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MagicScaler: Uncertainty-aware, Predictive Autoscaling
Summary: MagicScaler fuses multi-scale attention with Gaussian process regression to produce demand forecasts with quantified uncertainty, capturing scale-sensitive temporal patterns. An uncertainty-aware scaler uses a stochastic-constraint loss to trade off running cost vs QoS risk, validated on Alibaba clusters with superior results.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13210
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 6.6738199e-05
- Overall Rank
- 3,871 | 73.10%
- DOI
-
10.14778/3611540.3611566
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 2,305 |
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods |
2024 |
VLDB |
9.0655355e-05 |
| 7,993 |
RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems |
2025 |
VLDB |
4.6080455e-05 |
| 8,735 |
Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the Cloud |
2024 |
SIGMOD |
4.4520434e-05 |
| 10,271 |
OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning |
2026 |
VLDB |
4.1905499e-05 |
| 10,297 |
Resilience-Aware Elastic Scaling for Cloud-Native Online DL Training on Multi-Tenant GPU Clusters |
2026 |
VLDB |
4.1905499e-05 |
| 10,411 |
ABase: the Multi-Tenant NoSQL Serverless Database for Diverse and Dynamic Workloads in Large-scale Cloud Environments |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,421 |
Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,569 |
A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty |
2025 |
VLDB |
4.1905499e-05 |
| 11,044 |
QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models |
2024 |
VLDB |
4.1905499e-05 |
| 11,088 |
OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud |
2024 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 2,376 |
Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless |
2022 |
VLDB |
8.9367999e-05 |
| 3,453 |
Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter |
2020 |
SIGMOD |
7.0785866e-05 |
| 3,645 |
Autoscaling Tiered Cloud Storage in Anna |
2019 |
VLDB |
6.882432e-05 |
| 3,692 |
iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases |
2019 |
VLDB |
6.8328808e-05 |
| 4,110 |
RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection |
2021 |
SIGMOD |
6.4358092e-05 |
| 4,476 |
Classical and Contemporary Approaches to Big Time Series Forecasting |
2019 |
SIGMOD |
6.1458665e-05 |
| 5,023 |
AutoCTS: Automated Correlated Time Series Forecasting |
2022 |
VLDB |
5.7473072e-05 |
| 5,479 |
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles |
2022 |
VLDB |
5.4849266e-05 |
| 6,589 |
AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting |
2023 |
SIGMOD |
4.9964862e-05 |
| 11,202 |
LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation |
2023 |
SIGMOD |
4.1905499e-05 |
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