Service Configuration Optimization in Edge–Cloud Networks Leveraging Log Analysis
Bibliographic record
Abstract
The edge–cloud collaboration network is promising to support complex requirements with temporal constraints, where a requirement can be achieved through the composition of computation-demanding and delay-sensitive services. In this setting, most services should be optimally configured at the network edge, in order to decrease service response latency and reducing network resource consumption. To address this challenge, this article proposes an optimal service configuration mechanism, where temporal constraints between services are mined from event logs through our temporal interval discovery mechanism. Service configuration is formulated as a constrained multiobjective optimization problem, which is solved by our improved nondominated sorting genetic <xref ref-type="algorithm" rid="alg2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">algorithm II</xref> . Extensive experiments are conducted, and evaluation results demonstrate that our approach can find the close-to-optimal service configuration in comparison with the state-of-the-art techniques in terms of delay sensitivity and energy efficiency, especially when edge nodes can co-host a relatively large number of services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".