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DDPS: Dynamic Differential Pricing-based Edge Offloading System with Energy Harvesting Devices

Hai Xue, Yun Xia, Neal N. Xiong, Di Zhang, Songwen Pei

TL;DR

DDPS tackles MEC offloading with energy-harvesting devices by introducing a dynamic differential pricing scheme that accounts for per-user server resource usage. It pairs an energy-aware offloading decision with a Stackelberg game between mobile users and the edge server, and includes a redistribution mechanism to fully utilize excess server capacity. Through extensive simulations, DDPS improves edge server utility, increases the ratio of served users, and reduces average user latency compared to baseline pricing schemes. The work offers a practical incentive mechanism for sustainable and efficient edge computing in energy-constrained IoT deployments.

Abstract

Mobile edge computing (MEC) paves the way to alleviate the burden of energy and computation of mobile users (MUs) by offloading tasks to the network edge. To enhance the MEC server utilization by optimizing its resource allocation, a well-designed pricing strategy is indispensable. In this paper, we consider the edge offloading scenario with energy harvesting devices, and propose a dynamic differential pricing system (DDPS), which determines the price per unit time according to the usage of computing resources to improve the edge server utilization. Firstly, we propose an offloading decision algorithm to decide whether to conduct the offloading operation and how much data to be offloaded if conducted, the algorithm determines offloading operation by balancing the energy harvested with the energy consumed. Secondly, for the offloading case, we formulate the game between the MUs and the server as a Stackelberg game, and propose a differential pricing algorithm to determine the optimal computing resources required by MUs. Furthermore, the proposed algorithm also reallocates computing resources for delay-sensitive devices while server resources are surplus after the initial allocation, aiming to make full use of the server computing resources. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme.

DDPS: Dynamic Differential Pricing-based Edge Offloading System with Energy Harvesting Devices

TL;DR

DDPS tackles MEC offloading with energy-harvesting devices by introducing a dynamic differential pricing scheme that accounts for per-user server resource usage. It pairs an energy-aware offloading decision with a Stackelberg game between mobile users and the edge server, and includes a redistribution mechanism to fully utilize excess server capacity. Through extensive simulations, DDPS improves edge server utility, increases the ratio of served users, and reduces average user latency compared to baseline pricing schemes. The work offers a practical incentive mechanism for sustainable and efficient edge computing in energy-constrained IoT deployments.

Abstract

Mobile edge computing (MEC) paves the way to alleviate the burden of energy and computation of mobile users (MUs) by offloading tasks to the network edge. To enhance the MEC server utilization by optimizing its resource allocation, a well-designed pricing strategy is indispensable. In this paper, we consider the edge offloading scenario with energy harvesting devices, and propose a dynamic differential pricing system (DDPS), which determines the price per unit time according to the usage of computing resources to improve the edge server utilization. Firstly, we propose an offloading decision algorithm to decide whether to conduct the offloading operation and how much data to be offloaded if conducted, the algorithm determines offloading operation by balancing the energy harvested with the energy consumed. Secondly, for the offloading case, we formulate the game between the MUs and the server as a Stackelberg game, and propose a differential pricing algorithm to determine the optimal computing resources required by MUs. Furthermore, the proposed algorithm also reallocates computing resources for delay-sensitive devices while server resources are surplus after the initial allocation, aiming to make full use of the server computing resources. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme.
Paper Structure (20 sections, 4 theorems, 43 equations, 7 figures, 2 tables, 2 algorithms)

This paper contains 20 sections, 4 theorems, 43 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. Game pricing
  4. Auction pricing
  5. Dynamic Pricing Based on Physical Layer Parameters
  6. System model
  7. MU energy consumption model
  8. Queue model
  9. Pricing model for the server
  10. The proposed DDPS
  11. The utility function of MU and the optimal policy
  12. The optimal price of the edge server
  13. Performance evaluation
  14. Simulation Setup
  15. Impact of edge server computation capacity on average execution latency
  16. ...and 5 more sections

Key Result

Lemma 1

The payment $W_i$ is an increasing function concerning the offload data size $q_i$ and the CPU capacity of the server utilized by MUs $F_i$, respectively.

Figures (7)

  • Figure 1: System model.
  • Figure 2: The procedure of Stackelberg game.
  • Figure 3: Effect of different $\lambda$ on average latency.
  • Figure 4: Effect of different $\lambda$ on $U_{server}$.
  • Figure 5: Effect of different $\lambda$ on average latency.
  • ...and 2 more figures

Theorems & Definitions (8)

  • Lemma 1
  • Proof 1
  • Lemma 2
  • Proof 2
  • Lemma 3
  • Proof 3
  • Lemma 4
  • Proof 4