Energy efficiency and energy-proportional computing have become a central focus in enterprise server architecture. As thermal and electrical constraints limit system power, and datacenter operators become more conscious of energy costs, energy efficiency becomes important across the whole system. There are many proposals to scale energy at the datacenter and server level. However, one significant component of server power, the memory system, remains largely unaddressed. We propose memory dynamic voltage/frequency scaling (DVFS) to address this problem, and evaluate a simple algorithm in a real system.As we show, in a typical server platform, memory consumes 19% of system power on average while running SPEC CPU2006 workloads. While increasing core counts demand more bandwidth and drive the memory frequency upward, many workloads require much less than peak bandwidth. These workloads suffer minimal performance impact when memory frequency is reduced. When frequency reduces, voltage can be reduced as well. We demonstrate a large opportunity for memory power reduction with a simple control algorithm that adjusts memory voltage and frequency based on memory bandwidth utilization.We evaluate memory DVFS in a real system, emulating reduced memory frequency by altering timing registers and using an analytical model to compute power reduction. With an average of 0.17% slowdown, we show 10.4% average (20.5% max) memory power reduction, yielding 2.4% average (5.2% max) whole-system energy improvement.

The drive for higher performance and energy efficiency in data-centers has influenced trends toward increased power and cooling requirements in the facilities. Since enterprise servers rarely operate at their peak capacity, efficient power capping is deemed as a critical component of modern enterprise computing environments. In this paper we propose a new power measurement and power limiting architecture for main memory. Specifically, we describe a new approach for measuring memory power and demonstrate its applicability to a novel power limiting algorithm. We implement and evaluate our approach in the modern servers and show that we achieve up to 40% lower performance impact when compared to the stateof-art baseline across the power limiting range.

Consensus exists in much of industry and academia that engaging end-users is an essential element for improving energy efficiency in office buildings.We present our experiences implementing and deploying POEM (Personal Office Energy Monitor) with real office users. POEM is an end-user eco-feedback application. It provides detailed personalized data on energy usage and ambient conditions to each office user, as well as reporting aggregates for building-level management and policy setting. The POEM UI also allows users to state their subjective feeling of comfort. The system aggregates those inputs and informs the building manager to take corrective action if neededthus closing the control loop between the people and the building. We report our findings from pilot tests of POEM prototype.

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