Source-linked AI summary
Decentralized Computation Offloading Game For Mobile Cloud Computing
Xu Chen
TL;DR
Mobile devices need computation offloading for resource-hungry applications, but users sharing wireless access must coordinate to avoid interference. The paper formulates this interaction as a decentralized computation offloading game and designs a local-decision mechanism. The game admits Nash equilibrium, and numerical results indicate efficient performance that scales with system size.
Problem
Resource-hungry applications strain mobile devices, while simultaneous multi-user offloading can cause wireless interference and inefficient computation offloading.
Method
The paper formulates multi-user offloading as a decentralized game incorporating communication and computation, then designs a mechanism whose users make decisions locally.
Results
The game always admits a Nash equilibrium in homogeneous and heterogeneous wireless-access cases, and the mechanism achieves equilibrium with efficient, scalable offloading performance.
Takeaways & Limitations
Decentralized game-theoretic coordination can produce mutually satisfactory offloading decisions while reducing cloud control and signaling overhead.
Takeaways & Limitations
The analysis assumes the set of mobile users remains unchanged during each computation offloading period; dynamic departures are left for future work.
Abstract
from arXiv · showhide
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.
1 INTRODUCTION
Mobile cloud computing can augment resource-constrained mobile devices, but efficient offloading must coordinate users sharing wireless access. The paper models this coordination as a decentralized game and develops a mechanism with equilibrium and efficiency guarantees.
- Motivation: Resource-hungry applications demand intensive computation and energy, while mobile devices have limited computation resources and battery life.
- Motivation: Cloud offloading augments mobile-device capabilities by sending computation through wireless access to resource-rich cloud infrastructure.
- Coordination challenge: Simultaneous offloading can create severe wireless interference, reducing transmission rates, energy efficiency, and computation-data transmission performance.
- Approach: The paper formulates multi-user offloading decisions as a decentralized computation offloading game incorporating communication and computation aspects.
- Game structure: In homogeneous access, the game has a beneficial cloud-computing group structure guaranteeing Nash-equilibrium existence; in heterogeneous access, it is a potential game with the same guarantee.
- Mechanism: The decentralized mechanism lets users decide locally, reduces cloud control and signaling overhead, achieves Nash equilibrium, and scales well as system size increases.
2 RELATED WORK
Prior work largely studied computation offloading for single mobile-device users, while fewer studies addressed multiple users sharing wireless resources. Existing multi-user approaches included centralized heuristic or greedy schemes.
- Most previous offloading research designed mechanisms from the perspective of a single mobile-device user.
- Related single-user and system-level studies targeted energy savings, adaptive offloading, timeout design, scalability, and execution-time constraints.
- Only a few works addressed computation offloading with multiple mobile-device users.
- For multiple users, prior approaches included a centralized heuristic genetic algorithm for shared bandwidth and a centralized greedy scheme incorporating user mobility.
3 SYSTEM MODEL
The system models quasi-static mobile users choosing local computation or cloud offloading through a wireless access point. It combines communication interference, task computation, time, and energy overheads to represent coupled offloading decisions.
- System setting: The model considers N collocated mobile users with computationally intensive, delay-sensitive tasks and a wireless access base station.
- System setting: Users remain unchanged during each offloading period, while user membership may change across periods; dynamic departures are deferred to future work.
- Communication model: Each user chooses a_n=1 to offload through wireless access or a_n=0 to compute locally, with the joint profile determining uplink data rates.
- Communication model: The wireless model accounts for bandwidth, transmission power, channel gain, and background interference in determining offloading data rates.
- Computation model: Each task I_n=(B_n,D_n) contains input-data size B_n and required CPU cycles D_n, and can run locally or remotely in the cloud.
- Computation model: The model evaluates local and cloud execution using computational time and energy, including wireless transmission overhead for offloading.
- Assumption: The model neglects cloud-to-device result-transmission time because application outputs are generally smaller than computation inputs.
- Coupled decisions: Offloading decisions are coupled because simultaneous offloading increases interference, lowering data rates and increasing transmission energy and time.
4 DECENTRALIZED COMPUTATION OFFLOADING GAME
The paper models decentralized offloading as a two-action game in which users minimize energy and processing-time overhead. It establishes Nash-equilibrium existence for homogeneous and heterogeneous wireless access, using threshold best responses, beneficial cloud-computing groups, and potential-game structure.
- Game formulation: The decentralized computation offloading game represents users as players choosing local or cloud computing to minimize individual overhead.Each user’s cost incorporates the selected computation mode and depends on other users’ decisions.
- Best responses: A user’s best response is a threshold strategy: offload when received interference is low enough, otherwise compute locally.The threshold compares received interference from other offloading users with the user’s computation threshold.
- Homogeneous wireless access: In homogeneous wireless access, the game always has a Nash equilibrium, including either universal local computing or a beneficial cloud-computing group.For nonnegative threshold conditions, Algorithm 1 constructs a nonempty group whose members offload while the others compute locally.
