Camp

Abstract

Cost Adaptive Multi-queue eviction Policy (CAMP) is an algorithm for a general purpose key-value store (KVS) that manages key-value pairs computed by applications with different access patterns, key-value sizes, and varying costs for each key-value pair. CAMP is an approximation of the Greedy Dual Size (GDS) algorithm in that its eviction policy is as effective as GDS. At the same time, its implementation is as efficient at LRU. Similar to an implementation of LRU using queues, it adapts to changing workload patterns based on the history of requests for different key-value pairs. It is superior to LRU because it considers both the size and cost of key-value pairs to maximize the utility of the available memory across competing applications. We compare CAMP with both LRU and an alternative that requires human intervention to partition memory into pools and assign grouping of key-value pairs to different pools. The results demonstrate CAMP is as fast as LRU while outperforming both LRU and the pooled alternative. We also present results from an implementation of CAMP using Twitter's version of memcached.

The Subset Assignment Problem for Data Placement in Caches

“…If query and update (read and write) statistics are known in advance, the optimal policy is a static placement of data items in the memory banks that minimizes the expected time to service each request. A static placement can have much better performance over adaptive online algorithms if the request frequencies are stable [9,8]. Since the popularity of queries do vary over time, a static placement would need to be recomputed periodically based on recent statistics followed with a reorganization of key-value pairs across memory banks.…”

A Qualitative Study of Application-Level Caching

“…Despite many methods related to intelligent cache approaches have been published [15], adapting cache properties and decisions at application-level has only been tackled by few examples. They provide adaptive solutions for cache update algorithms [18], [39], [40], [41], or mapping of requests before repartitioning the cache [42]. Some approaches [31], [43], [44] focus on the infrastructure aspect of application-level caching by exploring the size and faulttolerance adaptations in cache servers.…”