Task Sharing and Scheduling for Edge Computing Servers Using Hyperledger Fabric Blockchain
Bibliographic record
Abstract
Efficient administration of computing resources in Multi-access Edge Computing (MEC) networks is a very active research topic. Task sharing in particular, is one of the capital problems with respect to MEC architectures although is mostly addressed from the end-user standpoint. Moreover, standard MEC frameworks do not consider task sharing schemes for computing servers at the edge level even though mechanisms of this kind could increase the utilization of resources in MEC setups. The integration of blockchain technologies into multi server cloud and edge computing solutions has started to gain steam in recent times, largely in part for its potential to enhance the functionality, security, and privacy of cloud-based architectures. In this context, this paper presents a task sharing mechanism for MEC servers with precedent-dependent tasks based on the Hyperledger Fabric framework. Hyperledger Fabric is a blockchain platform that provides a light-weight and distributed interaction playing field for the servers that mitigates the security and privacy concerns related to the collaboration scheme. For sharing tasks among the servers, the call dependencies of the tasks for a user application are captured by a control-flow graph and an optimization problem is formulated to obtain the allocation of tasks. Numerical results on the performance of the proposed task sharing mechanism are presented.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".