Memory Hierarchy Design for Caching Middleware in the Age of NVM

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Ghandeharizadeh, Shahram, Irani, Sandy, Lam, Jenny
Formato: Preprint
Publicado: 2025
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866913878182461440
author Ghandeharizadeh, Shahram
Irani, Sandy
Lam, Jenny
author_facet Ghandeharizadeh, Shahram
Irani, Sandy
Lam, Jenny
contents Advances in storage technology have introduced Non-Volatile Memory, NVM, as a new storage medium. NVM, along with Dynamic Random Access Memory (DRAM), Solid State Disk (SSD), and Disk present a system designer with a wide array of options in designing caching middleware. Moreover, design decisions to replicate a data item in more than one level of a caching memory hierarchy may enhance the overall system performance with a faster recovery time in the event of a memory failure. Given a fixed budget, the key configuration questions are: Which storage media should constitute the memory hierarchy? What is the storage capacity of each hierarchy? Should data be replicated or partitioned across the different levels of the hierarchy? We model these cache configuration questions as an instance of the Multiple Choice Knapsack Problem (MCKP). This model is guided by the specification of each type of memory along with an application's database characteristics and its workload. Although MCKP is NP-complete, its linear programming relaxation is efficiently solvable and can be used to closely approximate the optimal solution. We use the resulting simple algorithm to evaluate design tradeoffs in the context of a memory hierarchy for a Key-Value Store (e.g., memcached) as well as a host-side cache (e.g., Flashcache). The results show selective replication is appropriate with certain failure rates and workload characteristics. With a slim failure rate and frequent data updates, tiering of data across the different storage media that constitute the cache is superior to replication.
format Preprint
id arxiv_https___arxiv_org_abs_2506_05071
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Memory Hierarchy Design for Caching Middleware in the Age of NVM
Ghandeharizadeh, Shahram
Irani, Sandy
Lam, Jenny
Databases
Hardware Architecture
Data Structures and Algorithms
Advances in storage technology have introduced Non-Volatile Memory, NVM, as a new storage medium. NVM, along with Dynamic Random Access Memory (DRAM), Solid State Disk (SSD), and Disk present a system designer with a wide array of options in designing caching middleware. Moreover, design decisions to replicate a data item in more than one level of a caching memory hierarchy may enhance the overall system performance with a faster recovery time in the event of a memory failure. Given a fixed budget, the key configuration questions are: Which storage media should constitute the memory hierarchy? What is the storage capacity of each hierarchy? Should data be replicated or partitioned across the different levels of the hierarchy? We model these cache configuration questions as an instance of the Multiple Choice Knapsack Problem (MCKP). This model is guided by the specification of each type of memory along with an application's database characteristics and its workload. Although MCKP is NP-complete, its linear programming relaxation is efficiently solvable and can be used to closely approximate the optimal solution. We use the resulting simple algorithm to evaluate design tradeoffs in the context of a memory hierarchy for a Key-Value Store (e.g., memcached) as well as a host-side cache (e.g., Flashcache). The results show selective replication is appropriate with certain failure rates and workload characteristics. With a slim failure rate and frequent data updates, tiering of data across the different storage media that constitute the cache is superior to replication.
title Memory Hierarchy Design for Caching Middleware in the Age of NVM
topic Databases
Hardware Architecture
Data Structures and Algorithms
url https://arxiv.org/abs/2506.05071