The rapid proliferation of low-latency and high-bandwidth applications has brought edge computing to the forefront of mobile network architectures. However, the strategic placement of edge servers plays a vital role in balancing price-performance trade-offs significantly. Existing works addressing the Edge Server Placement Problem have assumed homogeneous computational capabilities across ESs, which is not a pragmatic assumption considering variations in user densities and workload fluctuations across typical cityscapes. This work proposes a solution to the Edge Server Placement Problem with heterogeneous ES capacities and introduces a novel scheme to evaluate the workload of ESs for 5G networks. Additionally, this paper also proposes a novel Knapsack-based Metaheuristic for allocating base stations to edge servers, turning the Edge Server Placement Problem into a 0-1 Knapsack problem. Experimental evaluation using popular 5G traffic demand datasets has found that the proposed approach improves workload balance by 40.79%, utilisation rates by 57.58%, and reduces energy consumption by 44.68% approximately vis-à-vis homogeneous counterparts.

A Knapsack-based Metaheuristic for Edge Server Placement in 5G networks with heterogeneous edge capacities

Fiore U.
2024-01-01

Abstract

The rapid proliferation of low-latency and high-bandwidth applications has brought edge computing to the forefront of mobile network architectures. However, the strategic placement of edge servers plays a vital role in balancing price-performance trade-offs significantly. Existing works addressing the Edge Server Placement Problem have assumed homogeneous computational capabilities across ESs, which is not a pragmatic assumption considering variations in user densities and workload fluctuations across typical cityscapes. This work proposes a solution to the Edge Server Placement Problem with heterogeneous ES capacities and introduces a novel scheme to evaluate the workload of ESs for 5G networks. Additionally, this paper also proposes a novel Knapsack-based Metaheuristic for allocating base stations to edge servers, turning the Edge Server Placement Problem into a 0-1 Knapsack problem. Experimental evaluation using popular 5G traffic demand datasets has found that the proposed approach improves workload balance by 40.79%, utilisation rates by 57.58%, and reduces energy consumption by 44.68% approximately vis-à-vis homogeneous counterparts.
2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4852111
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