Self-Driving Cars—Driving Hyperscale in the Data Center

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[vc_row][vc_column][vc_column_text][mk_dropcaps style=”simple-style”]A[/mk_dropcaps]s companies accelerate their development efforts for self-driving cars and advanced driver assistance systems (ADAS), they are expanding the size of their vehicle test fleets. And with each vehicle generating 2 PB or more of data annually, these companies need a good data management plan to support this large and rapidly growing volume of data.

The following table highlights the magnitude of this challenge, based on a nominal 9 TB of data being generated daily, per car.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][mk_table]

  Terabytes Generated
Cars Daily Monthly Yearly 3 Years 5 Years
1 9 198 2,376 7,128 11,880
3 27 594 7,128 21,384 35,640
5 45 990 11,880 35,640 59,400
10 90 1,980 23,760 71,280 118,800
25 225 4,950 59,400 178,200 297,000
50 450 9,900 118,800 356,400 594,000
[/mk_table][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]As you can see, a relatively small test fleet of five cars would generate about 1 PB of data monthly. This volume of data requires a storage infrastructure that meets performance requirements at scale, and can easily and cost-effectively scale capacity. It’s necessary to retain this data for long periods of time to feed artificial intelligence (AI) engines and backtest new software algorithms, and also for regulatory purposes.

Without proper storage planning, development efforts can suffer due to storage costs consuming too much of a project’s budget, and storage performance slowing down as the volume of data under management exceeds system capabilities.

Quantum understands these challenges. Our automotive solutions are proven to deliver high performance at scale with easy—massive—scalability and integrated data protection to provide complete data management solutions at one-tenth the cost of alternatives.[/vc_column_text][/vc_column][/vc_row]

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