Autonomic computing investigates how systems can achieve (user) specified “control”
outcomes on their own, without the intervention of a human operator. Autonomic computing …

Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI
refers to the practice of doing AI computations near the users at the network's edge, instead …

Resource management in computing is a very challenging problem that involves making
sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse …

Cloud computing is a mainstay of modern technology, offering cost-effective and scalable
solutions to a variety of different problems. The massive shift of organization resource needs …

In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

Over the past six decades, the computing systems field has experienced significant
transformations, profoundly impacting society with transformational developments, such as …

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry
4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in …

Building a fault-tolerant edge system that can quickly react to node overloads or failures is
challenging due to the unreliability of edge devices and the strict service deadlines of …

Recently, intelligent scheduling approaches using surrogate models have been proposed to
efficiently allocate volatile tasks in heterogeneous fog environments. Advances like …

Cloud computing being an integral part of today's technical advancements, still faces issues
regarding resource allocation, task scheduling, communication latency, etc. To address …