(A) study on the quantitative server sizing model for performance satisfaction in secure U2L migration

Abstract

There are many challenges in measuring capacity using metrics such as transactions per minute (TPM) and operation per second (OPS) for all server hardware, which are becoming increasingly obsolete due to the shortening of the lifecycle of hardware and the advent of microprocessors. Instead, the results of accredited performance measurements are used to measure these standards, which are further used as references in the capacity measuring methods employed by industries. Generally, in industries, the capacity of a WEB application server is defined by OPS, for which no clear transition criterion exists for calculating tpmC using an empirical verification method. Considering secure Unix to Linux (U2L) x86-based server migration, there are no methods to compare and verify the max-jOPS value of Standard Performance Evaluation Corp., which is an industry-recognized performance measurement standard, to the Unix-based benchmark tpmC. In this study, a scenario-based U2L migration was empirically verified by analyzing and comparing pre-to-post with the interpretation of a census statistical system log data, which was conducted on approximately 1.7M households over 21 days with 25,288 maximum concurrent users. We present the correlation through pre-to-post comparison and analysis of U2L for each census statistical system log data by measuring the maximum CPU utilization as U2L migration between heterogeneous CPUs. The correlation is applied to OPS using the tpmC value of TPC-C proportional equation and quantified as a derived conversion ratio of OPS to max-jOPS. Consequently, we formulated and normalized an arithmetic expression, resulting in a CPU core conversion ratio of 0.165 as facile from a Unix-based legacy platform to an x86-based server. Therefore, we propose a new server sizing model for secure U2L migration between heterogeneous CPU architectures, which results in an average of 14.3% improvement in data processing time.