Edge Computing vs Cloud Computing: A Comparative Review of Performance, Security, and Scalability

Abstract

The explosion of IoT devices, real-time apps, and bandwidth-hungry services has forced a hard look at how we\'ve traditionally relied on cloud computing. The cloud is great for centralized, scalable infrastructure that doesn\'t break the bank—but its latency and bandwidth limits are becoming real problems for applications that need fast responses or span lots of locations. That\'s where edge computing comes in, moving processing power closer to where data is actually generated and used. This paper compares edge and cloud computing across three core areas: performance, security, and scalability. Drawing on existing research and architectural analysis, it examines how each approach handles the demands of things like autonomous vehicles, smart healthcare, industrial automation, and augmented reality. The takeaway: edge computing wins on latency and bandwidth efficiency, while cloud computing still dominates when you need sheer computational muscle and global reach. Hybrid architectures— blending edge and cloud—look like the most promising path forward, combining the best of both worlds. The goal here is to give researchers, system architects, and practitioners a clearer picture when designing the next generation of distributed systems.

Introduction

The text compares cloud computing and edge computing in the context of rapidly growing IoT-driven data and the need for real-time processing.

It explains that the explosion of networked devices (expected to exceed 30 billion IoT devices by 2030) has exposed limitations of traditional cloud computing, especially high latency and bandwidth usage for time-sensitive applications like autonomous vehicles, healthcare monitoring, and industrial systems. While cloud computing offers scalable, centralized resources and cost efficiency, it struggles with delays and privacy concerns due to remote data processing.

To address these issues, edge computing shifts computation closer to data sources, reducing latency and bandwidth use. However, it introduces challenges such as limited processing power, heterogeneous devices, and complex management.

The paper aims to compare both paradigms in terms of architecture, performance (latency, processing, scalability), security, and cost. Literature shows that edge computing improves real-time responsiveness, while cloud computing remains superior for large-scale processing and storage. Recent research highlights hybrid approaches (like fog computing) that combine both models for better efficiency.

Architecturally, cloud computing is centralized (data centers managed by providers like AWS, Azure, Google Cloud), offering high scalability and powerful resources. Edge computing is decentralized, using local devices or gateways to process data near its source, reducing communication delays.

Key differences include:

• Latency: Edge is much faster (1–10 ms) vs cloud (50–150 ms)
• Processing power: Cloud is virtually unlimited; edge is resource-constrained
• Bandwidth: Edge reduces usage; cloud requires continuous data transfer
• Security: Cloud is centralized but high-value target; edge is distributed but harder to manage
• Scalability: Cloud is easier to scale; edge is more complex

Conclusion

In this paper, we have provided an overview of edge computing and cloud computing, as well as a comparative analysis of their architectures, performance, security, scalability, and use cases. The study reveals that the two paradigms are not competing but rather complementary, each with its unique strengths that make it suitable for different use-cases and design constraints. Use cases for edge computing are those applications that need low latency, location awareness and reduced load on the wireless network, and are ideal for any real-time application such as autonomous driving, process automation, IoT medical monitoring, and smart cities. Alternatively, cloud computing has unrivalled scalability and computational power, and a rich service ecosystem, making it best suited for global applications, complex workloads, and computational workloads such as large-scale machine learning training. The findings discussed in this paper clearly indicate that the future of distributed computing will not be about prevailing turf of either paradigm; rather it will be about advanced hybrid computing systems that exploit the benefits of both edge and cloud. Smart workload placement, federated machine learning and the advancement of edge management tools will together make it possible for systems to have the edge and the cloud in one package. With the roll-out of 5G networks, which will increasingly deliver high-speed, low-latency connectivity in more settings, and as edge hardware capabilities improve, the edge-cloud distinction will increasingly blur. Researchers need to develop standardised models for hybrid edge-cloud systems, extend the security mechanisms available in edge environments with limited resources, and build models of the cost of ownership for different deployment paradigms. We are at the level of transition and what researchers and practitioners do this time will determine where the computing world will be in future.

References

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Copyright

Copyright © 2026 Bandaru Prasanna, Saladi Gayathri Devi, Yalla Lavanya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.