With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for opti...With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for optimizing video content distribution.However,the limited edge node cache capacity and dynamic user requests make edge caching more complex.Therefore,we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming(FlyCache)designed to improve users’Quality of Experience(QoE)and reduce backhaul traffic consumption.FlyCache implements intelligent caching management across three key stages:before-playback,during-playback,and after-playback.Specifically,we introduce a cache placement policy for the before-playback stage,a dynamic prefetching and cache admission policy for the during-playback stage,and a progressive cache eviction policy for the after-playback stage.To validate the effectiveness of FlyCache,we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms.Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate,backhaul traffic,and delayed startup rate.展开更多
Cache performance is a critical design constraint for modern many-core systems.Since the cache often works in a"black-box"manner,it is difficult for the software to reason about the cache behavior to match t...Cache performance is a critical design constraint for modern many-core systems.Since the cache often works in a"black-box"manner,it is difficult for the software to reason about the cache behavior to match the running software to the underlying hardware.To better support code optimization,we need to understand and characterize the cache be-havior.While cache performance characterization is heavily studied on traditional x86 architectures,there is little work for understanding the cache implementations on emerging ARMv8-based many-cores.This paper presents a comprehensive study to evaluate the cache architecture design on three representative ARMv8 multi-cores,Phytium 2000+,ThunderX2,and Kunpeng 920(KP920).To this end,we develop wrBench,a micro-benchmark suite to measure the realized latency and bandwidth of caches at different memory hierarchies when performing core-to-core communication.Our evaluation pro-vides inter-core latency and bandwidth in different cache levels and coherency states for the three ARMv8 many-cores.The quantitative performance data is shown in tables.We mine the characteristics of caches and coherency protocols by analyzing the data for the three processors,Phytium 2000+,ThunderX2,and KP920.Our paper also provides discussions and guidelines for optimizing memory access on ARMv8 many-cores.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)[Grant No.62072469].
文摘With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for optimizing video content distribution.However,the limited edge node cache capacity and dynamic user requests make edge caching more complex.Therefore,we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming(FlyCache)designed to improve users’Quality of Experience(QoE)and reduce backhaul traffic consumption.FlyCache implements intelligent caching management across three key stages:before-playback,during-playback,and after-playback.Specifically,we introduce a cache placement policy for the before-playback stage,a dynamic prefetching and cache admission policy for the during-playback stage,and a progressive cache eviction policy for the after-playback stage.To validate the effectiveness of FlyCache,we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms.Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate,backhaul traffic,and delayed startup rate.
基金funded by the National Key Research and Development Program of China under Grant No.2018YFB0204301the National Natural Science Foundation of China under Grant Nos.61972408 and 61872294.
文摘Cache performance is a critical design constraint for modern many-core systems.Since the cache often works in a"black-box"manner,it is difficult for the software to reason about the cache behavior to match the running software to the underlying hardware.To better support code optimization,we need to understand and characterize the cache be-havior.While cache performance characterization is heavily studied on traditional x86 architectures,there is little work for understanding the cache implementations on emerging ARMv8-based many-cores.This paper presents a comprehensive study to evaluate the cache architecture design on three representative ARMv8 multi-cores,Phytium 2000+,ThunderX2,and Kunpeng 920(KP920).To this end,we develop wrBench,a micro-benchmark suite to measure the realized latency and bandwidth of caches at different memory hierarchies when performing core-to-core communication.Our evaluation pro-vides inter-core latency and bandwidth in different cache levels and coherency states for the three ARMv8 many-cores.The quantitative performance data is shown in tables.We mine the characteristics of caches and coherency protocols by analyzing the data for the three processors,Phytium 2000+,ThunderX2,and KP920.Our paper also provides discussions and guidelines for optimizing memory access on ARMv8 many-cores.
