Innovative Opportunistic Scheduling Algorithms for Networks with Packet-Level Dynamics
Bibliographic record
Abstract
Scheduling in wireless networks plays an important role. The undeterministic nature of the wireless channel is usually considered \nas an undesirable property. Recently, the idea of opportunistic scheduling is introduced and it takes advantage of the time-varying channel for performance improvement such as throughput and delay. \n \nSince the introduction of opportunistic scheduling, there are two main bodies of works. The first body of works assume that each user is greedy and has infinite backlog for transfer. With this assumption, fairness objective becomes an important factor in \ndesigning a scheduling algorithm to avoid severe starvation of certain users. Typical fairness involve processor sharing time \nfairness, proportional fairness, and minimum performance guarantee. On the other hand, delay performance is not a appropriate factor to evaluate the effectiveness of a scheduling algorithm because of the \ninfinite backlog assumption. In reality, this assumption is not true as data arrives and leaves the network randomly in practice. \n \nThe second body of works deal with the relaxation of the infinite backlog assumption. Thus, the notion of stability region arises. The definition of stability is that the queue at each source node remains finite. Stability region can be defined as the set of traffic intensities which can all be stabilized by the network. The well known throughput optimal algorithm is proven capable of achieving the largest stability region. \n \nIn this thesis, two innovative opportunistic scheduling algorithms which aim to minimize the amount of resources used to stabilize the \ncurrent traffics are proposed. The key feature of our algorithm is that the incoming traffic rates are available to the scheduler, whereas the throughput optimal algorithm has no such prior traffic knowledge. Performance comparisons are made by means of simulation to demonstrate that the proposed algorithms can achieve the same \nstability region as the throughput optimal algorithm. Moreover, the delay performance is better than that of the throughput optimal algorithm, especially under heavy traffic conditions.
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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 | 0.001 |
| 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".