Few Web services require as much computation per request as search engines. On average, a single query on Google reads hundreds of megabytes of data and consumes tens of billions of CPU cycles. Supporting a peak request stream of thousands of queries per second requires an infrastructure comparable in size to that of the largest supercomputer installations. Combining more than 15,000 commodity-class PCs with fault-tolerant software creates a solution that is more cost-effective than a comparable system built out of a smaller number of high-end servers.
Here we present the architecture of the Google cluster, and discuss the most important factors that influence its design: energy efficiency and price-performance ratio. Energy efficiency is key at our scale of operation, as power consumption and cooling issues become significant operational factors, taxing the limits of available data center power densities.
Our application affords easy parallelization: Different queries can run on different processors, and the overall index is partitioned so that a single query can use multiple processors. Consequently, peak processor performance is less important than its price/performance. As such, Google is an example of a throughput-oriented workload, and should benefit from processor architectures that offer more on-chip parallelism, such as simultaneous multithreading or on-chip multiprocessors.