CompSci 296.2 Self-Managing Systems Shivnath Babu In this talk my aims are: - to give an overview of the work that we have been doing at Stanford on adaptive processing in the stream system and what we are doing now [past and current work] - facilitate some interesting discussion. Hope the title is provoking enough I will not be comparing our system with eddies explicitly, although I am doing some work in that respect with DeWitt and one of his students.

Reminder Today: Progress talk <= 5 minutes Send me slides + 2-page writeup by next Tuesday Thursday: Speaker from Cisco Next Tuesday: Speaker from IBM Tivoli

Adaptive Metric Attribution Recap Metric attribution TANs Balanced accuracy Log-likelihood difference Experimental results

Ensembles What are the different approaches they test in the paper? Pros and cons Are they depending on “past predicts future”? Ensemble-based algorithm

Experiments Workloads Table 2 Figure 3(a) Learning surfaces Rationale behind the workload selection? Table 2 Figure 3(a) Learning surfaces

Discussion Strong points? Weak points? Root-cause detection Vs. metric attribution Strange behavior with BURST workload What are wrong with single models? Does it make sense to disqualify models? Bullet 3 on Page 3 Redundancy of metrics in Ensembles