A robust optimization approach for placement of applications in edge computing considering latency uncertainty
Bibliographic record
Abstract
Edge computing brings computing and storage resources close to end-users to support new applications and services that require low network latency. It is currently used in a wide range of industries, from industrial automation and augmented reality, to smart cities and connected vehicles, where low latency, data privacy, and real-time processing are critical requirements. The latency of accessing applications in edge computing must be consistently below a threshold of a few tens of milliseconds to maintain an acceptable experience for end-users. However, the latency between users and applications can vary considerably depending on the network load and mode of wireless access. An application provider must be able to guarantee that requests are served in a timely manner by their application instances hosted in the edge despite such latency variations. This article focuses on the placement and traffic allocation problem faced by application providers in determining where to place application instances on edge nodes such that requests are served within a certain deadline. It proposes novel formulations based on robust optimization to provide optimal plans that protect against latency variations in a configurable number of network links. The robust formulations are based on two different types of polyhedral uncertainty sets that offer different levels of protection against variations in latency. Extensive simulations show that our robust models are able to keep the number of chosen edge nodes low while reducing the number of latency violations as compared to a deterministic optimization model that only considers the average latency of network links.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".