AI for Next Generation Computing: Emerging Trends and Future Directions (2203.04159v1)

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 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.

AI for Next Generation Computing: Emerging Trends and Future Directions

The paper "AI for Next Generation Computing: Emerging Trends and Future Directions" by Sukhpal Singh Gill et al. presents a comprehensive exploration of current research frontiers and future possibilities for integrating AI and Machine Learning (ML) into various computing paradigms. The document serves as a visionary treatise on utilizing AI to enhance and transform cloud computing, fog computing, edge computing, serverless computing, and quantum computing environments. The paper underscores the challenges and opportunities posed by AI/ML integration and offers a forward-looking perspective on computing technologies' evolution over the next decade and beyond.

At its core, the paper roots its narrative in the paradigm of autonomic computing, defined as systems capable of self-management, often with minimal human intervention. In light of this, integrating AI/ML methodologies is posited as pivotal for creating systems that efficiently manage resources, adapt to environmental changes, and ultimately achieve higher degrees of autonomy. These autonomic systems can perform intricate tasks across extensive networks, spanning cloud, fog, and edge infrastructures, with applications in IoT environments touting the complexity and scale of operation.

The paper outlines a prospective software architecture model that integrates advanced technologies, allowing for enhanced and diverse Internet of Things (IoT) applications. It emphasizes the role of AI/ML in rendering these computing services while overcoming existing barriers, including latency, scalability, resource scheduling, and security. Emphasizing autonomic computing’s self-* properties—self-healing, self-configuring, self-protecting, and self-optimizing—the paper proposes a strategic framework for the next wave of computing environments supporting AI.

Furthermore, the authors delve into AI-enabled fog and edge computing, advocating for machine learning's potential in processing high-volume data generated by distributed networks. The document highlights the need for novel AI-based methodologies to improve resource usage and QoS while addressing the unique challenges posed by fog and edge environments, such as mobility support and real-time analytics.

By reviewing the state of serverless and quantum computing, the paper postulates that AI's role in these domains will be transformative, offering efficiencies in function management, task execution, and resource provision. The quantum computing segment maintains a theoretical approach, contemplating the promise of quantum-enhanced AI applications capable of executing presently intractable computations.

The paper also tackles pressing issues in AI-integrated computing, such as security, privacy, and data integrity, suggesting advancements like blockchain technology's potential for bolstering AI-based systems against vulnerabilities. Moreover, it lays the groundwork for future research directions across several computing arenas, emphasizing the necessity for AI-centric solutions to bridge current gaps in technology and operations.

In conclusion, the authors posit that AI-integrated computing systems herald a paradigm shift, characterized by increased autonomy, operational efficiency, and innovative service delivery. While these assertions are grounded in robust technical premises, the practical realization of these futuristic concepts hinges on overcoming significant technical, ethical, and economic challenges. The paper provides a fertile ground for further exploration, inviting researchers to venture beyond traditional computing boundaries and harness AI/ML's full potential in ushering next-generation computing solutions. In doing so, this work offers a compelling blueprint for future academic inquiry and industrial application in evolving computing landscapes.