Energy-aware Embedding of Logical Functionality Chains over Heterogeneous Platforms
Bibliographic record
Abstract
In the 5G radio access network, baseband functions are divided into the radio unit (RU) and two logical entities, namely the distributed unit (DU) and the centralized unit (CU). In the core network, the User Plane Function (UPF), another logical entity, serves as a singular data plane element. A key factor influencing performance and overall network efficiency is the placement of 5G logical functionalities, including DUs, CUs, and UPFs. While most existing research on provisioning focuses on homogeneous platforms such as virtual machines (VMs), practical implementations often use a variety of platforms, including bare-metal servers and containers alongside VMs. This paper proposes an energy-efficient placement and chaining strategy for 5G logical functionalities across heterogeneous platforms, addressing traffic demands with varying quality-of-service requirements. After presenting a novel formulation of the problem as an integer linear programming (ILP), we introduce a heuristic ranking-based algorithm (HRA) to solve it efficiently. Extensive simulation results demonstrate that our algorithm can reduce power consumption by up to 30% and improve acceptance rate by up to 40% while satisfying both latency and bandwidth constraints. Moreover, our comparative analysis of homogeneous and heterogeneous networks underscores the importance of choosing a virtualization strategy that aligns with the specific requirements of network functionalities to optimize power consumption.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".