Cache Replacement Algorithms for YouTube

2014 ◽  
Author(s):  
Ming-Chang Lee ◽  
Fang-Yie Leu ◽  
Ying-Ping Chen
Keyword(s):  
Cache Replacement ◽  
Replacement Algorithms

Penalty- and Locality-aware Memory Allocation in Redis Using Enhanced AET

2021 ◽  
Vol 17 (2) ◽  
pp. 1-45
Author(s):  
Cheng Pan ◽  
Xiaolin Wang ◽  
Yingwei Luo ◽  
Zhenlin Wang
Keyword(s):  
Large Data ◽  
Data Locality ◽  
Memory Allocation ◽  
Cache Management ◽  
Cache Replacement ◽  
Time Model ◽  
Memory Cache ◽  
Management Scheme ◽  
Data Volume ◽  
Replacement Algorithms

Due to large data volume and low latency requirements of modern web services, the use of an in-memory key-value (KV) cache often becomes an inevitable choice (e.g., Redis and Memcached). The in-memory cache holds hot data, reduces request latency, and alleviates the load on background databases. Inheriting from the traditional hardware cache design, many existing KV cache systems still use recency-based cache replacement algorithms, e.g., least recently used or its approximations. However, the diversity of miss penalty distinguishes a KV cache from a hardware cache. Inadequate consideration of penalty can substantially compromise space utilization and request service time. KV accesses also demonstrate locality, which needs to be coordinated with miss penalty to guide cache management. In this article, we first discuss how to enhance the existing cache model, the Average Eviction Time model, so that it can adapt to modeling a KV cache. After that, we apply the model to Redis and propose pRedis, Penalty- and Locality-aware Memory Allocation in Redis, which synthesizes data locality and miss penalty, in a quantitative manner, to guide memory allocation and replacement in Redis. At the same time, we also explore the diurnal behavior of a KV store and exploit long-term reuse. We replace the original passive eviction mechanism with an automatic dump/load mechanism, to smooth the transition between access peaks and valleys. Our evaluation shows that pRedis effectively reduces the average and tail access latency with minimal time and space overhead. For both real-world and synthetic workloads, our approach delivers an average of 14.0%∼52.3% latency reduction over a state-of-the-art penalty-aware cache management scheme, Hyperbolic Caching (HC), and shows more quantitative predictability of performance. Moreover, we can obtain even lower average latency (1.1%∼5.5%) when dynamically switching policies between pRedis and HC.

DYNAMIC CONTROL METHODS OF CACHE LINES REPLACEMENT POLICY

2020 ◽  
pp. 49-56
Author(s):  
A. V. Vishnekov ◽  
E. M. Ivanova
Keyword(s):  
Decision Making ◽  
Dynamic Control ◽  
Replacement Policy ◽  
Cache Memory ◽  
Cache Replacement ◽  
Program Code ◽  
Methods Development ◽  
Replacement Algorithm ◽  
Type Definition ◽  
Replacement Algorithms

The paper investigates the issues of increasing the performance of computing systems by improving the efficiency of cache memory, analyzes the efficiency indicators of replacement algorithms. We show the necessity of creation of automated or automatic means for cache memory tuning in the current conditions of program code execution, namely a dynamic cache replacement algorithms control by replacement of the current replacement algorithm by more effective one in current computation conditions. Methods development for caching policy control based on the program type definition: cyclic, sequential, locally-point, mixed. We suggest the procedure for selecting an effective replacement algorithm by support decision-making methods based on the current statistics of caching parameters. The paper gives the analysis of existing cache replacement algorithms. We propose a decision-making procedure for selecting an effective cache replacement algorithm based on the methods of ranking alternatives, preferences and hierarchy analysis. The critical number of cache hits, the average time of data query execution, the average cache latency are selected as indicators of initiation for the swapping procedure for the current replacement algorithm. The main advantage of the proposed approach is its universality. This approach assumes an adaptive decision-making procedure for the effective replacement algorithm selecting. The procedure allows the criteria variability for evaluating the replacement algorithms, its’ efficiency, and their preference for different types of program code. The dynamic swapping of the replacement algorithm with a more efficient one during the program execution improves the performance of the computer system.

scholarly journals Coherence State Awareness in Way-Replacement Algorithms for Multicore Processors

2019 ◽  
Author(s):  
Matheus Souza ◽  
Henrique Cota Freitas ◽  
Frédéric Pétrot
Keyword(s):  
Multicore Processors ◽  
Great Depth ◽  
General Purpose ◽  
Replacement Policy ◽  
Program Execution ◽  
Cache Replacement ◽  
Performance Impact ◽  
Account Information ◽  
Replacement Algorithms ◽  
Selection Algorithms

Due to their performance impact on program execution, cache replacement policies in set-associative caches have been studied in great depth. Currently, most general-purpose processors are multi-core, and among the very large corpus of research, and much to our surprise, we could not find any replacement policy that does actually take into account information relative to the sharing state of a cache way. Therefore, in this paper we propose to add, as a complement to the classical time-based related way-selection algorithms, an information relative to the sharing state and number of sharers of the ways. We propose several approaches to take this information into account, and our simulations show that LRU-based replacement policies can be slightly improved by them. Also, a much simpler policy, MRU, can be improved by our strategies, presenting up to 3.5× more IPC than baseline, and up to 82% less cache misses.

Two Cache Replacement Algorithms Based on Association Rules and Markov Models

2005 ◽  
Author(s):  
Keqiu Li ◽  
Wenyu Qu ◽  
Hong Shen ◽  
Di Wu ◽  
Takashi Nanya
Keyword(s):  
Association Rules ◽  
Markov Models ◽  
Cache Replacement ◽  
Replacement Algorithms
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