Abstract-Quantitatively estimating the relationship between the workload and the corresponding power consumption of a multicore processor is an essential step towards achieving energy proportional computing. Most existing and proposed approaches use Performance Monitoring Counters (Hardware Monitoring Counters) for this task. In this paper we propose a complementary approach that employs the statistics of CPU utilization (workload) only. Hence, we model the workload and the power consumption of a multicore processor as random variables and exploit the monotonicity property of their distribution functions to establish a quantitative relationship between the random variables. We will show that for a single-core processor the relationship is best approximated by a quadratic function whereas for a dualcore processor, the relationship is best approximated by a linear function. We will demonstrate the plausibility of our approach by estimating the power consumption of both custom-made and standard benchmarks (namely, the SPEC power benchmark and the Apache benchmarking tool) for an Intel and AMD processors.
Dargie REF proposed a stochastic model to estimate the power consumption for a multicore processor based on the CPU utilisation workload and found out that the relationship between the workload and power is best estimated using a linear function in a dualcore processor and using a quadratic function in a single-core processor.
A Stochastic Model for Estimating the Power Consumption of a Processor
IEEE Transactions on Computers
IEEE Transactions on Computers
Computer Science