A LAYERED FRAMEWORK FOR CONNECTING CLIENT OBJECTIVES AND RESOURCE CAPABILITIES
Bibliographic record
Abstract
In large-scale, distributed systems such as Grids, an agreement between a client and a service provider specifies service level objectives both as expressions of client requirements and as provider assurances. From an application perspective, these objectives should be expressed in a high-level, service or application-specific manner rather than requiring clients to detail the necessary resources. Resource providers on the other hand, expect low-level, resource-specific performance criteria that are uniform across applications and can be easily interpreted and provisioned. This paper presents a framework for service management that addresses this gap between high-level specification of client performance objectives and existing resource management infrastructures. The paper identifies three levels of abstraction for resource requirements a service provider needs to manage, namely: detailed specification of raw resources, virtualization of heterogeneous resources as abstract resources, and performance objectives at an application level. The paper also identifies three key functions for managing service-level agreements, namely: translation of resource requirements across abstraction layers, arbitration in allocating resources to client requests, and aggregation and allocation of resources from multiple lower-level resource managers. One or more of these key functions may be present at each abstraction layer of a service-level manager. Thus, layering and the composition of these functions across abstraction layers enables modeling of a wide array of management scenarios. The framework we present uses service metadata and/or service performance models to map client requirements to resource capabilities, uses business value associated with objectives to arbitrate between competing requests, and allocates resources based on previously negotiated agreements. We instantiate this framework for three different scenarios and explain how the architectural principles we introduce are used in the real-word.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".