Meta-Initialized Warm Pool Manager: Predictive Cold Start Mitigation for Serverless Functions via Adaptive Meta-Learning
Abstract
Serverless computing platforms commonly suffer
from cold-start latency because of Cold-start problems dynamic
invocation patterns, but with current warm pool management
strategies face difficulty in effectively adjusting to changes in
workload. We propose the Meta-Initialized Warm Pool Manager
(MI-WPM), a new component which combines meta-learning and
adaptive control to address the problem of cold start efficiently.
The innovation is based on a meta predictor trained on diverse
invocation scenarios, which offers a generalized Initialization for
rapid fine-tuning based on detection of workload drift. Contrary
to traditional schemes based upon static heuristics or expensive
“retraining, MI-WPM adapts its warm pool sizes by combining
“ meta-initialized predictions with lightweight statistical drift
detection. The architecture uses a temporal fusion transformer
for modeling invocation patterns, Kolmogorov-Smirnov test for
monitoring drift, and feedback-controlled controller for opti-
mizing resource distribution. The experimental results show the
reduction in the Cold Start problem using MI-WPM. occurring
by 47% compared to state-of-the-art approaches while keeping
provisioning costs below 5%. Moreover, the Meta learning frame-
work supports sub-minute adaptation on unknown invocation
patterns, filling a gap in serverless computing. The proposed
approach is developed as a modular extension of the existing
serverless architectures without any infrastructure modifications.
This work fills the gap between offline-trained models and
reactive scaling, providing a principled approach to cold start
mitigation in production environments.
from cold-start latency because of Cold-start problems dynamic
invocation patterns, but with current warm pool management
strategies face difficulty in effectively adjusting to changes in
workload. We propose the Meta-Initialized Warm Pool Manager
(MI-WPM), a new component which combines meta-learning and
adaptive control to address the problem of cold start efficiently.
The innovation is based on a meta predictor trained on diverse
invocation scenarios, which offers a generalized Initialization for
rapid fine-tuning based on detection of workload drift. Contrary
to traditional schemes based upon static heuristics or expensive
“retraining, MI-WPM adapts its warm pool sizes by combining
“ meta-initialized predictions with lightweight statistical drift
detection. The architecture uses a temporal fusion transformer
for modeling invocation patterns, Kolmogorov-Smirnov test for
monitoring drift, and feedback-controlled controller for opti-
mizing resource distribution. The experimental results show the
reduction in the Cold Start problem using MI-WPM. occurring
by 47% compared to state-of-the-art approaches while keeping
provisioning costs below 5%. Moreover, the Meta learning frame-
work supports sub-minute adaptation on unknown invocation
patterns, filling a gap in serverless computing. The proposed
approach is developed as a modular extension of the existing
serverless architectures without any infrastructure modifications.
This work fills the gap between offline-trained models and
reactive scaling, providing a principled approach to cold start
mitigation in production environments.
Research Areas
