On Understanding the Energy Consumption of ARM-based Multicore Servers

There is growing interest to replace traditional servers with low-power multicore systems such as ARM Cortex-A9. However, such systems are typically provisioned for mobile applications that have lower memory and I/O requirements than server application. Thus, the impact and extent of the imbalance between application and system resources in exploiting energy efficient execution of server workloads is unclear. This paper proposes a trace-driven analytical model for understanding the energy performance of server work-loads on ARM Cortex-A9 multicore systems. Key to our approach is the modeling of the degrees of CPU core, memory and I/O re-source overlap, and in estimating the number of cores and clock fre-quency that optimizes energy performance without compromising execution time. Since energy usage is the product of utilized power and execution time, the model first estimates the execution time o