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Power-aware resource allocation in high-end systems via online simulation

Traditionally, scheduling in high-end parallel systems focuses on how to minimize the average job waiting time and on how to maximize the overall system utilization. Despite the development of scheduling strategies that aim at maximizing system utilization, parallel supercomputing traces that span long time periods indicate that such systems are mostly underutilized. Much of the time there is simply not enough load to keep the system fully utilized, although time periods do exist where system utilization levels peak at nearly 95%. In this paper, we propose a new family of scheduling policies that aims at minimizing power consumption and cooling costs by selectively choosing to power down (or put in "sleep" mode) parts of the system during periods of low load. Our goal is the development of a scheduling mechanism that adaptively adjusts the number of processors to the offered load while meeting predefined service-level agreements (SLAs). This scheduling mechanism uses online simulation, i.e., lightweight simulation modules that can execute while the system and its scheduler are in operation, and can guide resource provisioning in parallel systems. Detailed experimentation using traces from the Parallel Workloads Archive indicates that the proposed online mechanism is a viable alternative to conserve energy while meeting performance-based SLAs.