Energy Aware Consolidation
for Cloud Computing
Srikanaiah, Kansal, Zhao
Usenix HotPower 2008
Power & Consolidation Issues
 High power consumption even at low load
(at 10% CPU util, 50% of peak power
consumed)
 Consolidation is not just bin-packing
– Packing too much might increase “energy used
per unit service provided”
– Consolidation can lead to performance
degradation
– There exists an “optimal” performance vs
energy operating point
Experiments for Understanding
Consolidation
Experimental Set up
Understanding Consolidation:
Experimental Results
•Started with
app using 10%
CPU, 10% disk
•Added
workloads with
varying CPUdisk utils
•Numbers
show CPU-disk
utilization mix
•Point made:
consolidation
results in
performance
degradation
Energy Consumption per transaction
U-shaped curve:
•At low utilizations idle
power is not
“amortized effectively”
•At high utilizations,
energy consumption
increases, but
throughput degrades
hence per tran energy
consumption
increases
•Observation: There is
an optimal
combination of CPU
and disk utils for this
setup (70% CPU,
50% disk)
Consolidation Problem
 Why not straightforward multi-dimensional
bin-packing
– Performance degradation: resource utilizations
are additive, but performance measures are not
modeled at all in bin-packing. Minimizing
number of bins not equal to minimizing energy
(implied to mean energy per transaction)
– Power variation: even if minimum number of
severs is used, the allocation of workloads will
result in varying power usage
Consolidation Algorithm
 Algorithm has to do following
– Workload “arrives” to host cluster
– Arriving workload has known CPU-disk
utilization
– Arriving workload has to be “assigned” to a host
Consolidation Algorithm
1.
Optimal point for each host should be known (details not
specified!!)

Assumes that optimal point is a host characteristic - not
dependent on application
For a particular allocation, the “Euclidean distance” from
the optimal point is calculated
Pick allocation which maximizes sum of such Euclidiean
distances of each server
2.
3.

In authors’ words “This heuristic is based on the intuition that we
can use both dimensions of a bin to the fullest (where “full” is
defined as the optimal utilization point) after the current allocation
is done, if we are left with maximum empty space in each
dimension after the allocation.”
Consolidation Heuristic
Evaluation
…Evaluation