1. Optimizing Edge-Cloud IoT Applications for Performance and Cost
Peter Bodik
Microsoft Research

2. Can we make IoT scalable and cheap?
Many IoT scenarios that we don’t know how to solve/enable
need better sensors, cheaper connectivity, …
However, many other verticals solved
Problem: simplify development, scale up, and make cheap
process
store
visualize
alert

3. Complex deployment of simple IoT apps
Challenges:
deployment
monitoring
cost estimates
placement
resource allocation
Complex deployment of simple IoT apps
application:
devices
stream analytics
Azure function DB
edge
cloud
devices
stream analytics
Azure function DB
event
hub
IoT
devices
stream analytics
IoT
hub
Azure function DB
pass-thru
function

4. Many ways to implement/optimize the app
Communication:
batching, encoding, compression, approximation, …
Processing:
Azure Stream Analytics, Function, SQL stored procedure, custom VM, …
(Time series data) storage:
SQL server, Cosmos DB, Time Series Insights, Tables, Blobs, …
Manual and complex optimization

5. End-to-end management
Declarative Spec
Deployment
Verify
Compile
Deploy
End-to-end management
on Edge-Cloud
Monitoring
Perf.
Cost
Bottlenecks
Placement
& allocation
Time-series
Storage
Communication
Optimization …