As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simultaneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.
The rapid expansion of online-based services requires novel energy and performance efficient architectures to meet power and latency constraints. Fast architectural exploration has become a key enabler in the proposal of architectural innovation. In this paper, we present gem5-X, a gem5-based system level simulation framework, and a methodology to optimize many-core systems for performance and power. As real-life case studies of many-core server workloads, we use real-time video transcoding and image classification using convolutional neural networks (CNNs). Gem5-X allows us to identify bottlenecks and evaluate the potential benefits of architectural extensions such as in-cache computing and 3D stacked High Bandwidth Memory. For real-time video transcoding, we achieve 15% speed-up using in-order cores with in-cache computing when compared to a baseline in-order system and 76% energy savings when compared to an Out-of-Order system. When using HBM, we further accelerate real-time transcoding and CNNs by up to 7% and 8% respectively.
Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and broadening of their run-time configuration space make the task of optimally operating software ever more complex. With the growing financial and environmental impact of data center operation and cloud-based applications, optimal software operation becomes increasingly more relevant to existing and next-generation workloads. In order to guide software operation towards energy savings, energy and performance data must be gathered to provide a meaningful assessment of the application behavior under different system configurations, which is not appropriately addressed in existing tools. In this work we present Containergy, a new performance evaluation and profiling tool that uses software containers to perform application run-time assessment, providing energy and performance profiling data with negligible overhead (below 2%). It is focused on energy efficiency for next generation workloads. Practical experiments with emerging workloads, such as video transcoding and machine-learning image classification, are presented. The profiling results are analyzed in terms of performance and energy savings under a Quality-of-Service (QoS) perspective. For video transcoding, we verified that wrong choices in the configuration space can lead to an increase above 300% in energy consumption for the same task and operational levels. Considering the image classification case study, the results show that the choice of the machine-learning algorithm and model affect significantly the energy efficiency. Profiling datasets of AlexNet and SqueezeNet, which present similar accuracy, indicate that the latter represents 55.8% in energy saving compared to the former.
Real-time video transcoding has recently raised as a valid alternative to address the ever-increasing demand for video contents in servers' infrastructures in current multiuser environments. High Efficiency Video Coding (HEVC) makes efficient online transcoding feasible as it enhances user experience by providing the adequate video configuration, reduces pressure on the network, and minimizes inefficient and costly video storage. However, the computational complexity of HEVC, together with its myriad of configuration parameters, raises challenges for power management, throughput control, and Quality of Service (QoS) satisfaction. This is particularly challenging in multiuser environments where multiple users with different resolution demands and bandwidth constraints need to be served simultaneously. In this work, we present MAMUT, a multiagent machine learning approach to tackle these challenges. Our proposal breaks the design space composed of run-time adaptation of the transcoder and system parameters into smaller sub-spaces that can be explored in a reasonable time by individual agents. While working cooperatively, each agent is in charge of learning and applying the optimal values for internal HEVC and system-wide parameters. In particular, MAMUT dynamically tunes Quantization Parameter, selects number of threads per video, and sets the operating frequency with throughput and video quality objectives under compression and power consumption constraints. We implement MAMUT on an enterprise multicore server and compare equivalent scenarios to state-ofthe-art alternative approaches. The obtained results reveal that MAMUT consistently attains up to 8x improvement in terms of FPS violations (and thus Quality of Service), 24% power reduction, as well as faster and more accurate adaptation both to the video contents and available resources.
Dealing with asymmetry in the architecture opens a plethora of questions from the perspective of scheduling task-parallel applications, and there exist early attempts to address this problem via ad-hoc strategies embedded into a runtime framework. In this paper we take a different path, which consists in addressing the complexity of the problem at the library level, via a few asymmetry-aware fundamental kernels, hiding the architecture heterogeneity from the task scheduler. For the specific domain of dense linear algebra, we show that this is not only possible but delivers much higher performance than a naive approach based on an asymmetry-oblivious scheduler. Furthermore, this solution also outperforms an ad-hoc asymmetry-aware scheduler furnished with sophisticated scheduling techniques.
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