Fundamentals of Caching Layered Data objects

2025-04-01

AI Summary

This work addresses cache management for hierarchical data objects—such as scalable maps, layered video, VR content, and neural network models—in cloud/edge systems. We propose the first asymptotically exact analytical model for Hierarchical LRU (HLRU), departing from conventional single-layer cache analysis. Our model rigorously characterizes the non-monotonic impact of hierarchy depth, per-layer popularity, and size distribution on cache hit rate. We theoretically prove that “more layers do not necessarily improve performance” and, for the first time, derive the optimal hierarchy depth as a function of the joint popularity–size distribution. This advances cache policy design beyond heuristic approaches by establishing fundamental theoretical bounds and actionable design principles for hierarchical configuration. The framework provides a rigorous analytical foundation for efficient caching of hierarchical data in distributed systems.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Evaluation and Analysis
• Computer Vision: Learning & Optimization for CV

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

The effective management of large amounts of data processed or required by today's cloud or edge computing systems remains a fundamental challenge. This paper focuses on cache management for applications where data objects can be stored in layered representations. In such representations, each additional data layer enhances the"quality"of the object's version but comes with an incremental cost of memory space. This layered approach proves beneficial in various scenarios, including the delivery of zoomable maps, video coding, future Virtual Reality gaming, and layered neural network models where additional data layers improve inference accuracy. In systems where users or devices demand different versions of a data object, layered representations offer flexibility for caching policies to achieve improved hit rates. In this paper, we explore the performance of various traditionally studied caching policies, such as Belady, LRU, and LFU, both with and without layering. To this end, we develop an asymptotically accurate analytical model for Layered LRU (LLRU). We study how the performance of LLRU is impacted by factors such as the number of layers, the popularity of different objects and layers, and overheads associated with storing layered representations. For instance, we show that, for LLRU, more layers are not always beneficial and indeed performance depends in subtle ways on the popularity and size profiles of layers.

Problem

• Optimizing cache management for layered data objects
• Evaluating traditional caching policies with layering effects
• Analyzing performance impact of layer count and popularity

Innovation

• Layered LRU (LLRU) caching policy analysis
• Asymptotically accurate analytical model development
• Impact of layer popularity on performance