OpenAMI: Software-Defined AMI Load Balancing
Bibliographic record
Abstract
The advanced metering infrastructure (AMI) is one of the main services of smart grid (SG), which collects data from smart meters (SMs) and sends them to utility company meter data management systems (MDMSs) via a communication network. In the next generation AMI, both the number of SMs and the meter sampling frequency will dramatically increase, thus creating a huge traffic load which should be efficiently routed and balanced across the communication network and MDMSs. This paper initially formulates the global load-balanced routing problem in the AMI communication network as an integer linear programming model, which is NP-hard. Then, to overcome this drawback, it is decomposed into two subproblems and a novel software defined network-based AMI communication network is proposed called OpenAMI. This paper also extends the OpenAMI for the cloud computing environment in which some virtual MDMSs are available. OpenAMI is implemented on a real test bed, which includes Open vSwitch, Floodlight controller, and OpenStack, and its performance is evaluated by extensive experiments and scenarios. Based on the results, OpenAMI achieves low end-to-end delay and a high delivery ratio by balancing the load on the entire AMI network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".