Iterative learning control of dynamic memory caching to enhance processing performance on java platform

In this study, computing system performance enhancement by dynamic memory scheduling has been presented. CPU processing time of the computing systems has been enhanced by classifying the arriving computing jobs on an “intelligent manner” and dynamically caching them in a limited memory space. In some special computing areas, like insurance risk investigation, calculations of income and premium need heavy and repetitive actuarial calculations. To improve the computation response time, computing schemes may be improved. In this study, a fairly special computing process has been elaborated, the computing jobs are always created by choosing the inputs among a finite set data such as age, income level, education, which describes the profile of the insurance policy holder or applicant who is inquiring the risk of his/her policy. Caching the repetitive jobs’ results and enhancing the CPU benefit has been presented and an intelligent regulation scheme has been introduced to software control. For experimental validation, Aspect Oriented Programming (AOP) methodology on Java platform is used. AOP allows identify computational jobs and their parameters based on the marked annotations, and the repeating jobs and the same results may be accessible for caching