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Autonomic Computing in Cloud Resource Management in Industry 4.0 pp 59–90Cite as

Autonomic Computing: Models, Applications, and Brokerage

Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Autonomic computing is not a core, rather a convergence of numerous concepts and supporting technologies. It is the junction that integrates salient computing domains and subdomains to create a self-driven, self-healing, and self-manageable computing environment. The possible integration, exploration, and hybridization toward the development of the new models and applications are new knowledge contributions. Modeling is a conceptualization of autonomic computing in general and contextualization in specific applications. In the context of cloud-based autonomic computing, a model presents the fact that the operations of the autonomic computing systems are goal-oriented and driven by certain activities and follow certain policies and behavioral aspects, with the existing features. Currently, client organizations or individual clients prefer to use packaged computing products and services over the cloud or distributed systems. This chapter covers how autonomic computing models hold the promising features for simplification, and the ease of computing system management over clouds such as process management, autonomic client migration for load balancing, monitoring, energy efficiency(green), automatic updating of software tools/drivers, predictive warning before failure, error detection and correction, backups, and recovery from sudden disasters. This chapter also covers the possible applications and related issues encountered during adaption and adoption at salient scales and types of organizations. The summarized feature-based comparative analysis of the existing computing and emerging autonomic models are also incorporated. A cloud-based green broker model for cloud service selection is designed and incorporated for the autonomic brokerage of green cloud services. Furthermore, the applications of the autonomic process management architecture to salient applications such as governance, commerce, management, industrial automation, etc. are included.

Keywords

  • Autonomic computing
  • Models
  • Applications
  • Green cloud service broker
  • Self-management
  • Computing and communication

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Author information

Authors and Affiliations

  1. Computing and Software Engineering, Arba Minch University, Arba Minch, Ethiopia

    Durga Prasad Sharma, Bhupesh Kumar Singh & Amin Tuni Gure

  2. Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

    Tanupriya Choudhury

Authors
  1. Durga Prasad Sharma
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  2. Bhupesh Kumar Singh
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  3. Amin Tuni Gure
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  4. Tanupriya Choudhury
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Corresponding author

Correspondence to Bhupesh Kumar Singh .

Editor information

Editors and Affiliations

  1. Department of Informatics, School of Computer Science University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

    Tanupriya Choudhury

  2. Department of Informatics, School of Computer Science University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

    Bhupesh Kumar Dewangan

  3. Department of Informatics, School of Computer Science University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India

    Dr. Ravi Tomar

  4. Computing and Software Engineering, Arba Minch University, Arba Minch, Ethiopia

    Bhupesh Kumar Singh

  5. Nanyang Technological University (NTU), Singapore, Singapore

    Teoh Teik Toe

  6. Duy Tan University, Da Nang, Vietnam

    Dr. Nguyen Gia Nhu

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Sharma, D.P., Singh, B.K., Gure, A.T., Choudhury, T. (2021). Autonomic Computing: Models, Applications, and Brokerage. In: Choudhury, T., Dewangan, B.K., Tomar, R., Singh, B.K., Toe, T.T., Nhu, N.G. (eds) Autonomic Computing in Cloud Resource Management in Industry 4.0. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-71756-8_4

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  • DOI: https://doi.org/10.1007/978-3-030-71756-8_4

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