Dynamic Resource Allocation of Smart Home Workloads in the Cloud
Bibliographic record
Abstract
Cloud computing offers provision for elastic and scalable infrastructure resource allocation across the network that allows deployment of services for controlling home devices and appliances. Data generated from heterogeneous smart home devices are processed in different application services deployed in the cloud data center. The primary challenge of smart home service provider s is to optimize the cloud resource allocation while satisfying the Quality of Service(QoS) constraints of the application services. Service execution time is one of the most vital QoS parameters. In this paper, a queuing theoretic approach is proposed to model the smart home workload. First, M/M/c queue model is applied to find the response time of smart home tasks with light variation over the arrival rate. Then, Markovian Modulated Poisson Process (MMPP) is used to extend the model to a more advanced type of smart home processing workloads. Next, the optimal number of Virtual Machines(VMs) required deploying the application servers that can satisfy the execution time constraint of incoming workloads is calculated. Finally, total service time of a smart home application is calculated considering into account the possible level of concurrency and dependency among tasks of an application service. In the end, some numerical and simulation examples are provided to validate our findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".