- The paper presents a Lyapunov optimization approach that achieves an [O(1/V), O(V)] tradeoff between power consumption and execution delay.
- It introduces a low-complexity online algorithm that dynamically adjusts CPU frequencies, transmission power, and bandwidth based on real-time system variations.
- Simulation results validate that optimal resource allocation stabilizes task buffers while efficiently balancing energy use and delay.
Stochastic Joint Radio and Computational Resource Management for Multi-User MEC Systems
The paper under review presents an online algorithm for managing radio and computational resources in Multi-User Mobile-Edge Computing (MEC) systems. This work targets the optimization of MEC systems, which aim to alleviate the computational burden on mobile devices by offloading tasks to nearby MEC servers.
Problem Context and Objective
MEC is positioned as a crucial enabler for mobile devices constrained by limited computational capabilities. This necessity arises from the increasing demand imposed by computation-intensive applications like 3D modeling and gesture recognition. The primary objective of the paper is to minimize the long-term average weighted sum power consumption of the mobile devices alongside the MEC server, under the constraint of task buffer stability. The challenge lies in the dynamic management of computational resources to balance power consumption while maintaining task execution delay within acceptable bounds.
Methodology
The authors employ Lyapunov optimization to develop a low-complexity online algorithm that accomplishes joint management of radio and computational resources. Specifically, the algorithm decides on the optimal CPU-cycle frequencies for both the mobile devices and the MEC server, along with optimal transmission power and bandwidth allocation required for task offloading. The critical advantage of the proposed solution is its adaptability to real-time variations in wireless channel conditions and task arrivals, without necessitating prior knowledge of these stochastic processes.
Key Findings and Results
The algorithm guarantees an [O(1/V), O(V)] tradeoff between the average weighted sum power consumption and the execution delay. This tradeoff implies that as the control parameter V increases, power consumption approaches optimal levels at the expense of higher execution delays. Conversely, smaller V achieves reduced delays with increased power consumption.
Simulation results confirm the theoretical findings, revealing how parameters such as task arrival rates and MEC server computational capacity influence the power-delay tradeoff. The paper also highlights the significance of effectively balancing the compute resources between local execution and offloading tasks based on current queue lengths and channel conditions.
Implications and Future Directions
From a practical viewpoint, this research informs MEC deployment strategies by detailing how computational and radio resources should be orchestrated to balance energy efficiency and computational latency. Theoretical contributions include a deeper understanding of the implications of varying system parameters on achieving task buffer stability without compromising on power consumption.
Future research might explore distributed implementations or adaptations of this algorithm in decentralized settings where multiple MEC servers exist. Additionally, integrating mobility awareness into the resource allocation framework may yield pertinent insights, especially for scenarios involving varying user dynamics and mobility patterns.
This work underscores the potential of stochastic optimization methodologies in effectively leveraging the combined computational power of mobile devices and MEC systems, paving the way for more responsive and energy-efficient mobile application experiences.