Title: Towards Adaptive Resilience in High Performance Computing,
Abstract: Failure rates in high performance computers rapidly increase due to the
growth in system size and complexity. Hence, failures became the norm rather
than the exception. Different approaches on high performance computing (HPC)
systems have been introduced, to prevent failures (e. g., redundancy) or at
least minimize their impacts (e. g., checkpoint and restart). In most cases,
when these approaches are employed to increase the resilience of certain parts
of a system, energy consumption rapidly increases, or performance significantly
degrades. To address this challenge, we propose on-demand resilience as an
approach to achieve adaptive resilience in HPC systems. In this work, the HPC
system is considered in its entirety and resilience mechanisms such as
checkpointing, isolation, and migration, are activated on-demand. Using the
proposed approach, the unavoidable increase in total energy consumption and
system performance degradation is decreased compared to the typical
checkpoint/restart and redundant resilience mechanisms. Our work aims to
mitigate a large number of failures occurring at various layers in the system,
to prevent their propagation, and to minimize their impact, all of this in an
energy-saving manner. In the case of failures that are estimated to occur but
cannot be mitigated using the proposed on-demand resilience approach, the
system administrators will be notified in view of performing further
investigations into the causes of these failures and their impacts.