Self-organizing autonomic computing systems
Bibliographic record
Abstract
Recently a great deal of research has been under-taken in the area of automating the enterprise IT Infrastructure. For enterprises with a large number of computers the IT Infrastructure operation represents a considerable amount of the enterprise budget. Autonomic Computing Systems are systems which were created for minimizing this budget component. They were meant to correct and optimize the IT infrastructure's own self-functioning by executing corrective operations without any need for human interventions. In most cases, where autonomic computing systems have been developed, this was achieved by the addition of external global controllers monitoring the sub-systems of the enterprise IT Infrastructure, determining where changes should be made and applying appropriate commands to implement these changes. Self-Organizing systems on the other hand are systems which reach a global desired state without the use of a central authority which in certain case is the human operator. This paper introduces a general architecture and appropriate algorithms for a self-organizing system which automates a cluster of servers and which maintains an equal desired response time across all the servers. The self-organizing control applies either in the case of homogeneous servers or heterogeneous ones. Furthermore, a simple controller can be built to add or remove servers from the cluster where the controller is itself a peer in the self-organizing system. Simulation data for an autonomic computing system made out of a few cluster servers which are controlled by a self-organizing controller is presented.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".