Energy-Aware Placement and Scheduling of Network Traffic Flows with Deadlines on Virtual Network Functions
Bibliographic record
Abstract
Hardware MiddleBoxes represent a vital part in today's networks. Despite their important roles, they are accompanied by several problems, namely, their lack of flexibility, high capital and operation expenditures, and power consumption. Network Function Virtualization is one promising solution to address these problems. This trend replaces the MiddleBoxes by software-based entities. Indeed, these Virtual Network Functions promise to alleviate the numerous disadvantages brought by their hardware counterparts. One of these most serious issuesis the steadily increasing power consumption. Studies suggest that the Virtual Network Functions will reduce the electricity costs needed to turn on and operate the hardware functions. In order to further optimize the power consumption of the network, an efficient framework, capable of placing and scheduling traffic on these VNFs, is needed. Such a framework allows to optimally place and schedule the flows to be serviced, and placing theunused servers in energy saving modes. In this article, we assume VNFs are already placed on physical machines, each hosting a subset of the functions. We consider traffic flows with deadlines. We aim at assigning and scheduling flows to VNFs in the most energy efficient manner. We formulate this problem mathematically and, owing to its complexity, present an efficient algorithmic method for solving the problem. We compareour heuristic with two other approaches, one of which aims to minimize the makespan, and the other to minimize number of servers used. We show that our heuristic combines the advantages of both approaches and generates better results by consuming up to 31.3% and 46.1% energy less than other two approaches respectively.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".