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A Resource Efficient Multi-Dimensional Cache Management Strategy in Content Centric Networks

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In-network cache management in Content-Centric Networking (CCN) has received significant interest from the research community in recent years. The default caching strategy of CCN is to store the content at each content-router (CR) along the downloading path. While this helps in increasing content availability and quality-of-experience (QoE) by reducing delay and the server load, the strategy is not resource-efficient as it increases the cache redundancy unnecessarily. A number of cache management schemes have been proposed to address the issue of resource utilization in CCN, and most of these schemes consider only one attribute of network while taking the content placement decision. This results in sub-optimal network performance as a single parameter may be useful for one scenario and may not be as effective for another. The resource utilization can be improved by considering multiple attributes simultaneously for cache management, which can result in better network performance even with dynamic environments. With this in mind, we propose an adaptive caching strategy, named as Multi-Attribute Caching (MAC) Strategy based on multi-parameters for CCN. MAC attempts to overcome inefficient cache utilization by intelligently selecting caching locations along the content delivery path. Simulation results show that MAC reduces cache load at each node while providing comparable delay and better cache hit rate using both synthetic and real network topologies.
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Keywords: CACHE DECISION; CACHE PLACEMENT; CONTENT POPULARITY; CONTENT-CENTRIC NETWORKING; IN-NETWORK CACHING

Document Type: Research Article

Publication date: 01 April 2018

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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