Instance-Optimized Mapping with Portfolio Methods
Year: 2024
Type: conference-paper
Abstract: Mappings are ubiquitous in computer systems, such as translating virtual memory to physical memory, file paths to inode numbers, database keys to data locations. Traditional system mappings are often hand-crafted and data-agnostic. In this paper, we explore the use of neural networks as learned mappings that are automatically generated and data-dependent, optimizing performance for specific workloads and scenarios. Unlike prior learned structures, we employ a portfolio method consisting of a set of independent neural networks, each responsible for making sole decisions. Our preliminary results indicate that these portfolio mappings can generalize across multiple applications.
Authors: Yibo Zhao, Panagiotis Manolios, Cheng Tan
Institution: Northeastern University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Universitas Brawijaya
Award: G294480798
OpenAlex ID: W4405845688