ElasticTree: Saving Energy
in Data Center Networks
許倫愷
2013/5/28
About the paper
Brandon Heller, Srini Seetharaman, Priya Mahadevan,
Yiannis Yiakoumis, Puneet Sharma, Sujata Banerjee,
Nick McKeown
NSDI’10 (USENIX conference on Networked systems
design and implementation)
Citation: 174
16 pages
Outline
The big picture
Introduction
ElasticTree system
Analysis
Conclusion
Outline
The big picture
Introduction
ElasticTree system
Analysis
Conclusion
The motivation
The motivation
Very inefficient!!
Desired
Why wasting power
Provisioning for peak
Time varying traffic demands
Low efficiency at low loads
The goal of ElasticTree
The approach…
Turn off unneeded links and switches
The challenge
Performance
Fault tolerance
Scalability
Outline
The big picture
Introduction
ElasticTree system
Analysis
Conclusion
Introduction
What is ElasticTree:
ElasticTree is a system for dynamically adapting the
energy consumption of a data center network
•
What does it do:
Finding minimum-power network subsets across a
range of traffic patterns
Trade-off:
energy efficiency, performance and robustness
Introduction
Data center network
(Traditional) 2N Tree:
One failure can cut the effective bisection bandwidth
in half; two failures can disconnect servers
Data center network
Fat tree: SIGCOMM 2008, A Scalable, Commodity
Data Center Network Architecture
Data center network
provision for peak workload
Traffic varies daily, weekly, monthly, and yearly.
Energy Proportionality
The strategy: turn off the links and switches that we
don’t need
Outline
The big picture
Introduction
ElasticTree system
Analysis
Conclusion
ElasticTree
ElasticTree is a system for dynamically adapting the
energy consumption of a data center network
ElasticTree
If 0.2 Gbps of traffic per host ,1 Gbps link…
ElasticTree
13/20 switches and 28/48 links stay active
ElasticTree reduces network power by 38%
0.8
0.4
0.2
ElasticTree
The optimizer: find the minimum- power network
subset which satisfies current traffic conditions
Optimizer
As traffic conditions change, the optimizer
continuously re-computes the optimal network subset
3 approaches:
Formal Model , Greedy Bin-Packing , Topology-aware
Heuristic
Optimizer comparison
Formal model
Finding the optimal flow assignment alone is an NPcomplete problem for integer flows.
Derived from standard multi-commodity flow (MCF)
problem
The model outputs a subset of the original topology,
plus the routes taken by each flow to satisfy the traffic
matrix
O(n^3.5+)
Greedy Bin-Packing
Strategy: choose the leftmost one with sufficient
capacity
O(n^2+)
1G link
Greedy Bin-Packing
1G link
Topo-aware Heuristic
1. does not compute the set of flow routes
2. assumes perfectly divisible flows
=> pack every link to full utilization and reduce TCP
bandwidth
=> starter subset
Decoupling power optimization from routing :
=> can be applied alongside any fat tree routing
algorithm
Topo-aware Heuristic
An edge switch doesn’t care which aggregation switches
are active, but instead, how many are active
Topo-aware Heuristic
Decoupling power optimization from routing
Optimizer comparison
Outline
The big picture
Introduction
ElasticTree system
Analysis
Conclusion
How to test
K = 6, fat tree
OpenFlow
Analysis
Traffic pattern:
Near: servers communicate only with other servers
through their edge switch
Far: servers communicate only with servers in other
pods
Analysis
Random demand:
Individual aggregation/core
switches turning on/off
Analysis
70% to outside, 30% inside DCN
Different traffic load
Analysis: redundancy
If only the MST is on
=> no redundancy => no fault tolerance
Analysis: redundancy
+MST: additive cost, multiplicative benefit
Analysis: latency
Ethernet overheads (preamble, inter-frame spacing, and the CRC)
cause the egress buffer to fill up
Packets either get dropped or significantly delayed
0.25
0.33
0.5
Need safety margin!!
Analysis: latency
Safety margin is the amount of capacity reserved at
every link by the optimizer
Traffic overload is the amount each host sends and
receives beyond the original traffic matrix
Trade-off between Energy and Performance
Outline
The big picture
Introduction
ElasticTree system
Analysis
Conclusion
Summary
Reference
The paper
The slide (by the author)
A youtube video (by the author, too)
http://www.youtube.com/watch?v=G2_D-CH4tQk
Questions
Thank you!