This document proposes a method called Elastic Tree to reduce network power consumption in data centers. Elastic Tree dynamically adjusts the set of active network elements like links and switches based on changing traffic loads to satisfy demand with minimum power. It consists of three modules: an optimizer to find the minimum power network subset, a routing module to choose paths, and a power control module to toggle network elements. Different optimization methods are explored, including a formal model, greedy bin-packing, and topology-aware heuristic to reduce network power consumption while meeting traffic needs. When applied to an e-commerce data center, Elastic Tree was able to significantly reduce power consumption in the network.

1. Elastic Tree: Saving Energy in
Data Center Networks
By
Abhishek Sutrave-107907204
Kishen Machamada-107916576

2. Introduction
• Networks basically are shared resources
connecting critical IT infrastructure, general
practice is to always leave them ON.
• Most efforts to reduce energy consumption in
Data Centers is focused on servers and cooling,
which account for about 70% of a data center’s
total power budget.
• This presentation focuses on reducing network
power consumption, which consumes 10-20% of
the total power.
-3 billion kWh in 2006[US] by networking elements.

4. Difference between the topologies
• In a typical DCN[2N tree]. One failure can cut the
effective bisection BW in half. While two failures can
disconnect servers.
• Richer mesh topologies like the fat-tree handle failures
more gracefully; with more components and more
paths, the effect of an individual component failure
becomes manageable.
• This can be used in improving energy efficiency, by
dynamically varying the no. of active network
elements.
• It can be thought as a control knob to tune between
energy efficiency, performance and fault tolerance.

5. Traffic collected from 292 servers hosting an E-com
application over a 5 days period .The traffic peaks
during the day and falls at night. Even though traffic
varies significantly with time, the associated switches
draw a constant power.

8. Energy Proportionality
• Today’s network elements are not energy
proportional
– Fixed overheads such as fans, switch chips, and
transceivers waste power at low loads.
• Maximum efficiency can be realized by a
combination of improved components and
improved management.
• Our strategy is simple:
– Turn off the links and switches that we don’t need to
keep available only as much networking capacity as
required.

9. ELASTIC TREE
• It is a network-wide power manager, which
dynamically adjusts the set of active network
elements-links and switches- to satisfy
changing data center traffic loads.
• It consists of three logical modules
– Optimizer
– Routing
– Power Control

10. Elastic Tree
Is to find
minimum
power
network
subset which Chooses path for all flows
satisfies
current traffic
conditions. Toggles the states of
-topology
-traffic matrix
-power model
of each switch
-desired fault
tolerance
properties

11. Optimizers
• Role-Is to find minimum power network subset
which satisfies current traffic conditions.
• There are three different methods for computing
a minimum power network subset:
– Formal Model
– Greedy-Bin Packing
– Topology-aware Heuristic
• Each method achieves different tradeoffs
between scalability and optimality.
• Methods can be further improved by considering
a data center’s traffic history

12. Formal Model
• Extension of the standard multi-commodity
flow (MCF) problem with additional
constraints which force flows to be assigned to
only active links and switches.
• The constraints include link capacity, flow
conservation and demand satisfaction.
• minimize Σ (Link + Switch Power)
• Optimization goal is to minimize the total
network power, while satisfying all constraints.

13. Formal Model
• MCF problem is NP-complete
• An instance of the MCF problem can easily be
reduced to the Formal Model problem (just
set the costs for each link and switch to be 0).
• So the Formal Model problem is also NP-
complete.
• Still scales well for networks with less than
1000 nodes, and supports arbitrary
topologies.

14. Greedy Bin-Packing
• Evaluates possible flow paths from left to
right. The flow is assigned to the first path
with sufficient capacity.
• Repeated for all flows.
• Solutions within a bound of optimal are not
guaranteed, but in practice, high quality
subsets result.

30. Conclusion
• 3 Algorithms – Model, Greedy and Heuristic
have been examined.
• Applied the above algorithms on E-commerce
data center[Google data center], and found
that power consumption can be reduced.

31. Questions ?

32. THANK
YOU