PROJECT TITLE :
Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm
Inexperienced cloud is an rising new technology in the computing world in that memory is a important component. Part-amendment memory (PCM) is one amongst the foremost promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent analysis has been specializing in the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, a lot of than one bit of data will be stored in one single PCM cell. But, the MLC PCM suffers from the degradation of performance compared to the one-level cell (SLC) PCM, thanks to the longer memory access time. During this paper, we gift a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in inexperienced clouds. The proposed genetic-primarily based algorithm not only schedules and assigns tasks to cores within the CMP system, but conjointly provides a PCM MLC configuration that balances the PCM memory performance in addition as the potency. The experimental results show that our genetic-based mostly algorithm can significantly cut back the most memory usage by 76.eight percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 % comparing with the uniform four bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.five percent comparing with the uniform four bits/cell MLC configuration in terms of total execution time.
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