Towards a biologically-inspired framework for multimedia service management
Bibliographic record
Abstract
The advent of service-oriented architecture (SOA), internet and ubiquitous delivery tech-nology has resulted in multimedia services (e.g. repurposing, streaming and conferencing services) being accessible at any time, from any device, through any network. However, there are still some problems related to heterogeneity, scalability and QoS demand of the management of such multimedia services. Some of the existing solutions are centralized, which evolve scalability problems in terms of the number of concurrent requests for the target service composition. Other solutions are distributed, which depend on the use of traditional algorithms (e.g. Dijkstra, Bellman Ford). Such distributed solutions also use replicated services, which can also result in scalability problems for large networks. In order to mitigate the above problems, this dissertation proposes a framework for multimedia service management that is based on a biologically-inspired approach. It utilizes an ant-colony-based selection algorithm for collecting the QoS requirements from the individual repurposing service in order to select the most suitable one for the desired composition process, which ensures higher scalability and efficient load balancing. It also develops a QoS-aware service selection algorithm for a multimedia repurposing service. The proposed framework's performance is validated through both simulation and proto-type implementation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".