Smart applications on virtual infrastructure
Bibliographic record
Abstract
Smart Applications on Virtual Infrastructure (SAVI) research is focused on the design of future application platforms built on flexible, versatile and evolvable infrastructure that can readily deploy, maintain, and retire the large-scale, possibly short-lived, distributed applications that are the future of software systems. Future application platforms will support an open applications and content marketplace where a vast number of vendors offer applications, content, and services to other vendors as well as to consumers. This marketplace will be characterized by extremely large scale and very high churn, with new applications being introduced and others retired at very fast rates. Content will also be produced at very high rates and large volumes, and demand for content will change quickly over time. These attributes of the marketplace will place extreme demands on the supporting infrastructure for agility in resource allocation, as well as scalability, reliability, accountability and security. Cost-effectiveness will require that infrastructure be flexible so that it can be readily re-purposed, essentially reprogrammed, to provide new capabilities. The management and control systems must be designed to provide efficient resource usage and high availability at low operations expense. Multiple owners will provide infrastructure and so the architecture for the infrastructure must be open and allow for interconnection and federation. Crucially, the architecture of the infrastructure should support the rapid introduction of applications, the delivery of applications with targeted levels of Quality of Experience, and the rapid retirement of applications and redeployment of their supporting resources. SAVI takes a view of infrastructure in that all resources whether computing, processing, or networking are viewed as being part of shareable resource pools that can be controlled and managed using the same systems. Future users will typically access the application platform through a mobile device that connects to a ubiquitous very-high-bandwidth, integrated wireless/optical access network. The application platform provides connectivity to services that support the application of interest. Many services will be supported by massive-scale distant datacenters located at sites of renewable energy. Other services will require low latency (alarms in grids, safety applications in transportation, monitoring in remote health) or processing of large volumes of local data (e.g., video capture in lecture rooms) provided by converged network and computing resources at the smart edge of the network, such as the premises of service providers. The role of the resource control and management systems is to ensure that applications can be supported by all the elements of the infrastructure in the anticipated future marketplace.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".