CBR and neural networks based technique for predictive prefetching
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
A hybrid intelligent system to improve predictive accuracy for cache prefetching
Expert Systems with Applications: An International Journal
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Storage systems need to provide high IO transactions in addition to their capacity and availability requirements to achieve high assurance characteristics, such as fault tolerance and timeliness. Caches built in large and medium size storage systems have not been very effective to improve the IO transactions and overall performance of the system. Caches at various layers of the memory hierarchy in computing systems are more fast but very small compared to the adjacent lower level of storage device. This paper proposes a novel concept to exploit local memory as a block device to improve the IO performance of storage systems called Data Transmission System (DTS) concept. This concept is based on hierarchical layered memory utilization and management in the information system. Data accessed frequently is maintained on the managed DTS cache device in close proximity to the CPU. The proposed concept is substantially different from the conventional unmanaged cache in the computing systems. System architecture and detailed system design are presented in this paper to realize the proposed concept. Performance evaluation of the system has been carried out through numerical as well as application scenario. The experimental results show improvement of the order of 10 to 100 times faster IO transactions and reduce CPU wait time significantly.