EP-MPCHS: Edge Server based cloudlet offloading using Multi-Core and Parallel Heap Structures

Nagulsamy, Rajkumar (2024) EP-MPCHS: Edge Server based cloudlet offloading using Multi-Core and Parallel Heap Structures. Masters thesis, Dublin, National College of Ireland.

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Abstract

The increasing computational demands of mobile applications, like image caption generators and Google Lens, result in higher memory and RAM usage. Thus, to offboard the computational workload of these applications cloud-based edge server frameworks has been in demand lately. In the age of cloud-based computing, the study aims to create a hybridized cloudlet placement algorithm that caters to reducing latency, increasing bandwidth, reducing network flow pressure, and optimizing the edge server resources. The primary condition of the utilized placement algorithm is to prioritize the cloudlets enabling the reduction of latency, increase of bandwidth, and CPU utilization.

This study proposes Edge Priority Placement using Multi-Core and Parallel Heap Structures (EP-MCPHS) utilizing the min heap prioritization technique to deduce the placement of each cloudlet. This incorporates the priority queue-based resource allocation system which ensures that the optimal process is selected to the minimum available edge server allowing the reduction of resource utilization, increased latency, decreased network flow, and enhanced bandwidth. The algorithm also reinforces the technique with a multi-latent parallel head provisioning or Ph.C. with parallel processing allowing reduced process starvation for the non-processing cloudlets. The EP-MCPHS reduces the end time for cloudlet processing by 27.36%, increases bandwidth by 71.27%, and data flow by 24.81%.

The study incorporates the SimPy framework for simulation testing using the EdgeSimPy framework for edge server simulation. The study compares the results achieved by the architecture with the Min-Max fairness algorithm. It provides statistical testing across time intervals to showcase the ability of the placement algorithm even with increasing workload.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Gupta, Punit UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Mobile Phone Applications T Technology > T Technology (General) > Information Technology > Computer software > Mobile Phone Applications
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	04 Jul 2025 08:46
Last Modified:	04 Jul 2025 08:46
URI:	https://norma.ncirl.ie/id/eprint/8039

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