Information-centric networking (ICN) is a promising architecture and has attracted much attention in the area of future Internet architectures. As one of the key technologies in ICN, in-network caching can enhance content retrieval at a global scale without requiring any special infrastructure. In this paper, we propose a workload-aware caching policy, LRU-GT, which allows cache nodes to protect newly cached contents for a period of time (guard time) during which contents are protected from being replaced. LRU-GT can utilize the temporal locality and distinguish contents of different popularity, which are both the characteristics of the workload. Cache replacement is modeled as a semi-Markov process under the Independent Reference Model (IRM) assumption and a theoretical analysis proves that popular contents have longer sojourn time in the cache compared with unpopular ones in LRU-GT and the value of guard time can affect the cache hit ratio. We also propose a dynamic guard time adjustment algorithm to optimize the performance. Simulation results show that LRU-GT can reduce the average hops to get contents and improve cache hit ratio.

As a promising future network architecture, Information-centric networking (ICN) has attracted much attention, its ubiquitous in-network caching is one of the key technologies to optimize the dissemination of information. However, considering the diversity of contents and the limitation of cache resources in the Internet, it is usually difficult to find a one-fit-all caching strategy. How to manage the ubiquitous in-network cache in ICN has become an important problem. In this paper, we explore ways to improve cache performance from the three perspectives of spatiality, temporality and availability, based on which we further propose an in-network cache management strategy to support differentiated service. We divide contents requested in the network into different levels and the selection of caching strategies depends on the content level. Furthermore, the corresponding models of utilizing cache resources in spatiality, temporality and availability are also derived for comparison and analysis. Simulation verifies that our differentiated service based cache management strategy can optimize the utilization of cache resources and get higher overall cache performance.