1. Sensing the Datacenter
Heterogeneous Sensor network for Datacenter Workload and power management
Jorge Ortiz
CS Architectures for Internet Datacenters
October 10, 2007

2. Datacenters Consume Lots of Power
Datacenter power consumption increasing
Environmental Protection Agency (EPA) report shows power-consumption has doubled in last 5 years
1.5% of total U.S. Electricity Consumption in 2006
Projected to double again in next 5 years
Datacenter under-provisioned for saving power
Sensing and control separate from load balancer
Protocols and applications are energy unaware
What I propose:
Couple the sensing and control with the load balancer/tasking decisions
Dynamic Adjustment
Graceful workload adjustment for saving power

3. Sensors Facilitate Dynamic Energy Accounting
Include power-related input into the protocol and management loop
Make use of equipment sensors already available to gather information about power consumption
Power meters to attach to server/racks
On-board temperature sensors
In-band network monitors
Wireless sensor network technology to include out-of-band monitoring infrastructures
Single on-board sensors sometimes give wacky readings
Array of sensors adds redundancy and improves accuracy
Wireless motes ease deployment and data collection

4. Loose Ends Datacenter-scale workloads unavailable
Monitoring machine room activity not at same scale as internet datacenter, but it’s a good start
Power metering equipment needed
A couple of one-outlet monitors already in use
Access to 420A (Soda Hall Machine room) or the RadLab Machine room
Direct access to specific machines in (either) machine room

5. Semester Plan
Provision the Soda Hall machine room (420A) or the RadLab machine room with wireless sensors (temperature, humidity, etc.)
Attach power meters to a set of servers in the machine room
Setup process (top), network (netstat), and disk monitors (iostat) to determine distribution of machine activity
Analyze gathered data
Formulate a model that relates temperature and power consumption
Analyze the relationship between component utilization and ambient temperature
Machine-learning techniques for formulation of predictive models for graceful workload degradation