Hierarchical Data Storage And Processing on the Edge of the Network
Bibliographic record
Abstract
Current wireless mobile networks are not able to support next-generation applications that require low latency or produce large volumes of data that can overwhelm the network. Examples include video analysis applications, wearable devices, safety-critical applications, and intelligent smart city systems. The use of servers on the wide-area cloud, however, is also not an option as these applications require low response times, or involve the processing of large volumes of data from many devices. To address these challenges, edge computing proposes the addition of computation and storage capabilities to the edge of the network. This thesis generalizes edge computing into a hierarchical cloud architecture deployed over the geographic span of a network. The vision supports scalable processing by providing storage and computation along a succession of datacenters positioned between the end device and the traditional wide area cloud datacenter. I develop a new deployment and execution platform called CloudPath based on the Function as a Service (FaaS) model that supports code and data mobility and distribution by enforcing a clear separation between computation and state. In CloudPath applications will be composed of a collection of light-weight stateless event handlers that can be implemented using high-level languages, such as Java. In this thesis, I also develop a shared database abstraction called PathStore that enables transparent data access to the hierarchy of cloud and edge data centers. PathStore supports concurrent object reads and writes on all nodes of the database hierarchy and its extension called SessionStore adds session consistency (read your own writes, monotonic reads/writes) for mobile applications. Finally, I implement a geo-distributed query engine that exploits the hierarchical structure of our eventually-consistent geo-distributed database to trade temporal accuracy (freshness) for improved latency and reduced bandwidth.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".