Service Migration for Delay-Sensitive IoT Applications in Edge Networks
Bibliographic record
Abstract
The proliferation ofInternetofThings (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided byIoTdevices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated asIoTservices. Thereafter,IoTapplications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguousIoTservices. The status of computational resources inIoTdevices may change continuously along with their occupancy and release byIoTservices. Considering the resource-scarceness ofIoTdevices, when the workload ofIoTdevices increases due to more services to be processed, certainIoTdevices may hardly have enough remaining resources to co-host more instances of certainIoTservices prescribed by forthcomingIoTapplications with strict constraints. As a result, the delay satisfaction of both on-running and forthcomingIoTapplications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient serviceConfiguration ($E^{2}$rC) mechanism, which aims to optimize the configuration of computational resources provided byIoTdevices with respect to complex requirements prescribed byIoTapplications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhancedDeepReinforcementLearning (DRL) approach with a two-layerQ-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our$E^{2}$rCis more efficient than the state-of-art counterparts in satisfying delay constraints ofIoTapplications, while reducing the energy consumption and improving the resource utilization efficiency ofIoTdevices.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".