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Architectural Complexity

Architectural Complexity. Henning Schulzrinne Dept. of Computer Science Columbia University. Overview. Deployment problems Layer creep Simple and universal wins Scaling in human terms Cross-cutting concerns, e.g., CPU vs. human cycles

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Architectural Complexity

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  1. Architectural Complexity Henning Schulzrinne Dept. of Computer Science Columbia University

  2. Overview • Deployment problems • Layer creep • Simple and universal wins • Scaling in human terms • Cross-cutting concerns, e.g., • CPU vs. human cycles • we optimize the $100 component, not the $100/hour labor • introspection • graceful upgrades • no policy magic

  3. Simple wins (mostly) • Examples: • Ethernet vs. all other L2 technologies • HTTP vs. HTTPng and all the other hypertext attempts • SMTP vs. X.400 • SDP vs. SDPng • TLS vs. IPsec (simpler to re-use) • no QoS & MPLS vs. RSVP • DNS-SD (“Bonjour”) vs. SLP • SIP vs. H.323 (but conversely: SIP vs. Jabber, SIP vs. Asterisk) • Efficiency is not • BitTorrent, P2P searching, RSS, …

  4. Cause of death for the next big thing

  5. Measuring complexity • Traditional O(.) metrics rarely helpful • except maybe for routing protocols • Focus on parsing, messaging complexity • marginally helpful, but no engineering metrics for trade-offs • No protocol engineering discipline, lacking • guidelines for design • learning from failures • we have plenty to choose from – but hard to look at our own (communal) failures • re-usable components • components not designed for plug-and-play • “we don’t do APIs”  we don’t worry about whether a simple API can be written that can be taught in Networking 101 • Separate worlds: • most of the new protocols in the real world based on WS • IETF stuck in bubble of one-off protocols  more fun! • re-use considered a disadvantage • insular protocols that have local cult following (BEEP)

  6. Measuring complexity • Conceptual complexity • can I explain the protocol operation in one class? • e.g., PIM-SM, MADCAP, OSPF • Observable vs. hidden • one side, without “god box” • hidden state and actions increase information complexity • unknown variables can have any state • Number of system interfaces • see 3GPP

  7. Possible Complexity Metrics • new code needed (vs. reuse)  less likely to be buggy or have buffer overflows • e.g., new text format almost the same • numerous binary formats • security components • new identities and identifiers needed • number of configurable options + parameters • must be configured & can be configured (with interop impact) • discoverable vs. manual/unspecified • SIP experience: things that shouldn’t be configurable will be • RED experience: parameter robustness • mute programmer interop test: two implementations, no side channel • number of “left-to-local policy” • DSCP confusion • start-up latency (“protocol boot time”) • IPv4 DAD, IGMP

  8. Time for a new protocol stack? • Now: add x.5 sublayers and overlay • HIP, MPLS, TLS, … • Doesn’t tell us what we could/should do • or where functionality belongs • use of upper layers to help lower layers (security associations, authorization) • what is innate complexity and what is entropy? • Examples: • Applications: do we need ftp, SMTP, IMAP, POP, SIP, RTSP, HTTP, p2p protocols? • Network: can we reduce complexity by integrating functionality or re-assigning it? • e.g., should e2e security focus on transport layer rather than network layer?

  9. Conclusion • Traditional protocol engineering • “must do congestion control” • “must do security” • New module engineering • re-usable components • most protocol design will be done by domain experts (cf. PHP vs. C++) • out-of-the-box experience • What would a clean-room design look like?

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