Optimization of Application Deployment Delay with Efficient Task Scheduling in Cloud-Based Smart Home Platform
Bibliographic record
Abstract
Smart home platform is an incarnation of Internet of Things (IoT) system. In such a platform, home applications are deployed using Software as a Service (SaaS) deployment model, a new way of software service provisioning for quick application deployment. However, this deployment model still has deployment performance issues due to the high degree of coordination and mutual dependencies of distributed services built on heterogeneous technologies. In a large scale deployment setup with more number of services, inter and intra-communication links between the coordinated services increase thereby introducing execution delays at service computation, and inter-service communications. Therefore, in this paper, we propose a smart home platform architecture based on Platform as a Service (PaaS) model supporting the SaaS deployment model. Based on the designed architecture, we model an optimization problem named as optimized IoT Application Deployment (OIAD) to minimize application deployment time (total execution time). To solve the OIAD problem, this paper proposes a heuristic algorithm to find a near-optimal deployment time. The results of our simulation show an improvement in comparison with FCFS (First Come First Serve) and Random execution algorithm under various deployment scenarios and strategies.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".