Abstract
The Information-Centric Network (ICN) is one of the most influential future network architectures and in-network caching in ICN brings some helpful features, such as low latency and mobility support. How to allocate cache capacity and place content properly will greatly influence the performance of ICN. This paper focuses on the cache allocation problem and content placement problem under the given cache space budget. Firstly, a lightweight allocation method utilizing information of both topology and content popularity is proposed, to allocate cache space and get the expected number of copies of popular content. The expected number of copies represents the number of content copies placed in the topology. Then, an on-path caching scheme based on the expected number of copies is proposed to handle the content placement problem. In the cache allocation scenario, the lightweight allocation method performs better than other baseline methods. In the content placement scenario, Leave Copy Down (LCD) based on the expected number of copies performs the second-best and is very close to Optimal Content Placement (OCP).
Highlights
The Information-Centric Network (ICN) has gained widespread attention in recent years
We introduce the concept of the Expected Number of Copies (ENC)—each content cached on an ICN node has an ENC, to control the content number of copies cached on this node and downstream nodes
We focus on the cache allocation problem and the content placement problem under a given cache space budget, with the optimization goal: to minimize the average network hop count from data transfer
Summary
The Information-Centric Network (ICN) has gained widespread attention in recent years. Manifold learning was used in Reference [6] to calculate the importance of the node, while graph-related centralities of nodes were used for cache allocation in Reference [7] These methods improve network performance to some extent, while [7] concluded that gain brought by heterogeneous cache capacity is very limited. We focus on the cache allocation problem and the content placement problem under a given cache space budget, with the optimization goal: to minimize the average network hop count from data transfer. The method distributes the total cache budget across all nodes and places content to nodes, maximizing network benefit This method calculates the ENC of different content, to guide content placement.
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