Enjima: A Resource-Adaptive Stream Processing System
Bibliographic record
Abstract
Effective system resource management is key to delivering high performance stream processing. Stream processing engines (SPEs) rely on their host operating system (OS) for managing compute and memory resources, but this is inefficient as the OS is not stream-aware, i.e., the OS does not understand the streaming dataflow or pipeline state in how they relate to the resource requirements of stream processing. Additionally, the lack of stream-awareness inhibits adaptive resource allocation in response to dynamic workload changes. We present Enjima, a modern SPE designed for scale-up on a single machine through adaptive stream-aware management of memory and compute resources. Enjima's eager, cache-aligned, block-based memory management avoids memory allocation on the critical path of system execution while providing efficient data transfer of events between streaming operators. Its variable batching forms event batches based on pending inputs and available output memory, reducing batching delays and memory accesses to enhance system performance. Enjima integrates a stream-aware, state-based operator scheduler that leverages fine-grained operator and pipeline metrics such as operator cost, selectivity, and latency gradient to optimize for both latency and throughput, enabling significant performance gains and rapid adaptation to dynamic workloads. Evaluation against state-of-the-art systems shows that Enjima achieves up to 6.3× higher throughput and up to three orders of magnitude lower latency through integrated stream-aware memory and CPU resource management.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".