Workload Balancing in Mobile Edge Computing for Internet of Things: A Population Game Approach
Bibliographic record
Abstract
Mobile edge computing (MEC) is an emerging paradigm that provides radio access networks with augmented resources to meet the requirements of Internet of Things (IoT) services. MEC allows IoT devices to offload delay sensitive and computation intensive tasks to edge clouds deployed at base stations (BSs). Offloading tasks to edge clouds can alleviate the computing and battery limitations of IoT devices. However, task offloading in MEC for IoT may face serious transmission latency and computation latency problems with massive number of IoT devices. Moreover, some edge clouds can be overloaded due to the spatially inhomogeneous distributions of IoT tasks. To solve these problems, we investigate the workload balancing problems to minimize the transmission latency and computation latency in task offloading process while considering the limited bandwidth resources of BSs and computation resources in edge clouds. We formulate the workload balancing problem as a population game in order to analyze the aggregate offloading decisions. We analyze the aggregate offloading decisions of mobile users through evolutionary game dynamics and show that the game always achieves a Nash equilibrium (NE). We further propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show that our proposed workload balancing algorithms can achieve better performance than existing solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".