Skip to content

Summer Camp 2011 Measurement Brainstorm

Stephen Woodrow edited this page Feb 27, 2012 · 1 revision

BISmark Summer Camp 2011/Measurement Brainstorm

  • Measurements
    • Wireless performance in the home
    • Streaming performance
    • Low-overhead bandwidth measurements
    • Passive measurements (what kinds, and how?)
    • Network neutrality / application performance monitoring
    • Wireless mesh networking (e.g., getting bismark running on mesh potatoes, area deployments)

Inside the Home

  • Wireless performance
    • Throughput to wireless devices in the home
    • Other wireless networks that exist in the "neighborhood" and how much traffic is being sent on these networks
  • User behavior
    • What does "normal" user behavior look like?
  • Application behavior
    • Automated vs. human-generated traffic (What does "ambient" traffic look like?)
    • How much traffic is inside the home (internal: mDNS, bonjour, ARP, internal application traffic) vs. external?
    • How much traffic is cacheable? (as a follow-up: how would caching any or all of this content improve performance?)
    • How much traffic belongs to different applications? (e.g., is it true that 20% of traffic at peak time is NetFlix?)
  • Device inventory and characterization/fingerprinting, including home automation devices
  • Do people modify the router itself? (configuration changes?)
  • What other information do applications leak? (monitoring set-top boxes for data leaks, etc.)
    • How much sensitive information is leaked in plaintext?
  • Does the use of a particular application affect the performance of others (e.g., on the access link)?
  • Security
    • "HomeIDS" - perform active and passive measurements to detect anomalies (e.g., compromised hosts) inside the home
    • Splitting snort/bro: generate signatures, but perform signature matching "in the cloud". Note: these signatures might have to be based on flow statistics if they are exported from the box.
  • Factoring/locating bottlenecks: host, home network, access link, peering, server

On the Access Link

  • Application performance (e.g., Skype, Netflix)
    • Passive monitoring
    • Active measurements (of statistics that are relevant to the application)
  • Effects of NAT on performance
  • Effects of split TCP on performance (Jacopo is working on this)
  • Are the ISP policies static or dynamic? Within the access network, where is each policy applied?
    • Time-based?
    • Based on user behavior? Do the ISPs build profiles of users and treat each user's traffic differently?
    • Based on destination? (e.g., do users behind the same head end experience different performance/policies than those in different locations?)

Topology and Applications

  • What does the topology of the access network look like (e.g., by tracerouting from Bismark node to Bismark node)?
    • Mapping out sup-IP topologies in a region
    • Which access ISPs peer with one another? Does peering vary by region?
  • CDN Performance (Srikanth and Walter are working on this)
    • What types of tricks do CDNs play in mapping clients to servers? What does DNS resolution look like from different geographies and providers?
    • How does a CDN's peering affect the performance that users in different access ISPs see?
  • Does fetching content from a peer yield better or worse performance than fetching from an origin?
  • Quantifying, measuring, and monitoring "information bubbles" (see below) (region-specific content, filtering, etc.).
    • action item: add some wgets to bismark-active for different Web servers, somehow do "diffs", upload the results to a portal


  • Combination of heavyweight and lightweight bandwidth tests to achieve more continuous measurements?
  • Could the same set of measurements be applied on a mobile device?

Next Steps and Other Ideas

  • Get TIE integrated with Bismark
  • Identifying human-generated vs. automated traffic
  • Identifying internal vs. external
    • Kandula: Expose (learning communication patterns by grouping related flows)

Information/Filter Bubbles


This is a project for understanding and quantifying information filter bubbles (, using a distributed measurement infrastructure like Bismark.


Filter bubbles focus on the impact of web personalization based on regional and personal characteristics. A user is exposed to things that he will probably like and be interested to. This creates a virtual environment which looks very familiar and friendly, removing "irrelevant" and/or "unpopular" content, where relevance and popularity is based on a rich vector of personal, regional, technical characteristics, and limits the breadth of user's experience.

The argument so far is on a relatively high-level. A more systematic and detailed study could help us understand and quantify how these reflects to the experience users perceive.

Using a distributed measurement platform like Bismark we can generate identical search requests from different regions to study such behaviors.

Design Ideas

  • Home Gateways act as clients. They issue search requests in multiple search engines (Bing, Google, Yahoo, - anything we could do with Facebook?), and report the results back to a central server.
  • Requests should not be hardcoded in the image. We don't expect a query to have time variance, even if it has we can't predict it. The clients can fetch a queriy from the server every day and then issue the requests to the search engines (aka the "tip of the day")

Other Notes

  • What sort of requests would reveal differences? Should it be up-to-date information like news, or static content?
  • Get personalized behavior seems more difficult. It requires fake profiles, but how do you build history on this?
  • What other things can we detect? Censorship, ...?
  • Another way to do this would be using Tor and selecting the exit node.
Something went wrong with that request. Please try again.