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Open Access

ARTICLE

Empowering Edge Computing: Public Edge as a Service for Performance and Cost Optimization

Ateeqa Jalal1,, Umar Farooq1,4,5, Ihsan Rabbi1,4, Afzal Badshah2, Aurangzeb Khan1,4, Muhammad Mansoor Alam3,4 and Mazliham Mohd Su'ud4,
1 Institute of Computer Science & IT, University of Science and Technology, Bannu, 28200, Pakistan
2 Department of Software Engineering, University of Sargodha, Sargodha, 40162, Pakistan
3 Faculty of Computing, Riphah International University, Islamabad, 44000, Pakistan
4 Faculty of Computing, Multimedia University, Cyberjaya, 63100, Malaysia
5 International Heriot Watt Faculty, K. Zhubanov University, Aktobe, 030001, Kazakhstan
* Corresponding Authors: Ateeqa Jalal. Email: ateeqajalal@gmail.com; Mazliham Mohd Su’ud. Email: mazliham@mmu.edu.my

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.068289

Received 24 May 2025; Accepted 05 August 2025; Published online 10 November 2025

Abstract

The exponential growth of Internet of Things (IoT) devices, autonomous systems, and digital services is generating massive volumes of big data, projected to exceed 291 zettabytes by 2027. Conventional cloud computing, despite its high processing and storage capacity, suffers from increased network latency, network congestion, and high operational costs, making it unsuitable for latency-sensitive applications. Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership (TCO). Hybrid solutions, such as fog computing, cloudlets, and Mobile Edge Computing (MEC), attempt to balance cost and performance; however, they still struggle with limited resource sharing and high deployment expenses. This paper proposes Public Edge as a Service (PEaaS), a novel paradigm that utilizes idle resources contributed by universities, enterprises, cellular operators, and individuals under a collaborative service model. By decentralizing computation and enabling multi-tenant resource sharing, PEaaS reduces reliance on centralized cloud infrastructure, minimizes communication costs, and enhances scalability. The proposed framework is evaluated using EdgeCloudSim under varying workloads, for key metrics such as latency, communication cost, server utilization, and task failure rate. Results reveal that while cloud has a task failure rate rising sharply to 12.3% at 2000 devices, PEaaS maintains a low rate of 2.5%, closely matching edge computing. Furthermore, communication costs remain 25% lower than cloud and latency remains below 0.3, even under peak load. These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability, offering a sustainable and economically viable solution for next-generation computing environments.

Keywords

Big data; edge as a service; edge computing

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