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Distributed snapshots: determining global states of distributed systems
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The constant demand of raw performance in high-performance computing (HPC) often leads to over-provisioning in high-performance systems which in turn can result in a colossal energy waste due to workload/application variation over time. Proposing energy efficient solutions in the context of large-scale HPC is a real, unavoidable challenge. This article explores two alternative approaches (with or without knowledge of applications and services) dealing with the same goal: reducing the energy usage of large-scale infrastructures which support HPC applications. This article describes the first approach, with knowledge of applications and services, which enables users to choose the less consuming implementation of services. Based on the energy consumption estimation of the different implementations (protocols) for each service, this approach is validated on the case of fault tolerance service in HPC. The 'without knowledge' approach allows some intelligent framework to observe the life of HPC systems and proposes some energy reduction schemes. This framework automatically estimates the energy consumption of the HPC system in order to apply power saving schemes. Both approaches are experimentally evaluated and analysed in terms of energy efficiency.