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1. Prior performance benchmarking and assessment work
   1. Three of the performance areas highlighted previously have not yet been sufficiently tested
      1. total data size
      2. ingest rate
      3. LDP/SPARQL Update performance (per Hydra practice)
   2. Clustering
      1. Primary use case seems to be high availability
      2. What increased scale clustering affords is unclear, partly because we haven't yet fully probed how far a single instance scales (as a baseline)
   3. Would be good to know how performance (response time) changes:
      1. as a file size increases
      2. as # of files increases
      3. as # of resources/containers increases
   4. We endeavor here to establish a process & baselines for the more isolated tests (1a in the agenda) so that we can make progress on "real-world"-type tests (1b in the agenda)
      1. How should we treat other axes?
         1. authorization
         2. transactions
         3. concurrency
         4. versioning
      2. Decision: Defer at first, then examine later once the process & baselines are clear.
      3. Decision: Process should include writing a number of objects/files into the repository (testing the speed of the writes), and every
         1. Every so often (# of writes),
         testing
         1. test a suite of operations (gets, deletes) to see how the speed of those change
         .
         2. That way we test reading, writing, and a number of other operations as overall repository size increases.