A Hybrid Optimized Intelligent Resource-Constrained Service Scheduling for Unified IoT Applications in Smart Cities
Bibliographic record
Abstract
As the Internet of Things (IoT) continues to advance as a technology, it has given rise to innovative and cross-domain IoT applications, particularly in smart cities. For IoT applications and services that are sensitive to latency and due resource constraints it affects the Quality of Service (QoS). To address these challenges, context-aware fog computing at the network edge requires an enhanced focus on optimizing resources for intelligent service management. Due to the dynamic change of workload at fog nodes, i.e., sudden rise in demand, an effective load balancing approach among fog nodes becomes essential. However, it’s crucial to execute load transfers, such as Virtual Machine (VM) migrations but improper migration can lead to a cascade of migrations and ultimately degrade system performance. In this paper, we introduce a resource-optimized intelligent service model (RoISM) designed to facilitate resource optimization through a forecasting technique. This technique predicts the requisite context instances and resource computation needed for efficient service delivery. The proposed hybrid approach to service management leverages context-sharing, context-migration, and live service migration strategies, all based on the forecast method. This method utilizes both current and predicted resource utilization data, as well as context availability, to fulfil service requests within the specified latency requirements for cross-domain IoT applications. To validate the effectiveness of our proposed service management algorithms, we conducted simulations using a CloudSim simulator. The results obtained from these simulations confirm the superiority of our proposed methods
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".