Motivation

1. Power Capping and Consolidation Techniques for Reducing the Cost of Computing Comp-to-Comm Useful work Bus Access Can Hankendi AyseK. Coskun Electrical and Computer Engineering Department, Boston University, MA, USA {hankendi, acoskun}@bu.edu IPC & Memory Utilization Useful work Comp-to-Comm Abstract [No. 22] Adaptive Power Capping [1] Experimental Results • Control Knobs for Power Capping • Dynamic Voltage-Frequency Scaling • Thread Packing: Allocating m threads on n number of cores, where m > n • Energy-related costs are projected to be the major contributor to total cost of ownership in computing clusters. As today’s computing trends are moving towards the cloud, meeting the increasing computational demand while minimizing the energy costs in data centers is essential to enable sustainability. • This work introduces two techniques to reduce energy consumption of computing clusters. • (1) We propose a novel power capping technique to constrain the power consumption of computing nodes. Capping the peak power is a desired feature as it provides effective cost control for clusters. Our technique combines Dynamic Voltage-Frequency Scaling (DVFS) and thread allocation on multicore systems. By utilizing machine learning techniques, our power capping method is able to meet the power budgets 82% of the time without requiring any power measurement device and reduces the energy consumption by 51.6% on average in comparison to the state-of-the-art techniques. • (2) We introduce a novel policy for selecting application pairs to co-schedule on multicore servers. Workload consolidation is a strategy to reduce energy consumption by utilizing the same physical resources for multiple workloads. We demonstrate that energy savings due to consolidation vary significantly depending on the characteristics of the workloads. Through dynamic workload analysis, our technique improves the energy per work savings up to 22% compared to existing consolidation methods. Power Capping: • MLR Model is queried during runtime to enforce the control decisions (DVFS & Thread#) • 82% adherence to given power constraints Performance counter data: μ-ops retired load locks l3-cache misses l2-cache misses … Core Temperatures Frequency Active Core Number • Classifier built using multinomial logistic regression (MLR) • Transforms continuous input onto probability of discrete outputs: • Thread Number & DVFS Setting • Added flexibility of thread packing reduces energy by 51.6% • Thread packing increases the dynamic power range by 21% Server Node delay inputs power x Motivation ϕ(x) y C. Model Learning B. Optimal Setting Calculation A. All Ratios Calculation • Energy consumption of computing clusters is increasing by 15% per year • Energy efficiency and budget/cost control are the major challenges for data centers w Model Parameters • L1-Regularization • Power Capping • Constraints power usage • Provides effective cost control • Challenge: Optimizing performance within a power cap Consolidation: Energy-Efficient Consolidation [2] • We generate 50 random workload sets to evaluate • Policy selection algorithm achieves 82% accuracy for selecting the best policy • Improves the E/work by 31% in comparison to unconsolidated case • (1) Selects the co-scheduling policy alternative that is the most energy-efficient • (2) Balances the performance event according to the selected policy • Consolidation • Reduces number of active server nodes • Challenge: Energy savings vary depending on the workload characteristics • Improves E/work by 9% in comparison to the best policy • Our policy provides up to 14% more energy/work savings in comparison to the best performing policy with less than 0.1% performance degradation Target Systems • Quad-core Intel Core i7 • Operating frequencies: • 1.60 GHz – 2.93 GHz at 1333 MHz intervals Policy Selection Algorithm • Check computation-to-communication ratio: (IPC*CPU Utilization)/Bus Accesses Policy Selection • 12-core AMD MagnyCours • Two 6-core processors in a single package Cache Miss Based -> Balance Useful Work IPC*CPU Utilization Memory Utilization Cache Misses Bus Accesses Policy Selection Algorithm References • Check comp-to-command memory utilization IPC*CPU Utilization Based • Check memory utilization [1]R.Cochran, C. Hankendi, A. Coskun, S. Reda ‘Pack & Cap: Adaptive DVFS and Thread Packing Under Power Caps’. In MICRO, 2011. [2] C. Hankendi, A. Coskun, ‘Reducing the Cost of Computing through Efficient Co-Scheduling of Parallel Workloads’. In DATE, 2012. [3]C. Bienia et al. The PARSEC benchmark suite: characterization and architectural implications. In PACT, 2008. *This work is funded by VMware, Inc. and Oracle, Inc.. Overall metrics for each workload set Memory Utilization -> Balance Useful Work -> Balance Cache Misses Applications: PARSEC 2.1 Parallel Benchmarks [3]