Facebook의 사진 서비스 워크로드를 캐싱 계층(브라우저 캐시, 엣지 캐시, 서버 캐시 등)마다 분석하고, 각 레이어의 캐시 알고리즘 성능을 평가한 연구
Abstract (요약) 🕵🏻♂️
This paper examines the workload of Facebook's photo-serving stack and the effectiveness of the many layers of caching it employs. Facebook's image-management infrastructure is complex and geographically distributed. It includes browser caches on end-user systems, Edge Caches at ~20 PoPs, an Origin Cache, and for some kinds of images, additional caching via Akamai. The underlying image storage layer is widely distributed, and includes multiple data centers.
We instrumented every Facebook-controlled layer of the stack and sampled the resulting event stream to obtain traces covering over 77 million requests for more than 1 million unique photos. This permits us to study traffic patterns, cache access patterns, geolocation of clients and servers, and to explore correlation between properties of the content and accesses. Our results (1) quantify the overall traffic percentages served by different layers: 65.5% browser cache, 20.0% Edge Cache, 4.6% Origin Cache, and 9.9% Backend storage, (2) reveal that a significant portion of photo requests are routed to remote PoPs and data centers as a consequence both of load-balancing and peering policy, (3) demonstrate the potential performance benefits of coordinating Edge Caches and adopting S4LRU eviction algorithms at both Edge and Origin layers, and (4) show that the popularity of photos is highly dependent on content age and conditionally dependent on the social-networking metrics we considered.
sypark9646
commented
1 year ago
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Facebook의 사진 서비스 워크로드를 캐싱 계층(브라우저 캐시, 엣지 캐시, 서버 캐시 등)마다 분석하고, 각 레이어의 캐시 알고리즘 성능을 평가한 연구
Abstract (요약) 🕵🏻♂️
This paper examines the workload of Facebook's photo-serving stack and the effectiveness of the many layers of caching it employs. Facebook's image-management infrastructure is complex and geographically distributed. It includes browser caches on end-user systems, Edge Caches at ~20 PoPs, an Origin Cache, and for some kinds of images, additional caching via Akamai. The underlying image storage layer is widely distributed, and includes multiple data centers. We instrumented every Facebook-controlled layer of the stack and sampled the resulting event stream to obtain traces covering over 77 million requests for more than 1 million unique photos. This permits us to study traffic patterns, cache access patterns, geolocation of clients and servers, and to explore correlation between properties of the content and accesses. Our results (1) quantify the overall traffic percentages served by different layers: 65.5% browser cache, 20.0% Edge Cache, 4.6% Origin Cache, and 9.9% Backend storage, (2) reveal that a significant portion of photo requests are routed to remote PoPs and data centers as a consequence both of load-balancing and peering policy, (3) demonstrate the potential performance benefits of coordinating Edge Caches and adopting S4LRU eviction algorithms at both Edge and Origin layers, and (4) show that the popularity of photos is highly dependent on content age and conditionally dependent on the social-networking metrics we considered.
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