AI for next generation computing: Emerging trends and future directions

Note

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• Publish Year : 2022

• Authors : Gill, Xu, Ottaviani, Patros, Bahsoon, Shaghaghi, Golec, Stankovski, Wu, Abraham, and others

Research Contributions

By combining AI/ML with cloud, fog, edge, serverless, and quantum computing, we have created the first review of its kind. Adding to the previous surveys, this new research gives a new imaginative approach to assessing and identifying the most current research challenges

Key Concepts and Terminologies

• Autonomic Computing Initiative (ACI)

  • The target of the ACI is to develop self-managing systems that can automatically adapt to changes in the environment and requirements of the system without human intervention.

  • Inspired by human nervous system and human cognition

  • self-adaptive systems

  • self-configuration, self-optimisation, self-protection and self-healing [2]

  • automatically upgrades missing or obsolete components depending on error messages/alerts generated by a monitoring system

  • educing resource overload and under-utilisation

  • self-protection is an autonomic system’s capacity to defend itself against potential cyber-attacks and intrusions

  • ability to discover, evaluate and recover from errors on its own, without the need for human intervention

• cloud, fog, edge, serverless and quantum computing

  • cloud computing : TODO

  • fog computing : TODO

  • edge computing : TODO

  • serverless computing : TODO

Proposed Methodologies

This figure is taken from the paper [1]

• Self-healing : The key concept of self-healing is to develop predictive models that can predict the future state of the system and take corrective actions to prevent system failures, rather than simply reacting to them. This approach is also known as fault detection and predictive maintenance.

• Self-protection : Continuously monitor suspicious activities to ensure the system runs smoothly.

• Self-configuration : Reinstalling missing or obsolete components without human intervention.

• Self-optimisation : The system is particularly suitable for data-intensive applications, as it can automatically adjust its resources based on environmental changes.

1. Monitor

2. Analyse

3. Plan

4. Execute

Cloud Computing

Open Challenge

1. Integration : Companies need to move their applications to the cloud

2. Inadequate Data : Data is accessible and clean for AI/ML

3. Security and Privacy : Prevent data breaches and protect sensitive information from adversaries

Fog Computing

Supplement of Cloud Computing IoT need mininal reaction time

Open Challenge

1. Execution time : Speed up reaction time

2. Mobility Awareness : Fog computing struggles with mobility (e.g., devices moving from one location to another) because it’s primarily designed for fixed devices.

3. Resource Schedule : Fog computing is less flexible than cloud computing due to limited resources, making efficient resource scheduling challenging.

4. Energy Efficiency :

5. Security and Privacy :

Conclusions

References

[1] (1,2,3)

Sukhpal Singh Gill, Minxian Xu, Carlo Ottaviani, Panos Patros, Rami Bahsoon, Arash Shaghaghi, Muhammed Golec, Vlado Stankovski, Huaming Wu, Ajith Abraham, and others. Ai for next generation computing: emerging trends and future directions. Internet of Things, 19:100514, 2022.

[2]

Erik Elmroth, Johan Tordsson, Francisco Hernández, Ahmed Ali-Eldin, Petter Svärd, Mina Sedaghat, and Wubin Li. Self-management challenges for multi-cloud architectures. In Towards a Service-Based Internet: 4th European Conference, ServiceWave 2011, Poznan, Poland, October 26-28, 2011. Proceedings 4, 38–49. Springer, 2011.