Decentralized Computation Offloading Game For Mobile Cloud Computing (1404.3200v5)

Abstract: Mobile cloud computing is envisioned as a promising approach to augment computation capabilities of mobile devices for emerging resource-hungry mobile applications. In this paper, we propose a game theoretic approach for achieving efficient computation offloading for mobile cloud computing. We formulate the decentralized computation offloading decision making problem among mobile device users as a decentralized computation offloading game. We analyze the structural property of the game and show that the game always admits a Nash equilibrium. We then design a decentralized computation offloading mechanism that can achieve a Nash equilibrium of the game and quantify its efficiency ratio over the centralized optimal solution. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.

• The paper presents a decentralized computation offloading game that guarantees Nash equilibrium in both homogeneous and heterogeneous wireless network scenarios.
• The proposed mechanism reduces computational costs by up to 33% over local processing and 38% over cloud-only strategies, achieving near-optimal performance.
• The study demonstrates rapid convergence and low communication overhead, enabling scalable and cost-effective mobile cloud computing solutions.

Decentralized Computation Offloading Game For Mobile Cloud Computing

Introduction

The paper "Decentralized Computation Offloading Game For Mobile Cloud Computing" by Xu Chen addresses the increasing demand for mobile cloud computing due to the prevalence of resource-intensive mobile applications. The core idea is to offload computational tasks from resource-constrained mobile devices to the cloud, thereby leveraging cloud resources to enhance the capabilities of mobile devices. The authors propose a decentralized game-theoretic approach to optimize the computation offloading process among multiple mobile device users.

Key Contributions

The paper offers several notable contributions:

1. Game Theoretic Model: The paper formulates the computation offloading problem as a decentralized computation offloading game. Each mobile device user aims to minimize its computational overhead by deciding whether to offload tasks to the cloud or perform them locally. The authors establish that this game always admits a Nash equilibrium for both homogenous and heterogeneous wireless network scenarios.
2. Homogenous and Heterogeneous Cases: The authors analyze the game structure for both homogenous and heterogeneous cases of wireless access. For the homogenous case, a beneficial cloud computing group is identified that guarantees Nash equilibrium. For the heterogeneous case, they show the game is a potential game, which admits the finite improvement property.
3. Decentralized Mechanism: A decentralized computation offloading mechanism is designed, enabling mobile device users to make local decisions based on the received interference and update their strategies accordingly. This mechanism ensures convergence to a Nash equilibrium, thus achieving efficient computation offloading without the need for a centralized controller.
4. Performance Metrics: The paper quantitatively evaluates the efficiency of the decentralized method. Numerical results indicate that the proposed mechanism achieves efficient performance and scales well with the increasing number of mobile device users.

Numerical Results and Implications

Efficiency and Scalability

The numerical simulations exhibit that the decentralized computation offloading mechanism substantially reduces computational overhead compared to both purely local computing and purely cloud computing strategies. The authors demonstrate up to 33% and 38% cost reductions over local and cloud computing solutions, respectively. Additionally, the mechanism's performance is within 10% of the centralized optimal solution, showcasing its efficiency.

Convergence and Communication Overhead

The practical applicability of the proposed mechanism is further validated by its quick convergence, shown to scale linearly with the number of users. The mechanism also significantly reduces the number of controlling and signaling messages compared to the centralized optimal computation offloading strategy, thus easing network congestion and enhancing system efficiency.

Future Directions

The paper lays a solid groundwork for decentralized computation offloading but acknowledges some areas for future research:

1. Dynamic Scenarios: Future research could extend the model to handle dynamic scenarios where mobile users arrive and depart during the computation offloading period. This would involve adapting to more complex real-time constraints and user mobility patterns.
2. Heterogeneous Services: Another promising direction is to explore heterogeneous cloud services beyond computation offloading, such as storage and backup services, which could offer a more comprehensive approach to mobile cloud computing.
3. Security and Privacy Concerns: While not the paper's focus, integrating data privacy and security measures in the computation offloading mechanisms will be critical. Ensuring that user data is protected while offloading and processing on the cloud will be paramount for practical deployment.

Conclusion

The paper by Xu Chen significantly advances our understanding and capability to manage computation offloading in mobile cloud computing environments. Through the innovative use of game theory, it devises a robust decentralized mechanism that balances computational load among mobile devices and cloud infrastructure efficiently. This work not only provides theoretical guarantees but also practical insights into achieving scalable and cost-effective mobile cloud computing. It exemplifies the potential for decentralized solutions in optimizing the ever-growing complexities of mobile computing in resource-constrained environments.