Ephemera: Accelerating I/O-Intensive Serverless Workloads with a Harvested In-memory File System
Why this work is in the frame
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Bibliographic record
Abstract
Serverless computing has gained popularity for its ability to shift the burden of server management from developers to cloud providers, which allows providers to exercise greater control over resource management, optimizing configurations to enhance efficiency and performance. The diversity of serverless computing tasks, from short-lived, event-driven tasks to more complex workloads, highlights the growing importance of efficient file I/O performance for I/O-intensive workloads, yet effectively handling ephemeral storage for I/O-intensive tasks remains a challenge. Traditional file system approaches often introduce substantial latency and fail to fully leverage available memory resources within the execution environment, limiting performance and efficiency. Our work stems from the observation of the under-utilization of memory resources in serverless computing platforms and the potential efficiency improvement of I/O operations using an in-memory file system. Based on this observation, we propose Ephemera , a system designed to enhance ephemeral storage efficiency and memory utilization. Ephemera satisfies three design goals: transparent memory I/O integration , heterogeneous tasks resource synergy , and harmonized cluster workload orchestration . Ephemera integrates three components: the Runtime Daemon, responsible for managing a container’s in-memory file system; the Tenant Manager, facilitating memory configuration sharing across containers; and the Cluster Controller, optimizing workload balancing. Our experiments demonstrate that Ephemera significantly improves performance for I/O-intensive tasks compared to traditional file systems. Specifically, Ephemera decreases I/O processing time by 50% on average and reduces latency by up to 95.73% in certain scenarios with negligible overhead.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it