On Learning Suitable Caching Policies for In-Network Caching

Stéfani Pires;Adriana Ribeiro;Leobino N. Sampaio
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Abstract

In-network cache architectures, such as Information-centric networks (ICNs), have proven to be an efficient alternative to deal with the growing content consumption on networks. In caching networks, any device can potentially act as a caching node. In practice, real cache networks may employ different caching replacement policies by a node. The reason is that the policies may vary in efficiency according to unbounded context factors, such as cache size, content request pattern, content distribution popularity, and the relative cache location. The lack of suitable policies for all nodes and scenarios undermines the efficient use of available cache resources. Therefore, a new model for choosing caching policies appropriately to cache contexts on-demand and over time becomes necessary. In this direction, we propose a new caching meta-policy strategy capable of learning the most appropriate policy for cache online and dynamically adapting to context variations that leads to changes in which policy is best. The meta-policy decouples the eviction strategy from managing the context information used by the policy, and models the choice of suitable policies as online learning with a bandit feedback problem. The meta-policy supports deploying a diverse set of self-contained caching policies in different scenarios, including adaptive policies. Experimental results with single and multiple caches have shown the meta-policy effectiveness and adaptability to different content request models in synthetic and trace-driven simulations. Moreover, we compared the meta-policy adaptive behavior with the Adaptive Replacement Policy (ARC) behavior.
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论学习适合网络内缓存的缓存策略
以信息为中心的网络(ICN)等网内高速缓存架构已被证明是应对网络上日益增长的内容消费的有效替代方案。在缓存网络中,任何设备都有可能充当缓存节点。在实践中,实际的高速缓存网络可能会对一个节点采用不同的高速缓存替换策略。原因在于,这些策略的效率可能会因各种无限制的背景因素而有所不同,如缓存大小、内容请求模式、内容分布流行度和相对缓存位置等。缺乏适合所有节点和场景的策略会影响可用缓存资源的有效利用。因此,有必要建立一个新的模型,根据缓存环境按需并随时间推移选择合适的缓存策略。为此,我们提出了一种新的缓存元政策策略,它能够在线学习最合适的缓存政策,并动态适应导致最佳政策发生变化的上下文变化。元策略将驱逐策略与管理策略所使用的上下文信息分离开来,并将合适策略的选择建模为具有强盗反馈问题的在线学习。元政策支持在不同场景下部署多种自足缓存政策,包括自适应政策。单缓存和多缓存的实验结果表明,在合成和跟踪驱动的模拟中,元政策对不同的内容请求模型具有有效性和适应性。此外,我们还将元政策自适应行为与自适应替换政策(ARC)行为进行了比较。
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