditional control systems face challenges in managing high data loads and computing power, prompting the evolution of Cloud Control Systems (CCS)-a fusion of Networked Control Systems (NCS) and cloud computing. Despite offering manifold advantages, CCS encounters hurdles in navigating the dynamic cloud environment characterized by fluctuating workloads, rendering static frequency settings inefficient. Moreover, the optimal utilization of cloud resources poses a pivotal challenge within CCS operations. To address these, the paper proposes a resilient CCS by adapting system frequency dynamically. Leveraging Q-learning, the approach measures Round Trip Time (RTT) and system output errors, dynamically adjusting the system's frequency to minimize control costs, optimize performance within the dynamic cloud environment, and achieve resource frugality, minimizing resource usage. Through real testbed experiments, this paper evaluates and analyzes the effectiveness of the proposed method, aiming to establish an adaptive and efficient control framework aligned with evolving cloud dynamics.}},
author = {{Akbarian, Fatemeh and Tärneberg, William and Fitzgerald, Emma and Kihl, Maria}},
booktitle = {{27th Conference on Innovation in Clouds, Internet and Networks (ICIN)}},
isbn = {{979-8-3503-9377-4}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Resilient Cloud Control System: Dynamic Frequency Adaptation via Q-learning}},
url = {{https://lup.lub.lu.se/search/files/181043022/Resilient_Cloud_Control_System_Dynamic_Frequency_Adaptation_via_Q-learning_2_.pdf}},
doi = {{10.1109/ICIN60470.2024.10494429}},
year = {{2024}},
}