Time- and Cost- Efficient Task Scheduling across Geo-Distributed Data Centers
Bibliographic record
Abstract
Typically called big data processing, analyzing large volumes of data from geographically distributed regions with machine learning algorithms has emerged as an important analytical tool for governments and multinational corporations. The traditional wisdom calls for the collection of all the data across the world to a central data center location, to be processed using data-parallel applications. This is neither efficient nor practical as the volume of data grows exponentially. Rather than transferring data, we believe that computation tasks should be scheduled near the data, while data should be processed with a minimum amount of transfers across data centers. In this paper, we design and implement Flutter, a new task scheduling algorithm that reduces both the completion times and the network costs of big data processing jobs across geographically distributed data centers. To cater to the specific characteristics of data-parallel applications, in the case of optimizing the job completion times only, we first formulate our problem as a lexicographical min-max integer linear programming (ILP) problem, and then transform the ILP problem into a nonlinear program problem with a separable convex objective function and a totally unimodular constraint matrix, which can be further solved using a standard linear programming solver efficiently in an online fashion. In the case of improving both time-and costefficiency, we formulate the general problem as an ILP problem and we find out that solving an LP problem can achieve the same goal in the real practice. Our implementation of Flutter is based on Apache Spark, a modern framework popular for big data processing. Our experimental results have shown convincing evidence that Flutter can shorten both job completion times and network costs by a substantial margin.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".