2 research outputs found
An Incentive-Aware Job Offloading Control Framework for Mobile Edge Computing
This paper considers a scenario in which an access point (AP) is equipped
with a mobile edge server of finite computing power, and serves multiple
resource-hungry mobile users by charging users a price. Pricing provides users
with incentives in offloading. However, existing works on pricing are based on
abstract concave utility functions (e.g, the logarithm function), giving no
dependence on physical layer parameters. To that end, we first introduce a
novel utility function, which measures the cost reduction by offloading as
compared with executing jobs locally. Based on this utility function we then
formulate two offloading games, with one maximizing individual's interest and
the other maximizing the overall system's interest. We analyze the structural
property of the games and admit in closed form the Nash Equilibrium and the
Social Equilibrium, respectively. The proposed expressions are functions of the
user parameters such as the weights of computational time and energy, the
distance from the AP, thus constituting an advancement over prior economic
works that have considered only abstract functions. Finally, we propose an
optimal pricing-based scheme, with which we prove that the interactive
decision-making process with self-interested users converges to a Nash
Equilibrium point equal to the Social Equilibrium point.Comment: 13 pages, 9 figure
Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach
We investigate the problem of computation offloading in a mobile edge
computing architecture, where multiple energy-constrained users compete to
offload their computational tasks to multiple servers through a shared wireless
medium. We propose a multi-agent deep reinforcement learning algorithm, where
each server is equipped with an agent, observing the status of its associated
users and selecting the best user for offloading at each step. We consider
computation time (i.e., task completion time) and system lifetime as two key
performance indicators, and we numerically demonstrate that our approach
outperforms baseline algorithms in terms of the trade-off between computation
time and system lifetime.Comment: Presented at the 2019 Asilomar Conference on Signals, Systems, and
Computer