Loading...
Real-time virtual machine scheduling in industry IoT network : A reinforcement learning method
Ma, Xiaojin Xu, Huahu Gao, Honghao Bian, Minjie Hussain, Walayat
Ma, Xiaojin
Xu, Huahu
Gao, Honghao
Bian, Minjie
Hussain, Walayat
Abstract
The widespread adoption of Industrial Internet of Things (IIoT)-based applications has driven the emergence and development of cloud-related computing paradigms with the ability to seamlessly leverage cloud resources. Heterogeneous resources, mobility factors in IoT, and dynamic behavior make it challenging for the corresponding virtual machine (VM) scheduling problem to address the processing effectiveness of application requests in these kinds of cloud environments. Based on reinforcement learning theory, this article proposes an online VM scheduling scheme (OSEC) for joint energy consumption and cost optimization that divides the scheduling process into two parts: VM allocation and VM migration. First, all the VMs and the physical machines (PMs) are regarded as a set of states and actions in the cloud environment, and the Q-learning feedback is used to achieve the iterative computation of Q-values to obtain the optimal parallel allocation sequence for multiple VMs. Then, VMs are migrated among the active PMs according to a grouping policy and the best-fit principle to achieve dynamic consolidation of the resources in the data center. Finally, experimental results show that compared with state-of-the-art algorithms under different conditions, the proposed method reduces energy consumption by approximately 18.25%, VM execution costs by approximately 21.34%, and service level agreement (SLA) violations by approximately 90.51%.
Keywords
energy consumption, execution cost, online consolidation, quality of service, reinforcement learning, virtual machine (VM) scheduling
Date
2023
Type
Journal article
Journal
IEEE Transactions on Industrial Informatics
Book
Volume
19
Issue
2
Page Range
2129-2139
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled