Cloud data centers require enormous amounts of energy to run their clusters of computers. There are huge financial and environmental incentives for cloud service providers to increase their energy efficiency without causing significant negative impacts on their customers' qualities of experience. Increasing resource utilization reduces energy consumption by consolidating workloads on fewer machines and allows cloud service providers to turn off inactive devices. While traditional architectures only allow virtual machines (VMs) to use the memory and CPU resources of a single device, VMs in a disaggregated cloud can utilize the small residual capacities of multiple separate devices. Separating VM resources across multiple devices leads to severe fragmentation that eventually negates any positive impact disaggregation has on utilization. To address the fragmentation problem, we present a method of ensuring a cloud operates using the minimal number of devices over time. Here we introduce an Energy-Aware Learning Agent (EALA) that uses reinforcement learning to guarantee the system can meet minimal quality of service requirements and provide energy savings without the need for VM migration. We evaluate the use of EALA guiding the decisions of Best-Fit compared to vanilla Best-Fit using the Google cluster trace. We show that EALA improves utilization by 2% and reduces the number of times that compute nodes switch on and off by 11% compared to vanilla Best-Fit.

To improve power efficiency, researchers are experimenting with dynamically adjusting the voltage and frequency margins of systems to just above the minimum required for reliable operation. Traditionally, manufacturers did not allow reducing these margins. Consequently, existing studies use system simulators, or software fault-injection methodologies, which are slow, inaccurate and cannot be applied on realistic workloads. However recent CPUs allow the operation outside the nominal voltage/frequency envelope. We present eXtended Margins eXperiment Manager (XM2) which enables the evaluation of software on systems operating outside their nominal margins. It supports both bare-metal and OS-controlled execution using an API to control the fault injection procedure and provides automatic management of experimental campaigns. XM2requires, on average, 5.6% extra lines of code and increases the application execution time by 2.5%. To demonstrate the flexibility of XM2, we perform three case studies: two employing bare-metal execution on a raspberry PI, and one featuring a full-fledged software stack (including OS) on an Intel Skylake Xeon processor.

This article introduces a significance-centric programming model and runtime support that sets the supply voltage in a multicore CPU to sub-nominal values to reduce the energy footprint and provide mechanisms to control output quality. The developers specify the significance of application tasks respecting their contribution to the output quality and provide check and repair functions for handling faults. On a multicore system, we evaluate five benchmarks using an energy model that quantifies the energy reduction. When executing the least-significant tasks unreliably, our approach leads to 20% CPU energy reduction with respect to a reliable execution and has minimal quality degradation.

Several applications may trade-off output quality for energy efficiency by computing only an approximation of their output. Current approaches to software-based approximate computing often require the programmer to specify parts of the code or data structures that can be approximated. A largely unaddressed challenge is how to automate the analysis of the significance of code for the output quality. To this end, we propose a methodology and toolset for automatic significance analysis. We use interval arithmetic and algorithmic differentiation in our profile-driven yet mathematical approach to evaluate the significance of input and intermediate variables for the output of a computation. Our methodology effectively matches decisions of a domain expert in significance characterization for a set of benchmarks, and in some cases offers new insights. Evaluation of the software infrastructure on a multicore x86 platform shows energy reduction (from 31% up to 91% with a mean of 56% compared to fully accurate execution, with graceful quality degradation.

We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energy-efficiency. This is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. The runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately. The experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.