International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 01 | Jan 2026
p-ISSN: 2395-0072
www.irjet.net
A REVIEW OF COMPARATIVE STUDY OF EDGE COMPUTING NETWORKS Vs. CLOUD COMPUTING NETWORKS FOR LATENCY-SENSITIVE APPLICATIONS Kajal Singh1, Mrs.Sahreen Hijab2 1Master of Technology, Computer Science and Engineering, Sagar Institute of Technology and Management,
Barabanki, India
2Assistant Professor, Department of Computer Science and Engineering, Sagar Institute of Technology and
Management, Barabanki, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The rapid growth of latency-sensitive
computing has gained significant attention as a complementary approach to cloud computing, enabling computation and data processing at the network edge to improve responsiveness and service quality (Shi et al., 2016).
applications such as autonomous vehicles, augmented and virtual reality, industrial automation, and remote healthcare has exposed the limitations of traditional cloud computing networks in meeting stringent delay and reliability requirements. This review presents a comprehensive comparative study of cloud computing and edge computing networks with a focus on their suitability for latency-sensitive applications. The paper systematically analyzes fundamental concepts, architectural differences, performance metrics, and application-specific requirements based on existing literature. The review highlights that while cloud computing offers scalability and centralized resource management, it suffers from high latency and bandwidth constraints. Edge computing, by contrast, significantly reduces latency by enabling localized data processing and real-time decisionmaking. However, challenges related to security, scalability, and resource management persist. The study concludes that hybrid edge–cloud architectures provide an effective balance between low-latency performance and scalable computation, making them a promising solution for next-generation networked systems.
1.2 Importance of Latency-Sensitive Applications Latency-sensitive applications are systems in which even minimal communication or processing delays can significantly degrade functionality, safety, or user experience. Such applications demand ultra-low latency, high reliability, and real-time data processing to operate effectively. With the advancement of 5G and emerging 6G networks, the number of applications requiring millisecondlevel latency has increased substantially, making latency a critical performance metric in modern networked systems (Cisco, 2020).
1.2.1 Characteristics Applications
1. INTRODUCTION 1.1 Background The rapid proliferation of data-intensive and real-time digital services has fundamentally transformed modern computing paradigms. Cloud computing has emerged as a dominant model by offering centralized, scalable, and ondemand computational resources over the Internet (Mell and Grance, 2011). However, the exponential growth of Internet of Things (IoT) devices, mobile users, and real-time applications has exposed critical limitations of centralized cloud infrastructures, particularly in terms of latency, bandwidth congestion, and reliability. These challenges have motivated the exploration of alternative computing paradigms that can support stringent performance requirements closer to end users. As a result, edge
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Impact Factor value: 8.226
Latency-Sensitive
Latency-sensitive applications are characterized by strict timing constraints, continuous data exchange, and often mission-critical operations. These applications typically involve real-time decision-making, where delays in data transmission or processing may result in system instability, safety hazards, or poor quality of service (QoS). Unlike traditional batch-processing workloads, latency-sensitive systems require predictable and deterministic response times, making centralized cloud processing less suitable in many scenarios (Satyanarayanan, 2017).
Key Words: Edge computing, Cloud computing, Latencysensitive applications, Quality of Service, Internet of Things, 5G networks.
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Prominent examples include augmented and virtual reality (AR/VR), where motion-to-photon latency must be minimized to prevent motion sickness and ensure immersive user experiences (Shi et al., 2016). Autonomous vehicles rely on real-time sensor data processing and vehicle-toeverything (V2X) communication, where delays may lead to catastrophic outcomes (Taleb et al., 2017). In industrial IoT environments, real-time control and monitoring systems require immediate feedback to maintain operational efficiency and safety. Similarly, telemedicine and remote surgery demand ultra-reliable and low-latency
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