[Paper Review] Autonomic Cloud Computing: Research Perspective

Sukhpal Singh Gill|arXiv (Cornell University)|Jul 6, 2015

Cloud Computing and Resource Management1 references3 citations

TL;DR

This paper proposes an AI-driven autonomic cloud computing model for QoS and SLA-aware resource management, enabling self-healing, self-optimizing, and self-configuring cloud systems. By integrating machine learning with autonomic principles, the framework optimizes server load distribution and reduces energy consumption, leading to improved cost efficiency and environmental sustainability in dynamic cloud environments.

ABSTRACT

As the cloud infrastructure grows, it becomes more challenging to manage resources in such a massive, diverse, and distributed setting, despite the fact that cloud computing provides computational capabilities on-demand. Due to resource variability and unpredictability, resource allocation issues arise in a cloud setting. A Quality of Service (QoS) based autonomic resource management strategy automates resource management, delivering trustworthy, dependable, and cost-effective cloud services that efficiently execute workloads. Autonomic cloud computing aims to understand how computing systems may autonomously accomplish user-specified "control" objectives without the need for an administrator and without violating the Service Level Agreement (SLA) in a dynamic cloud computing environments. This chapter presents a research perspective and analysis on autonomic resource allocation in cloud computing based on the last decade of conducted research with a focus on QoS and SLA-aware autonomic resource management. This study delves into the current state of autonomic resource management in the cloud and introduces a conceptual model for Artificial Intelligence (AI)-driven autonomic cloud computing. This model aims to optimise server load distribution and energy consumption, thus enhancing cost savings and environmental impact. Finally, it highlights key next-generation research directions.

Motivation & Objective

• To address the growing complexity of managing large-scale, dynamic cloud infrastructures with minimal human intervention.
• To develop a self-managing cloud system that maintains service-level agreements (SLAs) while ensuring quality of service (QoS).
• To reduce energy consumption and operational costs through intelligent, adaptive resource allocation.
• To introduce a conceptual AI-driven autonomic cloud computing model for future cloud management.
• To identify and guide next-generation research directions in autonomic cloud resource management.

Proposed method

• The paper proposes a conceptual AI-driven autonomic cloud computing model that integrates machine learning with autonomic principles for dynamic resource management.
• The system uses QoS and SLA metrics as feedback to guide self-configuration, self-optimization, and self-healing behaviors.
• Resource allocation decisions are based on real-time workload analysis and predictive modeling to balance server load.
• The model emphasizes energy-aware scheduling to minimize power consumption without compromising performance.
• It leverages historical and real-time data to train adaptive algorithms for workload prediction and resource provisioning.
• The framework is designed to operate autonomously, reducing dependency on manual administration while maintaining compliance with service-level objectives.

Experimental results

Research questions

• RQ1 How can cloud systems autonomously manage resources while maintaining QoS and SLA compliance in dynamic environments?
• RQ2 What role can AI and machine learning play in enabling self-optimizing and self-healing cloud infrastructures?
• RQ3 How can energy consumption be minimized in cloud data centers through intelligent resource allocation?
• RQ4 What are the key architectural components needed for scalable, AI-driven autonomic cloud management?
• RQ5 What future research directions are essential for advancing autonomic cloud computing?

Key findings

• The proposed AI-driven autonomic model enables autonomous adaptation to workload fluctuations while maintaining SLA compliance.
• The framework demonstrates potential for significant reductions in energy consumption through intelligent load balancing and scheduling.
• The integration of QoS metrics into autonomic decision-making improves service reliability and performance predictability.
• The model supports cost-effective resource utilization by minimizing over-provisioning and idle capacity.
• The study identifies critical research gaps in real-time learning, scalability, and cross-layer coordination for next-generation cloud systems.
• The conceptual model provides a foundation for future development of self-managing cloud platforms with enhanced sustainability and resilience.