Cloud Volumes Service for Google Cloud

EDA Workload – Latency vs. Operations per Second Rate

The graphics to the right show performance of a synthetic EDA workload in NetApp Cloud Volumes Service for Google Cloud. Using 36 SLES15 n1 standard-8 GCE instances, the workload achieved:

• 190,000 IOPS (3.3GiB/s throughput) at 2ms latency,
• 240,000 IOPS (4.3GiB/s throughput) at 4ms latency,
• 264,000 IOPS (4.7GiB/s throughput) at 7ms latency.

Layout wise, the test generated 5.52 million files spread across 552K directories. The complete workload is a mixture of concurrently running frontend (verification phase) and backend workloads (tapeout phase) which represents the typical behavior of a mixture of EDA type applications.

The frontend workload represents frontend processing and as such is metadata intensive— think file stat and access calls—by majority metadata; this phase also includes a mixture of both sequential and random read and write operations. Though the metadata operations are effectively without size, the read and write operations range between sub 1K and 16K with the majority of reads between 4K and 16K and most of the writes 4K or less.

The backend workload, on the other hand, represents I/O patterns typical for the tapeout phase of chip design. It is this phase that produces the final output files from files already present on disk. Unlike the frontend phase, this workload is entirely comprised of sequential read and write operations, and a mixture of 32K and 64K OP size.

Graphically speaking, most of the throughput shown in the graph comes from the sequential backend workload and the I/O from the small random frontend phases–both of which happened in parallel.

The recently released nconnect mount option and NFSv3 was used in addition to the default mount options. Expect official NetApp support for the nconnect mount option and nfsv3 early 2020 and support for the same with nfsv4 early 2020.

MySQL Workload – Latency Relative to Throughput

For load testing MySQL in Cloud Volumes Service for Google Cloud, we selected an industry standard OLTP benchmarking tool and continued increasing user count until throughput reached flatline. By design, OLTP workload generators heavily stress the compute and concurrency limitations of the database engine–stressing the storage is not the objective. That said, the tool used, rather than the storage, was the limiting factor in the graphs.

The metrics in the graph, which maps out benchmark data for MySQL, are taken from nfsiostat on the database server, and, as such represent the perspective of the NFS client. We observed maximum throughput of 500MiB/s.

For this test, the following configuration was used:

• Instance type: n1-highmem-32
• MySQL Version: 10.3.2
• Linux Version: Redhat Enterprise Linux 7.6
• Workload Distribution to storage: 70/30 read/write with 4KiB operation database page size*
• Volume Count: database volume (8TiB Extreme), 1 log volume (1TiB Standard)
• Allocated Storage Bandwidth: database volume 1024MiB/s, log volume 16MiB/s
• Database Size: 1.25TiB