{"title":"Beyond Belady to Attain a Seemingly Unattainable Byte Miss Ratio for Content Delivery Networks","authors":"Peng Wang;Hong Jiang;Yu Liu;Zhelong Zhao;Ke Zhou;Zhihai Huang","doi":"10.1109/TPDS.2024.3452096","DOIUrl":null,"url":null,"abstract":"Reducing the byte miss ratio (BMR) in the Content Delivery Network (CDN) caches can help providers save on the cost of paying for traffic. When evicting objects or files of different sizes in the caches of CDNs, it is no longer sufficient to pursue an optimal object miss ratio (OMR) by approximating Belady to ensure an optimal BMR. Our experimental observations suggest that there are multiple request sequence windows. In these windows, a replacement policy prioritizes the eviction of objects with large sizes and ultimately evicts the object with the longest reuse distance, lowering the BMR without increasing the OMR. To accurately capture those windows, we monitor the changes in OMR and BMR using a deep reinforcement learning (RL) model and then implement a BMR-friendly replacement algorithm in these windows. Based on this policy, we propose a Belady and Size Eviction (LRU-BaSE) algorithm that reduces BMR while maintaining OMR. To make LRU-BaSE efficient and practical, we address the feedback delay problem of RL with a two-pronged approach. On the one hand, we shorten the LRU-base decision region based on the observation that the rear section of the cache queue contains most of the eviction candidates. On the other hand, the request distribution on CDNs makes it feasible to divide the learning region into multiple sub-regions that are each learned with reduced time and increased accuracy. In real CDN systems, LRU-BaSE outperforms LRU by reducing “backing to OS” traffic and access latency by 30.05% and 17.07%, respectively, on average. In simulator tests, LRU-BaSE outperforms state-of-the-art cache replacement policies. On average, LRU-BaSE's BMR is 0.63% and 0.33% less than that of Belady and Practical Flow-based Offline Optimal (PFOO), respectively. In addition, compared to Learning Relaxed Belady (LRB), LRU-BaSE can yield relatively stable performance when facing workload drift.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10660562/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Reducing the byte miss ratio (BMR) in the Content Delivery Network (CDN) caches can help providers save on the cost of paying for traffic. When evicting objects or files of different sizes in the caches of CDNs, it is no longer sufficient to pursue an optimal object miss ratio (OMR) by approximating Belady to ensure an optimal BMR. Our experimental observations suggest that there are multiple request sequence windows. In these windows, a replacement policy prioritizes the eviction of objects with large sizes and ultimately evicts the object with the longest reuse distance, lowering the BMR without increasing the OMR. To accurately capture those windows, we monitor the changes in OMR and BMR using a deep reinforcement learning (RL) model and then implement a BMR-friendly replacement algorithm in these windows. Based on this policy, we propose a Belady and Size Eviction (LRU-BaSE) algorithm that reduces BMR while maintaining OMR. To make LRU-BaSE efficient and practical, we address the feedback delay problem of RL with a two-pronged approach. On the one hand, we shorten the LRU-base decision region based on the observation that the rear section of the cache queue contains most of the eviction candidates. On the other hand, the request distribution on CDNs makes it feasible to divide the learning region into multiple sub-regions that are each learned with reduced time and increased accuracy. In real CDN systems, LRU-BaSE outperforms LRU by reducing “backing to OS” traffic and access latency by 30.05% and 17.07%, respectively, on average. In simulator tests, LRU-BaSE outperforms state-of-the-art cache replacement policies. On average, LRU-BaSE's BMR is 0.63% and 0.33% less than that of Belady and Practical Flow-based Offline Optimal (PFOO), respectively. In addition, compared to Learning Relaxed Belady (LRB), LRU-BaSE can yield relatively stable performance when facing workload drift.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.