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Big data strongly demands a network infrastructure having the capability to efficiently collect, process, cache, share, and deliver the data, instead of simple transmissions. Such network designs show the requirements of energy efficiency, availability, high performance, and data-aware intelligence. To meet these requirements, we adopt the information-centric networking (ICN) approach, where data are retrieved through names and in-network caching is utilized. However, as the typical existing ICN architectures, content centric network (CCN) cannot efficiently utilize the caches for data sharing because of the on-path caching strategy, and network of information (NetInf) demonstrates the resolution latency for data retrievals. To design an efficient and effective ICN architecture for big data sharing, we combine the strong points of CCN and NetInf, where information islands (IOIs) and management plane are utilized for direct data retrieval and global data discovery, respectively. We provide a reference architecture and propose an aggregatable name-based routing (ANBR), which can naturally enable consumers to retrieve the closest copy of information. In this network, each piece of data can be cached at one IOI at most once, which greatly improves the efficiency of cache usages. The consumers first try to retrieve the data in the local IOI, and then try to globally retrieve it from the closest IOI, holding the copy of the data if necessary. We investigate the impact from the key factor, IOI size, to the energy consumption of ANBR. It shows that energy consumption first decreases and then increases as the IOI size increases, and the optimized IOI size can be found for deployment. Furthermore, we study the relation between the optimized IOI size and the average retrieval times for the data. The result shows that the optimized IOI size increases as the average retrieval times increase.

An Aggregatable Name-Based Routing for Energy-Efficient Data Sharing in Big Data Era