ACCRMI-2020: Autonomic Computing in Cloud Resource Management in Industry 4.0 |
Submission link | https://easychair.org/conferences/?conf=accrmi2020 |
Abstract registration deadline | May 28, 2020 |
Submission deadline | August 20, 2020 |
BOOK NAME:
Autonomic Computing in Cloud Resource Management in Industry 4.0
Submission link : https://easychair.org/conferences/?conf=accrmi2020
TENTATIVE CHAPTER(S) DETAILS:
1. Introduction to Cloud Resource ManagementThis chapter is the introduction of all about the core concept of resource management techniques in cloud, methods, processes, frameworks, and advantages.
2. Autonomic Computing in CloudIntroduction to autonomic computing in the cloud, how these techniques can use in cloud resource management, the techniques behind the autonomic computing, and its use.
3. Model, and ApplicationsDifferent models and its applicability has to be incorporated in this chapter. The evaluation and comparative analysis of existing models will be described.
4. Issues and challenges in Autonomic computing and resource managementThe backbone of any research article which encourages scholars to work further is all about how the issues and challenges of the research area are identified through state of art literature and its presentation, which will be included in this chapter.
5. The architecture of Autonomic Cloud Resource ManagementThe system architecture of autonomic computing, tradition resource management techniques, and cloud resource management through autonomic computing will be presented and describe in this chapter.
6. A self-adaptable framework to find best optimization techniquesThe adaptability of the optimization algorithm does not only find the optimal solution for real-time problem, it also facilitates the system to select the best-suited optimization technique automatically. Such techniques are highly profitable with some constraints that should also be focused.
7. Healing application against resource failureThe available resource has to be allocated to desired workloads as per their capacity and can be failed due to some uncertainty. These failures should be diagnosed first and then need to heal with some healing mechanism. This mechanism enhances the system efficiency without failure of execution.
8. Modelling re-installation components to adapt system configuration Automatic re-installation of all the components needed as per the requirements. Any failure occurs during the execution may cause system, application, and hardware failure and must be identified and restart.
9. Self-protection application to defend the malicious requestUsers may submit multiple requests/demands for any service and may inject malicious requests to the system that may harm or access the un-authorized information, which needs to diagnose and stop immediately without human intervention.
10. Resource management system for schedulingThe resource management based on the above techniques will be the results of the scheduling and may apply different scheduling techniques as per user and service provider’s policies and needs.
11. Comparative Analysis of various autonomic management systems in the cloudThe analysis of existing autonomic cloud resource management has to be carried out in this chapter with real-time data and graph/chart.
12. Applicability of Autonomic computing in resource managementThe use of autonomic computing in the resource management system and where it can be applied for efficient workflow will be presented in this chapter.
INFORMATION REGARDING THE BOOK:
Our next generation of an industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with automation, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent cloud services and resource sharing plays an important role in Industry 4.0 anticipated Fourth Industrial Revolution. The book will serve the different issues and challenges in cloud resource management CRM techniques with proper propped solution for IT organizations. The recent research in CRM is the evidence of better performance through autonomic computing. The book will cover five chapters based on the characteristics of autonomic computing with its applicability in CRM. Each chapter will have the techniques and analysis of each mechanism to make better resource management in cloud.
SHORT DESCRIPTION OF THE BOOK:
The cost of cloud service depends upon how resources are managed in cloud-based on different parameters. Providing service in dynamic environment with different constraints is challenging and typical to handle while any issue arises in run time, these may cause execution failure due to un-certainty hardware/application/operating system crash. In such a case, system needs to re-install all the components from the same point it was stopped. These issues are very typical to resolve manually on time. This book introduces various autonomic characteristics to resolve these issues so that things get settled down automatically.
KEY FEATURES OF THE BOOK:
• This book provide an overview of a self-adaptable framework to find the best optimization techniques automatically based on issues and platforms.
• Self-optimized model to find the optimal resource from the resource pool.
• Application to diagnose and heal the system failure, and protect from malicious attacks
EDITOR(S) NAME AND AFFILIATION:
Dr. Tanupriya Choudhury, Associate Professor; University of Petroleum and Energy Studies, Dehradun,INDIA, tanupriya@ddn.upes.ac.in; tanupriya1986@gmail.com
Mr. Bhupesh Kumar Dewangan , Assistant Professor; University of Petroleum and Energy Studies, Dehradun,INDIA, bhupesh.dewangan@gmail.com; b.dewangan@ddn.upes.ac.in
Mr. Ravi Tomar, Assistant Professor; University of Petroleum and Energy Studies, Dehradun,INDIA, ravitomar7@gmail.com; rtomar@ddn.upes.ac.in
Dr. Bhupesh Kumar Singh , Professor (Artificial Intelligence), Faculty of Computing and Software Engineering,Arba Minch University, Arba Minch, Ethiopia
Dr. Teoh Teik Toe, Academic Director and Senior Lecturer NTU, Singapore
Dr. Nguyen Gia Nhu, Dean of Graduate School- Duy Tan University, Viet Nam
ANY QUERIES:
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