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 …

Traffic classification is essential for cybersecurity maintenance and network management,
and has been widely used in QoS (Quality of Service) guarantees, intrusion detection, and …

The growing adoption of microservice architectures (MSAs) has led to major research and
development efforts to address their challenges and improve their performance, reliability …

Serverless edge computing decreases unnecessary resource usage on end devices with
limited processing power and storage capacity. Despite its benefits, serverless edge …

Currently, the accurate prediction of social relationships can effectively reduce the decision‐
making burden of users in various service platforms. However, in the big data environment …

Microservices is a structural approach, where multiple small set of services are composed
and processed independently with lightweight communication mechanism. To accomplish …

Cloud‐native architecture is becoming increasingly crucial for today's cloud computing
environments due to the need for speed and flexibility in developing applications. It utilizes …

Function-as-a-Service (FaaS) introduces a lightweight, function-based cloud execution
model that finds its relevance in a range of applications like IoT-edge data processing and …