Building Fuel Powered Supercomputing Data Center at Low Cost
Proceedings of the 29th ACM on International Conference on Supercomputing - ICS '15
Distributed power generations that fed with various economical clean fuels are emerging as promising power supplies for extremescale computing systems. Recent years have witnessed a growing adoption of these non-conventional power supplies in data center designs due to the heightening demand for reducing IT carbon footprint and server energy cost. However, the benefits of such a fuel powered data center are often severely compromised by its high initial capital cost (CapEx). This is because
... pilot designs today either rely on expensive advanced generators or employ lowperformance generators with costly standby power backup. In this study we exploit heterogeneous generation to reduce the cost of data center powered by fuel. We show that different types of power supplies, if used together, can greatly improve the costeffectiveness of self-generation but introduce a new layer of design complexity and raise an important question of how to dispatch computing tasks on heterogeneous power supplies. Specifically, due to the non-ideal output power response speed of heterogeneous generators, servers may incur serious power budget deficiencies when dispatching large amount of jobs. We refer to this phenomenon as power lagging, which jeopardizes system reliability and are not economical to be handled by costly power backup systems. To overcome this barrier, we propose μBatch, an agile load dispatching scheme that eliminate power lagging at the system/software level. Other than dispatch computing tasks in bulk without considering power system behaviors, μBatch intelligently splits job queue into small sets and incrementally schedule jobs based on the power ramping rate constraints and total power budget constraints. Using realistic HPC datacenter load traces, we demonstrate that μBatch enables supercomputers to smoothly operate on heterogeneous power. Our design helps data center operators save over 80% cost while maintaining the desired workload performance.