Low-cost data analytics for shared storage and network infrastructures
Bibliographic record
Abstract
Data analytics used to depend on specialized, high-end software and hardware platforms. Recent years, however, have brought forth the data-flow programming model, i.e., MapReduce, and with it a flurry of sturdy, scalable open-source software solutions for analyzing data. In essence, the commoditization of software frameworks for data analytics is well underway. Yet, up to this point, data analytics frameworks are still regarded as standalone, dedicated components; deploying these frameworks requires companies to purchase hardware to meet storage and network resource demands, and system administrators to handle management of data across multiple storage systems. This dissertation explores the low-cost integration of frameworks for data analytics within existing, shared infrastructures. The thesis centers on smart software being the key enabler for holistic commoditization of data analytics. We focus on two instances of smart software that aid in realizing the low-cost integration objective. For an efficient storage integration, we build MixApart, a scalable data analytics framework that removes the dependency on dedicated storage for analytics; with MixApart, a single, consolidated storage back-end manages data and services all types of workloads, thereby lowering hardware costs and simplifying data management. We evaluate MixApart at scale with micro-benchmarks and production workload traces, and show that MixApart provides faster or comparable performance to an analytics framework with dedicated storage. For an effective sharing of the networking infrastructure, we implement OX, a virtual machine management framework that allows latency-sensitive web applications to share the data center network with data analytics through intelligent VM placement ; OX further protects all applications from hardware failures. The two solutions allow the reuse of existing storage and networking infrastructures when deploying analytics frameworks, and substantiate our thesis that smart software upgrades can enable the end-to-end commoditization of analytics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".