Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Despite their widespread adoption in cloud computing, multicore processors are heavily under-utilized in terms of computing resources. To avoid the potential for negative and unpredictable interference, co-location of a latency-sensitive application with others on the same multicore processor is disallod, leaving many cores idle and causing low machine utilization. To enable co-location while providing QoS guarantees, it is challenging but important to predict performance interference beten co-located applications.  observed that the performance degradation of an application can be represented as a piecewise predictor function of the aggregate pressures on shared resources from all cores. Based on this observation,  propose to adopt regression analysis to build a predictor function for an application. Furthermore, the prediction model thus obtained for an application is able to characterize its contentiousness and sensitivity. Validation using a large number of single-threaded and multi-threaded benchmarks and nine real-world datacenter applications on two different platforms shows that our approach is also precise, with an average error not exceeding 0.4 percent.
['Jiacheng Zhao', 'Huimin Cui', 'Jingling Xue', 'Xiaobing Feng']
I am writing a research paper on a idea Predicting Cross-Core Performance Interference on Multicore Processors with Regression Analysis
[{"from": "human", "value": "I am writing a research paper on a idea Predicting Cross-Core Performance Interference on Multicore Processors with Regression Analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Despite their widespread adoption in cloud computing, multicore processors are heavily under-utilized in terms of computing resources. To avoid the potential for negative and unpredictable interference, co-location of a latency-sensitive application with others on the same multicore processor is disallod, leaving many cores idle and causing low machine utilization. To enable co-location while providing QoS guarantees, it is challenging but important to predict performance interference beten co-located applications.  observed that the performance degradation of an application can be represented as a piecewise predictor function of the aggregate pressures on shared resources from all cores. Based on this observation,  propose to adopt regression analysis to build a predictor function for an application. Furthermore, the prediction model thus obtained for an application is able to characterize its contentiousness and sensitivity. Validation using a large number of single-threaded and multi-threaded benchmarks and nine real-world datacenter applications on two different platforms shows that our approach is also precise, with an average error not exceeding 0.4 percent."}]