Performance evaluation for new web caching strategies combining LRU with score based object selection

Hasslinger, Gerhard; Ntougias, Konstantinos; Hasslinger, Frank; Hohlfeld, Oliver

doi:10.1016/j.comnet.2017.04.044

Cited by 34 publications

(17 citation statements)

References 84 publications

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“…where the sums are over possible global popularity profiles as well as files, and where the indicator function 1 {•} is 1 if its argument holds true; and 0 otherwise; while θ G i,f captures the average "overall" cost if the system is in global state p i G , and the CCU decides not to cache the f th content. By defining the (16) as…”

Section: Scalable Cachingmentioning

confidence: 99%

“…Nonetheless, popularities in practice are dynamic, meaning they fluctuate over a time horizon. For instance, half of the top 25 requested Wikipedia articles change on a daily basis [15,16], which motivates well adaptive caching strategies that can account for popularity dynamics [17,18,19,20,21,22,23]. To approximate the evolution of popularities, a Poisson shot noise model was adopted in [18], for which an age-based caching solution was proposed in [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement Learning for Caching with Space-Time Popularity Dynamics

Sadeghi¹,

Giannakis²,

Wang³

et al. 2020

Preprint

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With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. Considering the geographical and temporal content popularity dynamics, the limited available storage at cache nodes, as well as the interactive influence of caching decisions in networked caching settings, developing effective caching policies is practically challenging. In response to these challenges, this chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design, in both single-node and network caching settings under dynamic space-time popularities. The herein presented policies are complemented using a set of numerical tests, which showcase the merits of the presented approach relative to several standard caching policies.

show abstract

Section: Scalable Cachingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Caching with Space-Time Popularity Dynamics

Sadeghi¹,

Giannakis²,

Wang³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Ma et al [23] proposed a new weighting size and cost replacement policy (WSCRP), which was an extension of LFU. Hasslinger et al [24] proposed a class of score gate least recently used (SG-LRU) for web caching strategies. The hit ratios achieved by SG-LRU were better than that of LRU.…”

Section: Related Workmentioning

confidence: 99%

C3C: A New Static Content-Based Three-Level Web Cache

Trinh

Huang

2019

IEEE Access

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One way of improving the performance of a search engine is increasing the hit ratio of the search engine cache. A common and widely used approach for increasing the hit ratio is a combination of the result cache, the posting list cache and the intersection cache, yielding a three-level cache architecture. However, the existing multi-level cache architectures do not consider the dependencies among the content cached in different parts. Thus, the same content might be stored multiple times in the architectures, resulting in duplicate hits. In other words, a large amount of space in the cache is wasted. In this paper, we propose a new static content-based three-level cache architecture that adopts a new C3C filling algorithm that takes into account the dependencies among the content cached in different parts. In the proposed cache architecture, duplicate hits are reduced and the hit ratio increases. Extensive experiments were conducted on a real data set. The results have shown a significant improvement on the hit ratios compared with two existing approaches.

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“…To cope with limited memory size, several replacement policies have been proposed in the literature, from simple First Input First Output (FIFO), Least Recently Used (LRU), and Least Frequently Used (LFU) schemes to the recent hyperbolic caching [50], Score-Gated LRU (SG-LRU) cache [51], probabilistic caching [3], and many others [52]. Performance evaluation of caching strategies and replacement policies are deeply tied to the traffic model.…”

Section: In-network Cachingmentioning

confidence: 99%

Analysis of Data Persistence in Collaborative Content Creation Systems: The Wikipedia Case

et al. 2019

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A very common problem in designing caching/prefetching systems, distribution networks, search engines, and web-crawlers is determining how long a given content lasts before being updated, i.e., its update frequency. Indeed, while some content is not frequently updated (e.g., videos), in other cases revisions periodically invalidate contents. In this work, we present an analysis of Wikipedia, currently the 5th most visited website in the world, evaluating the statistics of updates of its pages and their relationship with page view statistics. We discovered that the number of updates of a page follows a lognormal distribution. We provide fitting parameters as well as a goodness of fit analysis, showing the statistical significance of the model to describe the empirical data. We perform an analysis of the views-updates relationship, showing that in a time period of a month, there is a lack of evident correlation between the most updated pages and the most viewed pages. However, observing specific pages, we show that there is a strong correlation between the peaks of views and updates, and we find that in more than 50% of cases, the time difference between the two peaks is less than a week. This reflects the underlying process whereby an event causes both an update and a visit peak that occurs with different time delays. This behavior can pave the way for predictive traffic analysis applications based on content update statistics. Finally, we show how the model can be used to evaluate the performance of an in-network caching scenario.

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Performance evaluation for new web caching strategies combining LRU with score based object selection

Cited by 34 publications

References 84 publications

Reinforcement Learning for Caching with Space-Time Popularity Dynamics

Reinforcement Learning for Caching with Space-Time Popularity Dynamics

C3C: A New Static Content-Based Three-Level Web Cache

Analysis of Data Persistence in Collaborative Content Creation Systems: The Wikipedia Case

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