Distributed Service Placement in Fog Computing: An Iterative Combinatorial Auction Approach
Bibliographic record
Abstract
A primary concern in fog computing is how to efficiently allocate limited fog resources to applications with diverse resource requirements. In fog computing, applications that consist of a set of interdependent microservices are mapped to computing and communication devices, referred to as fog nodes. While placement of microservices can be done centrally, the essentially decentralized infrastructure of participating end-user devices motivates the search for distributed solutions. In this paper, we present a distributed placement strategy that seeks to optimize energy consumption and communication costs. We devise a game-theoretic approximation method that is inspired by an iterative combinatorial auction. By properly restricting the types of bids that can be made in an auction, we can avoid the need for a centralized auctioneer. We devise a fully distributed service placement algorithm without central coordination or global state information. The algorithm operates in rounds, where the number of rounds is bounded by the number of applications and the total number of microservices. Numerical examples show that our placement algorithm outperforms existing heuristics in terms of efficiency and network utilization while achieving comparable utilization and load balancing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".