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When clustering devices at the edge, inter-node latency poses a significant challenge that directly impacts the application performance. In this paper, we experimentally examine the impact that inter-node latency has on application…

The use of Deep Learning and Machine Learning is becoming pervasive day by day which is opening doors to new opportunities in every aspect of technology. Its application Ranges from Health-care to Self-driving Cars, Home Automation to…

Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing and benchmarking fog applications, however, is hard since runtime…

Virtually all of the rapidly increasing data traffic consumed by mobile users requires some kind of processing, normally performed at cloud servers. A recent thrust, {\em mobile edge computing}, moves such processing to servers {\em within}…

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of…

Fog computing offers increased performance and efficiency for Industrial Internet of Things (IIoT) applications through distributed data processing in nearby proximity to sensors. Given resource constraints and their contentious use in IoT…

In this paper, we analyze some of our real-world deployment of face recognition (FR) systems for various applications and discuss the gaps between expectations of the user and what the system can deliver. We evaluate some of our proposed…

Service meshes play a central role in the modern application ecosystem by providing an easy and flexible way to connect different services that form a distributed application. However, because of the way they interpose on application…

Current fog systems rely on centralized and strongly consistent services for configuration management originally designed for cloud systems. In the geo-distributed fog, such systems can exhibit high communication latency or become…

The convergence of Artificial Intelligence (AI) and the Internet of Things has accelerated the development of distributed, network-sensitive applications, necessitating ultra-low latency, high throughput, and real-time processing…

With the proliferation of latency-critical applications, fog-radio network (FRAN) has been envisioned as a paradigm shift enabling distributed deployment of cloud-clone facilities at the network edge. In this paper, we consider proactive…

Priority-aware networks-on-chip (NoCs) are used in industry to achieve predictable latency under different workload conditions. These NoCs incorporate deflection routing to minimize queuing resources within routers and achieve low latency…

With the rapid increase in the Internet of Things (IoT), the amount of data produced and processed is also increased. Cloud Computing facilitates the storage, processing, and analysis of data as needed. However, cloud computing devices are…

Fog and edge computing require adaptive control schemes that can handle partial observability, severe latency requirements, and dynamically changing workloads. Recent research on Agentic AI (AAI) increasingly integrates reasoning systems…

Cloud computing represents an appealing opportunity for cost-effective deployment of HPC workloads on the best-fitting hardware. However, although cloud and on-premise HPC systems offer similar computational resources, their network…

A decentralized optimization policy for service placement in fog computing is presented. The optimization is addressed to place most popular services as closer to the users as possible. The experimental validation is done in the iFogSim…

Recently, the use of IoT devices and sensors has been rapidly increased which also caused data generation (information and logs), bandwidth usage, and related phenomena to be increased. To our best knowledge, a standard definition for the…

Identifying the closest fog node is crucial for mobile clients to benefit from fog computing. Relying on geographical location alone us insufficient for this as it ignores real observed client access latency. In this paper, we analyze the…

As edge and fog computing become central to modern distributed systems, there's growing interest in combining serverless architectures with privacy-preserving machine learning techniques like federated learning (FL). However, current…

Internet of Things typically involves a significant number of smart sensors sensing information from the environment and sharing it to a cloud service for processing. Various architectural abstractions, such as Fog and Edge computing, have…