- Homogeneous wireless access: Algorithm 1 constructs a homogeneous-case Nash equilibrium with computational complexity O(N log N).The complexity is dominated by ordering users; group construction adds at most N constant-time operations.
- Heterogeneous wireless access: For heterogeneous wireless access, the game is a potential game and therefore has the finite improvement property and a Nash equilibrium.Any asynchronous better-response update process reaches a Nash equilibrium after finitely many updates.
5 DECENTRALIZED COMPUTATION OFFLOADING MECHANISM
The proposed mechanism coordinates decentralized decision updates by measuring interference and allowing one improving user to update at a time. It exploits finite improvement to reach a Nash equilibrium, with efficiency evaluated against the centralized optimum.
- Mechanism design: The mechanism achieves a Nash equilibrium by letting mobile users improve their offloading decisions through decentralized coordination.Its design uses the game’s finite improvement property and permits one user to update at each decision opportunity.
- Mechanism design: Users locally measure interference from current offloading users before computing their best-response update sets.Pilot signals and base-station feedback support local interference measurement.
- Mechanism design: Users with an improving decision contend for a single update opportunity, and the winner broadcasts a request-to-update message.A random backoff mechanism selects the updating user; others retain their current decisions for the next slot.
- Complexity: The algorithm’s computational complexity is O(CN), where N is the number of users and C is the convergence iteration count.Each iteration performs O(N) basic operations, and numerical results determine the iterations needed for convergence.
- Efficiency: Theorem 3 relates the price of anarchy to local-computing cost and communication efficiency, while the mechanism may randomly select among multiple equilibria.Lower local-computing cost and higher communication efficiency improve the reported equilibrium performance.
- Efficiency: The mechanism’s Nash equilibrium has at most 10% performance loss relative to the centralized optimal solution.The paper evaluates worst-case equilibrium efficiency using the price of anarchy, which is lower bounded by 1.
6 NUMERICAL RESULTS
Numerical studies show that the decentralized mechanism converges to a Nash equilibrium, achieves low system-wide computing cost, scales with system size, and substantially reduces signaling overhead.
- Convergence and equilibrium: The decentralized mechanism keeps users’ computation costs decreasing and converges to a Nash equilibrium by minimizing the game’s potential function.The equilibrium is identified through the potential-game property.
- Impact of computation size: As CPU processing cycles increase, decentralized offloading’s system-wide computing cost grows more slowly than local mobile computing.More users choose cloud computing to mitigate the increasing cost of local computation.
- Impact of data size: As offloading data size increases, decentralized offloading cost rises because wireless communication overhead increases, while growth slows for large data sizes.With large data sizes, more users compute locally to avoid heavy offloading costs.
- Comparison with benchmarks: The decentralized mechanism’s performance loss relative to centralized optimization is less than 10% in all tested cases.It reduces computing cost by up to 33% versus all-local computing and 38% versus all-cloud computing.
- Scalability: Average convergence time increases linearly with the number of mobile device users, while centralized optimization has exponentially growing computational complexity.The centralized solution requires solving an integer programming problem with binary decision variables.
- Communication overhead: The decentralized mechanism reduces controlling and signaling messages by at least 89% compared with centralized optimal offloading in all cases.Users exchange messages only when updating decisions, rather than reporting all local parameters to the cloud.
7 CONCLUSION
The paper formulates decentralized computation offloading as a game, establishes Nash-equilibrium existence, and designs a decentralized mechanism whose efficiency and scalability are demonstrated numerically. Future work extends the model to users departing dynamically during an offloading period.
- The decentralized computation offloading game always admits a Nash equilibrium under homogeneous and heterogeneous wireless access.
- A decentralized mechanism achieves a Nash equilibrium and quantifies its price of anarchy.
- Numerical results show that the proposed mechanism is efficient and scales well as system size increases.
- Future work considers mobile users departing dynamically within a computation offloading period, with mobility patterns potentially affecting the formulation.
8 APPENDIX
The appendix proves equilibrium properties by comparing users’ best responses and constructing beneficial cloud-computing groups. It also uses these response relationships to establish a potential-function argument and constrain equilibrium utilities.
- The proof establishes Nash equilibrium by showing that each user’s indicated strategy is a best response to the others.
- When K < 0, the empty cloud-computing group is a Nash equilibrium because every user’s strategy an = 0 is a best response.
- When K ≥ 0, Algorithm 1 constructs a beneficial cloud-computing group by adding ordered users while the stated group-size condition remains satisfied.
- The construction stops at the first violated size condition, yielding a group that satisfies the beneficial-group inequalities.
- The proof shows that a user’s preference between strategies changes consistently with the potential function, supporting the game’s equilibrium analysis.
- At any Nash equilibrium, a user cannot obtain utility above the stated threshold because choosing an = 0 would otherwise improve its outcome.