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Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks

Houyi Qi, Minghui Liwang, Xianbin Wang, Li Li, Wei Gong, Jian Jin, Zhenzhen Jiao

TL;DR

This work tackles dynamic, multi-layer resource provisioning in CAMENs by introducing a hybrid market that combines futures (OA-CLM) and spot (OS-CLM) trading with overbooking to manage uncertainty across MUs, ESs, and CSs. The OA-CLM framework uses two forward-contract matchings—MU-ES (M2O) and ES-CS (M2M)—driven by Gale-Shapley, with risk constraints ($R^U_i$, $R^E_i$, $R^C$) and stability guarantees (strong stability, competitive equilibrium, and weak Pareto optimality). A spot-market OS-CLM backup plan adds volunteers and onsite cross-layer matching to ensure reliability under current conditions. Extensive simulations on numerical and real-world EUA data demonstrate improvements in social welfare, time/energy efficiency, and reduced matching overhead, validating the proposed risk-aware cross-layer design for dynamic CAMENs. The approach provides a practical path toward flexible, contract-aware resource trading in edge-cloud ecosystems, with potential extensions to smart contracts and cross-provider collaborations.

Abstract

Cloud-aided mobile edge networks (CAMENs) allow edge servers (ESs) to purchase resources from remote cloud servers (CSs), while overcoming resource shortage when handling computation-intensive tasks of mobile users (MUs). Conventional trading mechanisms (e.g., onsite trading) confront many challenges, including decision-making overhead (e.g., latency) and potential trading failures. This paper investigates a series of cross-layer matching mechanisms to achieve stable and cost-effective resource provisioning across different layers (i.e., MUs, ESs, CSs), seamlessly integrated into a novel hybrid paradigm that incorporates futures and spot trading. In futures trading, we explore an overbooking-driven aforehand cross-layer matching (OA-CLM) mechanism, facilitating two future contract types: contract between MUs and ESs, and contract between ESs and CSs, while assessing potential risks under historical statistical analysis. In spot trading, we design two backup plans respond to current network/market conditions: determination on contractual MUs that should switch to local processing from edge/cloud services; and an onsite cross-layer matching (OS-CLM) mechanism that engages participants in real-time practical transactions. We next show that our matching mechanisms theoretically satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Comprehensive simulations in real-world and numerical network settings confirm the corresponding efficacy, while revealing remarkable improvements in time/energy efficiency and social welfare.

Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks

TL;DR

This work tackles dynamic, multi-layer resource provisioning in CAMENs by introducing a hybrid market that combines futures (OA-CLM) and spot (OS-CLM) trading with overbooking to manage uncertainty across MUs, ESs, and CSs. The OA-CLM framework uses two forward-contract matchings—MU-ES (M2O) and ES-CS (M2M)—driven by Gale-Shapley, with risk constraints (, , ) and stability guarantees (strong stability, competitive equilibrium, and weak Pareto optimality). A spot-market OS-CLM backup plan adds volunteers and onsite cross-layer matching to ensure reliability under current conditions. Extensive simulations on numerical and real-world EUA data demonstrate improvements in social welfare, time/energy efficiency, and reduced matching overhead, validating the proposed risk-aware cross-layer design for dynamic CAMENs. The approach provides a practical path toward flexible, contract-aware resource trading in edge-cloud ecosystems, with potential extensions to smart contracts and cross-provider collaborations.

Abstract

Cloud-aided mobile edge networks (CAMENs) allow edge servers (ESs) to purchase resources from remote cloud servers (CSs), while overcoming resource shortage when handling computation-intensive tasks of mobile users (MUs). Conventional trading mechanisms (e.g., onsite trading) confront many challenges, including decision-making overhead (e.g., latency) and potential trading failures. This paper investigates a series of cross-layer matching mechanisms to achieve stable and cost-effective resource provisioning across different layers (i.e., MUs, ESs, CSs), seamlessly integrated into a novel hybrid paradigm that incorporates futures and spot trading. In futures trading, we explore an overbooking-driven aforehand cross-layer matching (OA-CLM) mechanism, facilitating two future contract types: contract between MUs and ESs, and contract between ESs and CSs, while assessing potential risks under historical statistical analysis. In spot trading, we design two backup plans respond to current network/market conditions: determination on contractual MUs that should switch to local processing from edge/cloud services; and an onsite cross-layer matching (OS-CLM) mechanism that engages participants in real-time practical transactions. We next show that our matching mechanisms theoretically satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Comprehensive simulations in real-world and numerical network settings confirm the corresponding efficacy, while revealing remarkable improvements in time/energy efficiency and social welfare.
Paper Structure (41 sections, 20 theorems, 86 equations, 7 figures, 4 tables)

This paper contains 41 sections, 20 theorems, 86 equations, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Hybrid resource trading: bridge present and future
  4. Overbooking: a helpful tool in dealing with dynamics
  5. Investigation
  6. Novelty and Contribution
  7. System Overview and Modeling
  8. Overview
  9. Preliminary Modeling
  10. OA-CLM Game in Futures Market
  11. Utility, Expected Utility and Risk
  12. Utility, expected utility and risk of MUs
  13. Utility, expected utility and risk of ESs
  14. Utility, expected utility and risk of CSs
  15. Key Definitions in OA-CLM Game
  16. ...and 26 more sections

Key Result

Lemma 1

(Convergence of MU-ES matching of OA-CLM) Phase 1 of Algorithm 1 converges within finite rounds.

Figures (7)

  • Figure 1: Framework and procedure in terms of a timeline associated with our proposed cross-layer matching game in dynamic CAMENs.
  • Figure 2: Performance comparisons in terms of social welfare, where (a) 110 ESs, (b) 125 ESs, (c) 650 MUs, and (d) 800 MUs.
  • Figure 3: Performance comparisons in terms of running time, the number of interactions and practical task completion time.
  • Figure 4: Individual rationality in terms of utilities.
  • Figure 5: Performance comparisons in terms of running time and number of interaction under different overbooking rates ($\tau$).
  • ...and 2 more figures

Theorems & Definitions (64)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Definition 7
  • Definition 8
  • Definition 9
  • Definition 10
  • ...and 54 more