Using cooperation to improve the resource utilization in service ecologies
Bibliographic record
Abstract
The digital ecosystem (DE) paradigm is a holistic management/design/integration paradigm that is based on the notion of self-interested, self-managing, proactive and autonomous digital entities that evolve and self-organize. While a DE can be realized using different technologies Web services (WS) are particularly promising due to their widespread acceptance within industry and academia, established and open standards, a wealth of development tools and infrastructures, and maybe most importantly the number of successful deployments. A key issue in the development/deployment of any system is the behaviour exhibited by components (e.g. providers & consumers). Service-oriented systems tend to use a contract approach in which implicit, predefined or negotiated service-level agreements (SLAs) are established between providers and consumers. A SLA defines from a provider's point of view the guaranties, responsibilities and warranties (e.g. availability, reliability, performance, throughput, compensations, etc) towards a consumer. While being conceptually fairly straightforward, SLAs have the unfortunate side effect of forcing providers to use reservation/locking of resources as a means of ensuring their availability should the consumer request them. This in turn leads to the problem of resource underutilization that threatens the efficiency of the service-oriented system. This paper focuses on the use of cooperation between providers and consumers in service ecologies as an alternative to the constraining SLAs. By enabling the consumers and providers to exchange information regarding their current needs and capabilities it becomes possible to better utilize the resources and therefore improve overall system performance.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".