Abstract
Recent energy-related hardware developments trend towards offering more and more configuration opportunities for the software to control its own energy consumption. Existing research so far mainly focused on finding the most energy-efficient hardware configuration for specific operators or entire queries in the database domain. However, the configuration opportunities influence the energy consumption as well as the processing performance. Thus, treating energy efficiency and performance as independent optimization goals offers a lot of drawbacks. To overcome these drawbacks, we introduce a model based approach in this paper which enables us to select a hardware configuration offering the best energy efficiency for a requested performance. Our model is a work-energy-profile being a set of useful work done during a fixed time span and the required energy for this work for all possible hardware configurations. The models are determined using a well-defined benchmark concept. Moreover, we apply our approach on in-memory databases and present the work-energy profiles for a heterogeneous multiprocessor.
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Acknowledgments
This work is partly funded by the German Research Foundation in the Collaborative Research Center 912 “Highly Adaptive Energy-Efficient Computing” and within the Cluster of Excellence “Center for Advancing Electronics Dresden” (Orchestration Path).
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Ungethüm, A., Kissinger, T., Habich, D., Lehner, W. (2017). Work-Energy Profiles: General Approach and In-Memory Database Application. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking. Traditional - Big Data - Internet of Things. TPCTC 2016. Lecture Notes in Computer Science(), vol 10080. Springer, Cham. https://doi.org/10.1007/978-3-319-54334-5_10
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DOI: https://doi.org/10.1007/978-3-319-54334-5_10
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