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 …

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

To meet next-generation Internet of Things (IoT) application demands, edge computing
moves processing power and storage closer to the network edge to minimize latency and …

Kubernetes has revolutionized traditional monolithic Internet of Things (IoT) applications into
lightweight, decentralized, and independent microservices, thus becoming the de facto …

Container orchestration handles the semi-automated management of applications across
Edge-Cloud, providing features such as autoscaling, high availability, and portability. Having …

Deep neural network (DNN) and machine learning (ML) models/inferences produce highly
accurate results demanding enormous computational resources. The limited capacity of end …

The growing convergence of high-performance, data analytics, and machine-learning
applications is increasingly pushing computing systems toward heterogeneous processors …

The University Course Timetabling Problem is an active and complex field with broad
implications at most educational institutions. The current gap between literature and real …

In the edge computing environment, due to factors, such as expensive devices, complex
scenarios, and fine control requirements, it is necessary to use simulators to construct …