Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Repeated Auction Model for Load-Aware Dynamic Resource Allocation in Multi-Access Edge Computing

Ummy Habiba, Setareh Maghsudi, Ekram Hossain

TL;DR

This paper tackles dynamic MEC resource allocation in a multi-vendor setting by formulating a repeated GSP auction framework that matches offloading tasks to VM resources across multiple servers. It designs a workload-aware, polynomial-time resource allocation and pricing algorithm, supported by adaptive Restricted Balanced Bidding (RBB) strategies that guarantee IR and converge to symmetric Nash equilibria in every auction round. The approach balances social welfare, server profits, and UE QoE, and is shown to outperform VCG in terms of server revenue and overall welfare, while preserving practical computational efficiency. The results demonstrate robust performance under dynamic arrivals, varying workloads, and multi-seller competition, highlighting the method’s potential for scalable, market-based MEC orchestration.

Abstract

Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6 G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence, effective resource management is essential; nevertheless, state-of-the-art research lacks efficient economic models to support the exponential growth of the MEC-enabled applications market. We focus on designing a MEC offloading service market based on a repeated auction model with multiple resource sellers (e.g., network operators and service providers) that compete to sell their computing resources to the offloading users. We design a computationally-efficient modified Generalized Second Price (GSP)-based algorithm that decides on pricing and resource allocation by considering the dynamic offloading requests arrival and the servers' computational workloads. Besides, we propose adaptive best-response bidding strategies for the resource sellers, satisfying the symmetric Nash equilibrium (SNE) and individual rationality properties. Finally, via intensive numerical results, we show the effectiveness of our proposed resource allocation mechanism.

A Repeated Auction Model for Load-Aware Dynamic Resource Allocation in Multi-Access Edge Computing

TL;DR

This paper tackles dynamic MEC resource allocation in a multi-vendor setting by formulating a repeated GSP auction framework that matches offloading tasks to VM resources across multiple servers. It designs a workload-aware, polynomial-time resource allocation and pricing algorithm, supported by adaptive Restricted Balanced Bidding (RBB) strategies that guarantee IR and converge to symmetric Nash equilibria in every auction round. The approach balances social welfare, server profits, and UE QoE, and is shown to outperform VCG in terms of server revenue and overall welfare, while preserving practical computational efficiency. The results demonstrate robust performance under dynamic arrivals, varying workloads, and multi-seller competition, highlighting the method’s potential for scalable, market-based MEC orchestration.

Abstract

Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6 G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence, effective resource management is essential; nevertheless, state-of-the-art research lacks efficient economic models to support the exponential growth of the MEC-enabled applications market. We focus on designing a MEC offloading service market based on a repeated auction model with multiple resource sellers (e.g., network operators and service providers) that compete to sell their computing resources to the offloading users. We design a computationally-efficient modified Generalized Second Price (GSP)-based algorithm that decides on pricing and resource allocation by considering the dynamic offloading requests arrival and the servers' computational workloads. Besides, we propose adaptive best-response bidding strategies for the resource sellers, satisfying the symmetric Nash equilibrium (SNE) and individual rationality properties. Finally, via intensive numerical results, we show the effectiveness of our proposed resource allocation mechanism.
Paper Structure (23 sections, 4 theorems, 26 equations, 11 figures, 4 tables, 3 algorithms)

This paper contains 23 sections, 4 theorems, 26 equations, 11 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Contributions
  3. Related Work
  4. System Model and Assumptions
  5. Wireless Network and Communication Model
  6. MEC Service Provisioning Model
  7. MEC Orchestration Model
  8. Utility Model of MEC Servers
  9. Utility Model of Offloading Users
  10. GSP-Based Auction Mechanism Design for Dynamic Computation Offloading and Resource Allocation
  11. Winner Determination Problem Formulation
  12. WDP Solution Approaches
  13. GSP-Based Resource Allocation and Pricing Mechanism
  14. Repeated GSP-Based Dynamic Resource Allocation and Pricing Mechanism
  15. A Toy Example
  16. ...and 8 more sections

Key Result

Theorem 5.1

The GSP-based MEC offloading auction at each processor $n$ guarantees the individual rationality for every participating VM, when the servers follow the proposed RBB strategy in every round of auction.

Figures (11)

  • Figure 1: SDN-based multi-tier multi-access edge computing infrastructure and communication network architecture.
  • Figure 2: Service-based MEC system architecture.
  • Figure 3: Workflow of the computation offloading mechanism in MEC framework.
  • Figure 4: An illustration of incoming offloading requests and VM instances at MEC processors.
  • Figure 5: An example illustrating the GSP-based task assignment, VM allocation, and pricing.
  • ...and 6 more figures

Theorems & Definitions (12)

  • Example 1
  • Definition 1
  • Definition 2
  • Theorem 5.1
  • proof
  • Definition 3
  • Theorem 5.2
  • proof
  • Theorem 5.3
  • proof
  • ...and 2 more