Analysis and Optimization of Big-Data Stream Processing
Bibliographic record
Abstract
Big data processing is rapidly growing in recent years due to the immediate demanding of many applications. This growth compels industries to leverage scheduling in order to optimally allocate the resources to the big data streams which requires data-driven big data analysis. Moreover, optimal scheduling of big data stream process should guarantee the QoS requirements of computing tasks. Execution deadlines of tasks within the streams is specified as one of the most significant QoS factors. In this paper, we study the scheduling and execution of big data stream processes. First, a queueing theory approach to the modeling of the streams as a collection of sequential and parallel tasks is proposed. It is assumed that heterogeneous threads are required to handle various big data tasks such as processing, storing and searching which may have quite general service time distributions. Then, with the proposed model, an optimization problem is defined to minimize the total number of resources required to serve the big data streams while guaranteeing the QoS requirements of their tasks. An algorithm is also proposed to mitigate the complexity order of the optimization problem. The objective of this research is to minimize the stream processing resources in terms of threads with constraints over the task waiting time of the application tasks. We apply the proposed scheduling algorithm to Apache Storm, a distributed real-time computation platform, to optimize the cloud resource requirements. The experiment results validate our analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".