A Workload-Driven Unit of Cache Replacement for Mid-Tier Database Caching

2007 ◽  
pp. 374-385 ◽  
Author(s):  
Xiaodan Wang ◽  
Tanu Malik ◽  
Randal Burns ◽  
Stratos Papadomanolakis ◽  
Anastassia Ailamaki
Keyword(s):  
Cache Replacement ◽  
Database Caching

Web Cache Replacement with the Repairable LRU Algorithm

2015 ◽  
Vol 10 (6) ◽  
pp. 620 ◽  
Author(s):  
Prapai Sridama ◽  
Somchai Prakancharoen ◽  
Nalinpat Porrawatpreyakorn
Keyword(s):  
Cache Replacement ◽  
Web Cache

Enabling Service Cache in Edge Clouds

2021 ◽  
Vol 2 (3) ◽  
pp. 1-24
Author(s):  
Chih-Kai Huang ◽  
Shan-Hsiang Shen
Keyword(s):  
Virtual Machines ◽  
Replacement Policy ◽  
Cache Replacement ◽  
Cache Replacement Policy ◽  
Average Latency ◽  
Hit Rates ◽  
Iot Devices ◽  
Multi Access ◽  
Computational Resources ◽  
The Cost

The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache , which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.

CCF-LRU: hybrid storage cache replacement strategy based on counting cuckoo filter hot-probe method

2021 ◽  
Author(s):  
Yinyin Wang ◽  
Yuwang Yang ◽  
Xiulin Qiu ◽  
Yaqi Ke ◽  
Qingguang Wang
Keyword(s):  
Probe Method ◽  
Cache Replacement ◽  
Replacement Strategy ◽  
Hybrid Storage ◽  
Cuckoo Filter ◽  
Hot Probe

scholarly journals Relative competitive analysis of cache replacement policies

2008 ◽  
Vol 43 (7) ◽  
pp. 51-60 ◽  
Author(s):  
Jan Reineke ◽  
Daniel Grund
Keyword(s):  
Competitive Analysis ◽  
Cache Replacement ◽  
Cache Replacement Policies

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.

Value‐aware cache replacement in edge networks for Internet of Things

2021 ◽  
Author(s):  
Ting Peng ◽  
Haohao Wang ◽  
Cangming Liang ◽  
Pingping Dong ◽  
Yehua Wei ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Cache Replacement ◽  
Edge Networks
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