Cache (computing)

Summary

In computing, a cache (kæʃ ) is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation or a copy of data stored elsewhere. A cache hit occurs when the requested data can be found in a cache, while a cache miss occurs when it cannot. Cache hits are served by reading data from the cache, which is faster than recomputing a result or reading from a slower data store; thus, the more requests that can be served from the cache, the faster the system performs. To be cost-effective and to enable efficient use of data, caches must be relatively small. Nevertheless, caches have proven themselves in many areas of computing, because typical computer applications access data with a high degree of locality of reference. Such access patterns exhibit temporal locality, where data is requested that has been recently requested already, and spatial locality, where data is re

Official source

https://en.wikipedia.org/wiki/Cache_(computing)

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Adaptive Cache Mode Selection for Queries over Raw Data

Anastasia Ailamaki, Tahir Azim, Azqa Nadeem

Caching the results of intermediate query results for future re-use is a common technique for improving the performance of analytics over raw data sources. An important design choice in this regard is whether to lazily cache only the offsets of satisfying tuples, or to eagerly cache the entire tuples. Lazily cached offsets have the benefit of smaller memory requirement and lower initial caching overhead, but they are much more expensive to reuse. In this paper, we explore this tradeoff and show that neither lazy nor the eager caching mode is optimal for all situations. Instead, the ideal caching mode depends on the workload, the dataset and the cache size. We further show that choosing the sub-optimal caching mode can result in a performance penalty of over 200%. We solve this problem using an adaptive online approach that uses information about query history, cache behavior and cache size to choose the optimal caching mode automatically. Experiments on TPC-H based workloads show that our approach enables execution time to differ by, at most, 16% from the optimal caching mode, and by just 4% on the average.

2018

Successive Refinement to Caching for Dynamic Requests

Michael Christoph Gastpar, Pinar Sen

In the celebrated coded caching problem studied by Maddah-Ali and Niesen, the peak-traffic network load is to be reduced by first caching some information about contents into individual memories of end users during the off-peak hours and then upon user requests broadcasting some other information about the contents, which, combined with cached information, can let each user recover their requested content. Thus, information-theoretic studies of coded caching involve the optimal tradeoff among communication rates for the two phases of cache placement and content delivery, and the optimal construction of codes for cache placement and content delivery. In order to allow better utilization of network resources, this paper introduces a new caching model in which user requests can arise at any point of time during the cache placement phase, and proposes a successive refinement approach as an answer to this dynamic caching problem. For uniformly random file requests, the optimal tradeoff among average-case delivery rates are characterized when the cache rate is above a well-defined threshold. For arbitrary file requests, a successive caching algorithm is developed to simultaneously reduce worst-case delivery rates at every request time, which are uniformly within a constant multiplicative factor of their respective optima.

IEEE2020

Successive Refinement to Caching for Dynamic Content

Michael Christoph Gastpar, Pinar Sen

To reduce the network load during peak hours, servers deliver partial data to users during the off-peak time of the network before the actual requests are known, which is known as caching. This paper studies a single user caching problem in which the file contents are subject to dynamic modifications with respect to a certain probability distribution. To cope with the dynamical nature of the file contents, a successive refinement approach to caching is presented: partial information of the original data is cached first and then if there is a modification, a refinement to the previously cached data is delivered to the user. Given a fixed cache memory, there is a tension between the rates of two cache descriptions. The problem of optimal caching strategies is formulated through a successive Gray-Wyner network, the optimal rate region of which is characterized. Some lower and upper bounds on the performance of optimal caching strategies are developed and shown to actually yield closed form solutions for certain classes of file contents.

IEEE2019