2025· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Abstract
As enterprise data grows across cloud, edge, and geographically distributed environments, traditional Edge computing has emerged as a transformative extension of cloud computing by addressing the limitations of latency, bandwidth, and scalability in real-time applications. With the increasing demand for ultra-low latency in autonomous vehicles, industrial automation, telemedicine, smart cities, and augmented reality, traditional cloud architectures face challenges due to centralized processing and network delays. Edge computing overcomes these issues by processing data closer to end devices, enabling faster decision-making and reduced communication overhead. This paper presents a comprehensive survey of edge computing architectures for ultra-low latency applications, covering key technologies such as 5G, Software-Defined Networking (SDN), Network Function Virtualization (NFV), Artificial Intelligence (AI), microservices, and container orchestration. It also examines major challenges, including resource management, interoperability, security, and energy efficiency. A multi-layer edge computing framework with intelligent task scheduling and dynamic resource allocation is proposed to optimize latency and resource utilization. Experimental findings demonstrate significant improvements over conventional cloud architectures, achieving over 70% reduction in end-to-end latency and 65% improvement in resource efficiency. The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
The evaluation of the proposed architecture through analytical models and simulation-based evaluations shows that the proposed architecture can reduce the latency onto 65 percent of the time relative to the conventional cloud-based architecture, affirm the claim that edge computing is an essential enabler of the next-generation applications that demand deterministic response time, high reliability and localized intelligence.
Priya Natarajan· International Journal of Mod...· 0 citations
This paper introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture.
Pradeep Kachakayala, Akshith Kachakayala· International Journal for Re...· 0 citations
The rapid growth of IoT, 5G, AI, and cyber-physical systems has accelerated the development of smart applications that require low-latency, reliable, and intelligent computing. Traditional cloud computing faces challenges in meeting these demands due to latency, bandwidth, and privacy limitations. Intelligent Edge–Cloud collaboration addresses these issues by combining edge computing with cloud resources for efficient workload distribution, AI-driven resource management, and adaptive service orchestration. This paper reviews recent advances in collaborative architectures, distributed AI, intelligent orchestration, and resource optimization. It also highlights key challenges, including interoperability, security, heterogeneous resource management, and sustainable computing, while demonstrating the potential of Edge–Cloud collaboration to improve computational efficiency, response time, energy efficiency, scalability, and privacy for next-generation smart applications.
Mahabala H.N· International Journal of Eme...· 0 citations
The rapid growth of Internet of Things (IoT) devices, together with the increasing demand for real-time data processing, has exposed the limitations of traditional centralized cloud computing architectures. Edge computing has emerged as a transformative paradigm that brings computation, storage, and networking closer to the data source, thereby reducing latency, conserving bandwidth, and enhancing privacy. This paper presents a comprehensive review of edge computing as a modern trend in information technology. We systematically examine the architectural foundations, enabling technologies, and deployment models that underpin edge computing ecosystems. Furthermore, we analyze prominent application domains including autonomous vehicles, smart cities, industrial IoT (IIoT), healthcare, and augmented or virtual reality where edge computing delivers measurable performance improvements. A critical assessment of open challenges such as security vulnerabilities, resource constraints, interoperability, and orchestration complexity is also provided. Finally, we outline future research directions, including the convergence of edge computing with artificial intelligence (Edge AI), 6G networks, digital twins, and serverless edge architectures. This review aims to serve as a foundational reference for researchers and practitioners seeking to understand the current state and future direction of edge computing within the broader information technology landscape.
Keywords: Edge Computing, Internet of Things, Fog Computing, Cloud-Edge Continuum, Latency Reduction, Edge AI, Distributed Systems, Real-Time Processing.
Awiti Gideon Appiah, Dr. Lazarus Kwao, Benjamin Opoku Atuahene· International Journal of Cre...· 0 citations
Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing frameworks cannot adequately handle this level of heterogeneity and distribution. This paper proposes EdgeFaaS, a novel function-based edge computing framework to enable edge applications to effectively utilize heterogeneous resources distributed across the Internet of Things (IoT), edge, and cloud for computing. It proposes function virtualization and storage virtualization to abstract distributed and heterogeneous physical resources and provides consistent virtual interfaces for deploying and executing functions and storing and accessing data. EdgeFaaS provides comprehensive support to diverse edge computing workflows, and at the same time allows users to flexibly adjust the configurations and explore various important tradeoffs. To demonstrate its usability, the paper also presents the implementation and evaluation of three representative workflows on EdgeFaaS for video analytics, federated learning, and audio classification, on a real testbed of 100+ geographically distributed IoT devices, edge servers, and cloud services. EdgeFaaS allows users to flexibly explore the deployment configurations of these workflows over distributed and heterogeneous resources. For example, users can easily vary the function placement of the video processing pipeline across IoT, edge, and cloud resources and study the tradeoff between computation and communication costs; users can also flexibly adjust the cluster count and size in the hierarchical federated learning system and explore the tradeoff between training accuracy and speed.
N. Vadnere, Yu-Ting Wang, Yitao Chen et al.· International Conference on...· 0 citations
A lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time is introduced that yields reduced latency and more consistent wait times relative to heuristic and genetic baselines.
Anshul Atre, K. Singh, B. Chaurasia et al.· Journal of Circuits, Systems...· 0 citations