Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing (2402.11653v2)

Abstract: Recently, there has been an explosion of mobile applications that perform computationally intensive tasks such as video streaming, data mining, virtual reality, augmented reality, image processing, video processing, face recognition, and online gaming. However, user devices (UDs), such as tablets and smartphones, have a limited ability to perform the computation needs of the tasks. Mobile edge computing (MEC) has emerged as a promising technology to meet the increasing computing demands of UDs. Task offloading in MEC is a strategy that meets the demands of UDs by distributing tasks between UDs and MEC servers. Deep reinforcement learning (DRL) is gaining attention in task-offloading problems because it can adapt to dynamic changes and minimize online computational complexity. However, the various types of continuous and discrete resource constraints on UDs and MEC servers pose challenges to the design of an efficient DRL-based task-offloading strategy. Existing DRL-based task-offloading algorithms focus on the constraints of the UDs, assuming the availability of enough storage resources on the server. Moreover, existing multiagent DRL (MADRL)--based task-offloading algorithms are homogeneous agents and consider homogeneous constraints as a penalty in their reward function. We proposed a novel combinatorial client-master MADRL (CCM_MADRL) algorithm for task offloading in MEC (CCM_MADRL_MEC) that enables UDs to decide their resource requirements and the server to make a combinatorial decision based on the requirements of the UDs. CCM_MADRL_MEC is the first MADRL in task offloading to consider server storage capacity in addition to the constraints in the UDs. By taking advantage of the combinatorial action selection, CCM_MADRL_MEC has shown superior convergence over existing MADDPG and heuristic algorithms.

• The paper presents a novel client-master MADRL architecture that improves task offloading efficiency in mobile edge computing.
• It incorporates explicit resource and channel constraints into combinatorial decision-making, enhancing convergence and reducing latency.
• Heuristic benchmarks demonstrate that CCM_MADRL outperforms existing methods in energy savings and computational latency for mobile applications.

Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing

The paper presents a novel multiagent deep reinforcement learning (MADRL) approach, termed combinatorial client-master MADRL (CCM_MADRL), targeted at optimizing task offloading in the context of mobile edge computing (MEC). Given the rapid proliferation of computationally intense mobile applications, such as virtual reality, online gaming, and video processing, there arises a critical need to efficiently manage computational resources and task distribution between user devices (UDs) and edge servers. UDs, often limited by computational power and battery life, see the offloading of tasks to MEC servers as a viable solution to mitigate their resource constraints. This paper addresses the complexity of developing an efficient offloading strategy that accounts for both discrete and continuous resource restrictions inherent at both UDs and MEC servers.

Key Contributions

1. Novel Architecture - CCM_MADRL: The paper introduces a unique modeling framework that employs a client-master agent architecture. In this setting, client agents at user devices make local resource allocation decisions and propose tasks for offloading. The master agent, centralized at the edge server, then processes these proposals to make combinatorial decisions about which tasks to accept for execution at the server.
2. Combinatorial Decision-Making: Unlike prevailing DRL methodologies, which often oversimplify by assuming abundant server resources or penalizing constraints as part of reward functions, the CCM_MADRL approach incorporates explicit server storage and channel constraints in its decision-making process. This level of granularity allows for more realistic and efficient task allocation.
3. Enhanced Convergence: The proposed algorithm demonstrates superior convergence properties in comparison to existing MADDPG-based algorithms, by leveraging combinatorial action space exploration. This improvement is particularly critical in scenarios involving high dimensionality and complexity, enabling more efficient learning dynamics.
4. Heuristic Benchmarks and Evaluation: To gauge the efficiency of CCM_MADRL, the authors developed and utilized heuristic benchmarks, revealing that the combination of a master agent to guide task allocation results in reduced computational latency and energy consumption.

Theoretical and Practical Implications

Theoretically, CCM_MADRL advances the discourse in multiagent systems by proposing a strategy that effectively integrates resource constraints into the learning model, enhancing the applicability of MADRL frameworks to real-world, resource-constrained environments. It challenges existing paradigms by evidencing how explicit combinatorial strategies can significantly enhance decision accuracy and convergence rates.

Practically, this research offers a framework that could be directly applicable in dynamic environments where UDs frequently experience fluctuating loads and resource availabilities. This is particularly relevant for MEC scenarios, as characterized by tight latency bounds and diverse application demands. The CCM_MADRL model stands to impact how computational tasks are managed in next-generation networks, highlighting pathways for optimizing edge computing architectures in the face of growing computational demands.

Future Developments

The work sets a foundation for further research into MADRL-driven MEC strategies, particularly in extending the framework to multi-server settings. Future research could focus on enhancing agent coordination mechanisms, extending the model's applicability across more heterogeneous network scenarios, and accommodating broader classes of tasks with varying resource and service requirements.

In conclusion, this paper's contributions vividly illustrate the potential of integrating combinatorial decision-making within MADRL frameworks to address the intricate challenges posed by modern computational landscape demands. Its implications underscore a pragmatic step forward in the ongoing evolution of edge computing and resource management strategies.