Interference of billing and scheduling strategies for energy and cost savings in modern data centers

The high energy consumption of HPC systems is an obstacle for evergrowing systems. Unfortunately, energy consumption does not decrease linearly with reduced workload; therefore, energy conservation techniques have been deployed on various levels which steer the overall system. While the overall saving of energy is useful, the price of energy is not necessarily proportional to the consumption. Particularly with renewable energies, there are occasions in which the price is significantly lower. The potential of saving energy costs when using smart contracts with energy providers is lacking research. In this paper, we conduct an analysis of the potential savings when applying cost-aware schedulers to data center workloads while considering power contracts that allow for dynamic (hourly) pricing. The contributions of this paper are twofold: 1) the theoretic assessment of cost savings; 2) the development of a simulator to replay batch scheduler traces which supports flexible energy cost models and various cost-aware scheduling algorithms. This allows to approximate the energy costs savings of data centers for various scenarios including off-peak and hourly budgeted energy prices as provided by the energy spot market. An evaluation is conducted with four annual job traces from the German Climate Computing Center (DKRZ) and Leibniz Supercomputing Centre (LRZ)

Power-Aware Job Dispatching in High Performance Computing Systems

This works deals with the power-aware job dispatching problem in supercomputers; broadly speaking the dispatching consists of assigning finite capacity resources to a set of activities, with a special concern toward power and energy efficient solutions. We introduce novel optimization approaches to address its multiple aspects. The proposed techniques have a broad application range but are aimed at applications in the field of High Performance Computing (HPC) systems. Devising a power-aware HPC job dispatcher is a complex, where contrasting goals must be satisfied. Furthermore, the online nature of the problem request that solutions must be computed in real time respecting stringent limits. This aspect historically discouraged the usage of exact methods and favouring instead the adoption of heuristic techniques. The application of optimization approaches to the dispatching task is still an unexplored area of research and can drastically improve the performance of HPC systems. In this work we tackle the job dispatching problem on a real HPC machine, the Eurora supercomputer hosted at the Cineca research center, Bologna. We propose a Constraint Programming (CP) model that outperforms the dispatching software currently in use. An essential element to take power-aware decisions during the job dispatching phase is the possibility to estimate jobs power consumptions before their execution. To this end, we applied Machine Learning techniques to create a prediction model that was trained and tested on the Euora supercomputer, showing a great prediction accuracy. Then we finally develop a power-aware solution, considering the same target machine, and we devise different approaches to solve the dispatching problem while curtailing the power consumption of the whole system under a given threshold. We proposed a heuristic technique and a CP/heuristic hybrid method, both able to solve practical size instances and outperform the current state-of-the-art techniques