exploiting latency bounds for energy efficient load balancing
DESCRIPTION
These slides are taken from a research paper the PSL group wrote while under the direction of Dr. Vijay Garg at the Universtiy of Texas at Austin. The abstract is provided below. In this paper we explore exploitation of latency bounds in order to gain energy efficiency in load balancing applications. We are proposing an energy aware job scheduler that uses vary-on, vary-off features in order to maximize time spent at peak utilization, while maintaining bounded latency. Computing resources will either be at load saturation(highest work per joule) or off. The premise being that servers are most efficient at peak utilization, measured in terms of energy per calculation. We explore the efficiency gains achieved through this approach and compare our results to other methods.TRANSCRIPT
Exploiting latency bounds for energy efficient load balancing
Cruz Monrreal, Daniel Jones, Michael May and Mohit Taneja
The University of Texas at Austin
Overview
● Problem● State of server power (lack of power
proportionality)● Inspiration● Current Solutions● Assumptions● Our Solution● Comparisons of results● Limitations of our solutions● Future prospects● Q & A
Description of Problem
Current server implementations are power inefficient during low load hours.
Many requests do not need to be serviced as fast as possible thus have an acceptable stall period.
System Power Consumption
Source - http://static.usenix.org/events/hotpower08/tech/full_papers/rivoire/rivoire_html/
System Power ConsumptionTable 1 - Number of Cores utilized to Power Usage
# of Cores Power (W)
0 0
1 270
2 300
3 320
4 330
Inspiration
Inspiration (Cont)
Assumptions - Model4 core machine
3 servers total
1 job saturates a core
Instant on/off
No background tasks
Assumptions - Model (Cont)Load generator simulates sending variable time jobs (service requests) to load balancer.
Load Scheduler distributes jobs to servers.
Server simulates running job by sleeping the given time.
Server sends number of cores running back to load balancer with its own timestamp.
Current Solutions
No Power management (Round Robin)
Basic Power management (Round Robin)
Advanced Power management
Current Solutions - No Power Management
Load Scheduler uniformly schedules jobs to each server in sequence
Problems:Lots of time spent in idleFew cores used = low efficiency
Current Solutions - Round Robin w/o Power Management
Load Scheduler uniformly schedules jobs to each server in sequenceTurns off unused machines
Problems:Few cores used = low efficiency
Current Solutions - Round Robin w/ Server Toggling
Load Scheduler uniformly schedules jobs to each server by sending 4 jobs at a time sequentially. Turns off unused servers.
Problems:Does not fully utilize latency
Overview of Solution
If any servers are running but not full, the load balancer will send a job to the server with the most jobs running.
If all servers are full on/off than the load balancer will wait the given stall time until sending a job to an off server (thus turning it on).
Comparisons
Run at average load = 25%,50%,75%,100%
Vary job time around average load job time.
Example: 25% load timeJob time = 8-12 millisecondsStall time = 500 millisecondsTime between jobs = 2-8 milliseconds
Conclusion
Limitations
With large core counts advantages start to diminish
At 100% no gains
Future Prospects
Storage systems (SANs)Latency exploitation for networks
Q&A
Resources
[1] http://web.eecs.umich.edu/~twenisch/papers/asplos12.pdf
[2] http://static.usenix.org/events/hotpower08/tech/full_papers/rivoire/rivoire_html/