MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912545750646784 |
|---|---|
| author | Agrawal, Kushagra Goktas, Polat Bandopadhyay, Anjan Ghosh, Debolina Jena, Junali Jasmine Gourisaria, Mahendra Kumar |
| author_facet | Agrawal, Kushagra Goktas, Polat Bandopadhyay, Anjan Ghosh, Debolina Jena, Junali Jasmine Gourisaria, Mahendra Kumar |
| contents | The rapid growth of Internet of Things (IoT) ecosystems has intensified the challenge of efficiently allocating heterogeneous resources in highly dynamic, distributed environments. Conventional centralized mechanisms and single-objective auction models, focusing solely on metrics such as cost minimization or revenue maximization, struggle to deliver balanced system performance. This paper proposes the Multi-Objective Hierarchical Auction Framework (MOHAF), a distributed resource allocation mechanism that jointly optimizes cost, Quality of Service (QoS), energy efficiency, and fairness. MOHAF integrates hierarchical clustering to reduce computational complexity with a greedy, submodular optimization strategy that guarantees a (1-1/e) approximation ratio. A dynamic pricing mechanism adapts in real time to resource utilization, enhancing market stability and allocation quality. Extensive experiments on the Google Cluster Data trace, comprising 3,553 requests and 888 resources, demonstrate MOHAF's superior allocation efficiency (0.263) compared to Greedy (0.185), First-Price (0.138), and Random (0.101) auctions, while achieving perfect fairness (Jain's index = 1.000). Ablation studies reveal the critical influence of cost and QoS components in sustaining balanced multi-objective outcomes. With near-linear scalability, theoretical guarantees, and robust empirical performance, MOHAF offers a practical and adaptable solution for large-scale IoT deployments, effectively reconciling efficiency, equity, and sustainability in distributed resource coordination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14830 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems Agrawal, Kushagra Goktas, Polat Bandopadhyay, Anjan Ghosh, Debolina Jena, Junali Jasmine Gourisaria, Mahendra Kumar Distributed, Parallel, and Cluster Computing Computer Science and Game Theory Neural and Evolutionary Computing Networking and Internet Architecture The rapid growth of Internet of Things (IoT) ecosystems has intensified the challenge of efficiently allocating heterogeneous resources in highly dynamic, distributed environments. Conventional centralized mechanisms and single-objective auction models, focusing solely on metrics such as cost minimization or revenue maximization, struggle to deliver balanced system performance. This paper proposes the Multi-Objective Hierarchical Auction Framework (MOHAF), a distributed resource allocation mechanism that jointly optimizes cost, Quality of Service (QoS), energy efficiency, and fairness. MOHAF integrates hierarchical clustering to reduce computational complexity with a greedy, submodular optimization strategy that guarantees a (1-1/e) approximation ratio. A dynamic pricing mechanism adapts in real time to resource utilization, enhancing market stability and allocation quality. Extensive experiments on the Google Cluster Data trace, comprising 3,553 requests and 888 resources, demonstrate MOHAF's superior allocation efficiency (0.263) compared to Greedy (0.185), First-Price (0.138), and Random (0.101) auctions, while achieving perfect fairness (Jain's index = 1.000). Ablation studies reveal the critical influence of cost and QoS components in sustaining balanced multi-objective outcomes. With near-linear scalability, theoretical guarantees, and robust empirical performance, MOHAF offers a practical and adaptable solution for large-scale IoT deployments, effectively reconciling efficiency, equity, and sustainability in distributed resource coordination. |
| title | MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems |
| topic | Distributed, Parallel, and Cluster Computing Computer Science and Game Theory Neural and Evolutionary Computing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.14830 |