Green Cloud Multimedia Networking: NFV/SDN based Energy-efficient Resource Allocation
Bibliographic record
Abstract
The rapid growth of communications and multimedia network services such as Voice over Internet Protocol (VoIP) have caused these networks to face a crisis in resources from two perspectives: 1. Lack of resources and, as a result, overload; 2. Redundancy of resources and, as a result, energy loss. Cloud computing allows the scale of resources to be reduced or increased on demand. Many of the gains obtained from the cloud computing come from resource sharing and virtualization technology. On the other hand, the emerging concept of Software-Defined Networking (SDN) can provide a global view of the entire network for integrated resource management. Network Function Virtualization (NFV) can also be used to virtually implement a variety of network devices and functions. In this paper, we present an energy-efficient framework called GreenVoIP to manage the resources of virtualized cloud VoIP centers. By managing the number of VoIP servers and network equipment, such as switches, this framework not only prevents overload but also supports green computing by saving energy. Finally, GreenVoIP is implemented and evaluated on real platforms, including Floodlight, Open vSwitch, and Kamailio. The results suggest that the proposed framework can minimize the number of active devices, prevent overloading, and provide service quality requirements.
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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 | 0.000 |
| 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 | 0.001 | 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".