Multiple Adaptive-Resource-Allocation Real-Time Supervisor (MARS) for Elastic IIoT Hybrid Cloud Services

Younghwan Shin, Wonsik Yang, Sangdo Kim, Jong Moon Chung

Research output: Contribution to journalArticlepeer-review


In this paper, a Multiple Adaptive-resource-allocation Real-time Supervisor (MARS) scheme for hybrid cloud-assisted Industrial Internet of Things (IIoT) is proposed to support reliable cloud services even under rapidly changing service demands that occur in massive IIoT networks. Virtual Machines (VMs) in both private cloud and public clouds can be elastically and accurately allocated through the proposed MARS scheme, which uses Karush-Kuhn-Tucker (KKT) optimization applied to the VM Continuous-Time Markov Chain (CTMC) scheme. Because the MARS scheme can immediately determine the optimal number of VMs based on the hybrid cloud situation, a significant improvement in the elasticity performance can be obtained. Compared to using the CTMC scheme, the results show that the MARS scheme can improve the response time up to 19.3 ∼ 73% (based on the activation rate) and the elasticity by 26.7%, and reduce the cost by 1.2%.

Original languageEnglish
Pages (from-to)1462-1476
Number of pages15
JournalIEEE Transactions on Network Science and Engineering
Issue number3
Publication statusPublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by the Ministry of Science and ICT under Grant D0318-21-1008 and in part by the National Fire Agency of the Republic of Korea under Grant 20017102.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications


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