TL;DR
The paper discusses how changing computing costs influence the design of Internet-scale distributed systems, emphasizing data locality to minimize expensive network traffic.
Contribution
It highlights the shift in computing economics and advocates for data-centric architectures to optimize performance and cost.
Findings
Network traffic is now as costly as database access.
Data locality reduces overall system costs.
Designing for data proximity improves efficiency.
Abstract
Computing economics are changing. Today there is rough price parity between (1) one database access, (2) ten bytes of network traffic, (3) 100,000 instructions, (4) 10 bytes of disk storage, and (5) a megabyte of disk bandwidth. This has implications for how one structures Internet-scale distributed computing: one puts computing as close to the data as possible in order to avoid expensive network traffic.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDistributed and Parallel Computing Systems