A Cloud-Based Architecture for Multimedia Conferencing Service Provisioning
Bibliographic record
Abstract
Multimedia conferencing is the real-time exchange of multimedia content between multiple parties. It is the basis of several interactive multiuser applications, such as distance learning and multimedia multiplayer online games. The cloud-based provisioning of the conferencing services on which these applications rely on can have several benefits, including the easy provisioning of new applications, efficient use of resources, and elastic scalability. This paper proposes a holistic cloud-based architecture for conferencing service provisioning, which covers both the infrastructure and platform layers of the cloud. The proposed infrastructure layer offers conferencing substrates-as-a-service (e.g., dial-in signaling, video mixing, and audio mixing), instead of virtual machines or containers. The platform layer abstracts the details of the conferencing concepts and offers a high-level interface to simplify conference service provisioning for a wide range of service and application providers (experts versus non-experts). It also enables the on-the-fly scaling of the running conferences while guaranteeing the required quality of service, enables substrates composition to create new conferencing services, and eases the reuse of conferencing services in building new applications. The presented architecture is supported by a proof-of-concept prototype and performance measurements. The latter provides the analysis of resource allocation efficiency and response time, as well as the scalability of the system under suboptimal and over-provisioned conditions. It also provides recommendations for service providers regarding the best alternatives for provisioning their service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".