AI for Next Generation Computing: Emerging Trends and Future Directions
arXiv:2203.04159 · doi:10.1016/j.iot.2022.100514
Abstract
Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
arXiv commentAccepted for Publication in Elsevier IoT Journal, 2022
References in corpus (20)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- Quantum Mechanics helps in searching for a needle in a haystack
- Strengths and Weaknesses of Quantum Computing
- Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
- HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments
- Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions
- Demonstration of the trapped-ion quantum-CCD computer architecture
- Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges
- Experimental quantum speed-up in reinforcement learning agents
- Status of Serverless Computing and Function-as-a-Service(FaaS) in Industry and Research
- A Decentralized Framework for Serverless Edge Computing in the Internet of Things
- Formal Foundations of Serverless Computing
- HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing
- Explainable AI: current status and future directions
- ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments
- A Self-adaptive Approach for Managing Applications and Harnessing Renewable Energy for Sustainable Cloud Computing
- Machine Learning and Cloud Computing: Survey of Distributed and SaaS Solutions
- The Road to Quantum Artificial Intelligence
- With Great Freedom Comes Great Opportunity: Rethinking Resource Allocation for Serverless Functions
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- Intelligence at the Extreme Edge: A Survey on Reformable TinyML
- Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions
- Quantum Cloud Computing: Trends and Challenges
- A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction
- Towards Transmission-Friendly and Robust CNN Models over Cloud and Device
- Quantum Computing: Vision and Challenges
- Managing Cold-start in The Serverless Cloud with Temporal Convolutional Networks
- Computing: Looking Back and Moving Forward
- Fortaleza: The emergence of a network hub
- Autonomic Cloud Computing: Research Perspective
- SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework
- Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs