HALP: Improving Performance and Efficiency of Cache Eviction Policies for YouTube CDN

Cache Eviction Policies: HALP Framework for Improved Performance and Efficiency

Introduction to Cache Eviction
Cache eviction is a widely used concept in computer science that enhances the performance of storage and retrieval systems. It involves storing popular items closer to the client based on request patterns. The decision policy for cache management plays a crucial role in updating the stored items, and several efficient heuristics have been developed to optimize this process.

The Challenge of Applying AI to Cache Policies
Although machine learning has shown promising results in optimizing cache policies, surpassing robust heuristics in real-world scenarios while maintaining computational and memory efficiency remains a challenge. This article presents a solution to this problem by introducing the Heuristic Aided Learned Preference (HALP) framework.

HALP Framework Overview
HALP is a state-of-the-art cache eviction framework that utilizes learned rewards and preference learning with automated feedback. It combines a lightweight heuristic baseline eviction rule with a neural reward model. The reward model is continuously trained using automated feedback, simulating an offline oracle.

How HALP Enhances Efficiency
HALP improves infrastructure efficiency and reduces user video playback latency in YouTube’s content delivery network. It computes cache eviction decisions by training a small neural network to predict rewards for each item based on preference learning. It uses a filtering mechanism with a heuristic and a neural network scoring function to optimize the final decision.

The Neural Reward Model
HALP utilizes a lightweight multilayer perceptron (MLP) as its reward model. The model selectively scores individual items in the cache based on externally tagged features and internally constructed dynamic features. HALP learns the reward model in real-time, allowing for specialization based on different environments. Online training captures local network conditions and locally popular content.

Scoring with a Randomized Priority Queue
To optimize eviction decisions efficiently, HALP employs a random sampling-based heuristic scoring rule. This approach approximates exact priority queues and ensures compute efficiency. The sampled candidates provide diversity for both training and inference, reducing skew between samples.

Online Preference Learning with Automated Feedback
HALP learns the reward model using online feedback and preference labels. It constructs pairwise preference queries that are relevant for eviction decisions and appends them to a set of pending comparisons. The labels for these comparisons are resolved at a random future time. HALP performs additional bookkeeping to process pending comparisons and manage memory overhead.

Results on YouTube CDN
Empirical analysis demonstrates that HALP outperforms state-of-the-art cache policies in terms of cache miss rates on public benchmark traces. However, the framework aims to go beyond benchmarks and generalize reliably in production settings.

HALP provides a scalable cache eviction framework that combines heuristic baseline eviction rules with learned rewards. It improves efficiency and reduces latency in YouTube’s content delivery network. By continuously training a neural reward model and utilizing preference learning, HALP offers a reliable solution for cache management in real-world scenarios.

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