An Energy-Efficient And Error-Resilient Server Ecosystem Exceeding Conservative Scaling Limits

PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)(2018)

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摘要
The explosive growth of Internet-connected devices will soon result in a flood of generated data, which will increase the demand for network bandwidth as well as compute power to process the generated data. Consequently, there is a need for more energy efficient servers to empower traditional centralized Cloud data-centers as well as emerging decentralized data-centers at the Edges of the Cloud. In this paper, we present our approach, which aims at developing a new class of micro-servers the UniServer that exceed the conservative energy and performance scaling boundaries by introducing novel mechanisms at all layers of the design stack. The main idea lies on the realization of the intrinsic hardware heterogeneity and the development of mechanisms that will automatically expose the unique varying capabilities of each hardware. Low overhead schemes are employed to monitor and predict the hardware behavior and report it to the system software. The system software including a virtualization and resource management layer is responsible for optimizing the system operation in terms of energy or performance, while guaranteeing non-disruptive operation under the extended operating points. Our characterization results on a 64-bit ARMv8 micro-server in 28nm process reveal large voltage margins in terms of Vmin variation among the 8 cores of the CPU chip, among three different sigma chips, and among different benchmarks with the potential to obtain up-to 38.8% energy savings. Similarly, DRAM characterizations show that refresh rate and voltage can be relaxed by 35x and 5%, respectively, leading to 23.2% power savings on average.
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关键词
low overhead schemes,hardware behavior,system software,virtualization,resource management layer,nondisruptive operation,extended operating points,ARMv8 microserver,error-resilient server ecosystem,conservative scaling limits,Internet-connected devices,generated data,network bandwidth,compute power,energy efficient servers,conservative energy,intrinsic hardware heterogeneity,unique varying capabilities,traditional centralized cloud data-centers
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