Greener Data Exchange in the Cloud: A Coding-Based Optimization for Big Data Processing
Bibliographic record
Abstract
The rise of the cloud and distributed data-intensive (big data) applications puts pressure on data center networks due to the movement of massive volumes of data. Reducing the volume of communication is pivotal for embracing greener data exchange by efficient utilization of network resources. This paper proposes the use of mixing technique, spate coding, working in tandem with software-defined network control as a means of dynamically-controlled reduction in volume of communication. We introduce motivating real-world use-cases, and present a novel spate coding algorithm for the data center networks. We also analyze the computational complexity of the general problem of minimizing the volume of communication in a distributed data center application without degrading the rate of information exchange, and provide theoretical limits of such schemes. Moreover, we proceed to bridge the gap between theory and practice by performing a proof-of-concept implementation of the proposed system in a real world data center. We use Hadoop MapReduce, the most widely used big data processing framework, as our target. The experimental results employing two of industry standard benchmarks show the advantage of our proposed system compared to a vanilla Hadoop implementation, an in-network combiner, and Combine-N-Code. The proposed coding-based scheme shows performance improvement in terms of volume of communication (up to 62%), goodput (up to 76%), disk utilization (up to 38%), and the number of bits that can be transmitted per Joule of energy (up to 200%).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.014 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".