Enhancing Cloud Application Performance Through Edge AI Integration

Mahajan, Omkar Avinash (2025) Enhancing Cloud Application Performance Through Edge AI Integration. Masters thesis, Dublin, National College of Ireland.

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Abstract

The growth of Internet of Things (IoT) devices and demand for low-latency applications expose the limits of cloud-only computing. Edge computing addresses these gaps by placing processing closer to data sources, reducing latency and band-width use. This report outlines the design and evaluation of a hybrid cloud-edge system enhanced with an AI-driven task-offloading engine. The system uses AWS services such as Lambda, API Gateway, S3, DynamoDB, IoT Core, and Green-grass to enable intelligent workload distribution. At its core, an AI decision engine selects whether inference tasks run on the edge device or in the cloud, based on device metrics, network conditions, task complexity, and historical performance. The performance tests evaluated a baseline setup for cloud-only, edge-enabled and AI-enabled systems. The edge-enabled system resulted in an overall reduction of P95 latency of 11.08%. Although edge offloading was intended and resource conditions were favorable, successful edge execution did not occur in the early experiments because the fixed client-side timeout triggered a premature fallback to the cloud. This clearly highlights the need for a more robust and adaptive failover mechanism in the system design. Overall, the results are supportive of an AI-driven hybrid architecture.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Siddig, Abubakr UNSPECIFIED
Subjects:	Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Electronic computers. Computer science > Computer Systems > Computers > Electronic data processing > Electronic data processing--Distributed processing > Edge computing T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science > Computer Systems > Computers > Electronic data processing > Electronic data processing--Distributed processing > Edge computing T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	01 Sep 2026 09:07
Last Modified:	01 Sep 2026 09:07
URI:	https://norma.ncirl.ie/id/eprint/9719

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