1. Online parameter optimization for elastic data stream processing
Heinze et.al
Scribed by Tao Feng 02/18/2016

2. Recap
Introduce an Elastic Scaling Streaming Process that achieves an efficient balance between Monetary cost and Quality of Service (latency)
Threshold based elastic scaling
Runtime and automated operator placement
On-line parameter optimization
Parameter search scheme
Cost function
Online profiler
When to trigger the optimization
Size of utilization history

3. Evaluation Trade-off between Monetary cost and Quality of Service
Manually tuned parameter
Optimized parameter (contribution of this paper)
Parameter learned from Reinforcement Learning

4. Pros & Cons
Pros
Carefully and extensively comparison the trade-offs of the three baseline using different parameter values
Simple architecture and intuitive algorithm for elastic scaling and parameter search
The framework can be used to other type of elastic scaling stream processing
The prototype scales linearly with number of queries and window size …

5. Pros & Cons Cons Cost function seems self-provable
MAX is predefined
Overloading part could be further improved or with more explanation
The evaluation is based authors’ own system
What the situation will be on a system used in the industry (Storm)
Didn’t talk about load burst when that is much larger than regular workload
User still need to define the latency threshold
Reinforcement Learning
Black box

6. Comments & Questions
Why is monetary cost the only cost considered? We could also focus on energy as cost etc.
What advantage did we get from making the upper and lower threshold granularities so small (which led to several thousand parameter configurations)?
Besides  CPU, network, and memory consumption, are there any other possible resources that also should be considered?
Why can’t memory and network overload be parametrized?