Authors:
Dorian Perkins, Nitin Agrawal, Akshat Aranya, Curtis Yu, Younghwan Go, Harsha V. Madhyastha, Cristian Ungureanu
Abstract:
Memory-based key-value stores, such as Memcached and Redis, are often used to speed up web applications. Specifically, they are used to cache the results of computations, such as database queries and dynamically generated web pages, so that a future request to the web application may not have to repeat the same computation. Currently, when memory-based key-value stores reach their capacity limits, they use replacement policies, like LRU and random, that are oblivious to differences among the cached results in their recomputation costs. However, this paper shows that if the costs of recomputing cached results vary significantly, as in the RUBiS and TPC-W benchmarks, then a cost-aware replacement policy will not only reduce the web application's total recomputation cost but also reduce its average response time.
To this end, this paper introduces GD-Wheel, which is an amortized constant-time implementation of the GreedyDual replacement algorithm that supports a limited range of costs. In effect, GD-Wheel integrates recency of access and cost of recomputation in an efficient manner. Moreover, this paper describes an implementation of GD-Wheel in Memcached, including the modifications to Memcached's interface so that web applications can include cost information with each key-value pair, and a new cost-aware slab rebalancing policy for Memcached's slab-based memory allocator. An evaluation of this implementation using the Yahoo! Cloud Serving Benchmark shows that GD-Wheel, when compared to LRU, reduces the total recomputation cost by as much as 90%. Moreover, GD-Wheel reduces the web application's average and 99th percentile latency to obtain the computed results by as much as 56% and 85%, respectively.
ACM DL:
http://dl.acm.org/citation.cfm?id=2741956DOI:
http://dx.doi.org/10.1145/2741948.2741956About the Site 🌐
This site provides links to random videos hosted at YouTube, with the emphasis on random. 🎥
Origins of the Idea 🌱
The original idea for this site stemmed from the need to benchmark the popularity of a video against the general population of YouTube videos. 🧠
Challenges Faced 🤔
Obtaining a large sample of videos was crucial for accurate ranking, but YouTube lacks a direct method to gather random video IDs.
Even searching for random strings on YouTube doesn't yield truly random results, complicating the process further. 🔍
Creating Truly Random Links 🛠️
The YouTube API offers additional functions enabling the discovery of more random videos. Through inventive techniques and a touch of space-time manipulation, we've achieved a process yielding nearly 100% random links to YouTube videos.
About YouTube 📺
YouTube, an American video-sharing website based in San Bruno, California, offers a diverse range of user-generated and corporate media content. 🌟
Content and Users 🎵
Users can upload, view, rate, share, and comment on videos, with content spanning video clips, music videos, live streams, and more.
While most content is uploaded by individuals, media corporations like CBS and the BBC also contribute. Unregistered users can watch videos, while registered users enjoy additional privileges such as uploading unlimited videos and adding comments.
Monetization and Impact 🤑
YouTube and creators earn revenue through Google AdSense, with most videos free to view. Premium channels and subscription services like YouTube Music and YouTube Premium offer ad-free streaming.
As of February 2017, over 400 hours of content were uploaded to YouTube every minute, with the site ranking as the second-most popular globally. By May 2019, this figure exceeded 500 hours per minute. 📈
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Random YouTube Videos Generator
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