ElasticTree: Saving Energy in Data Center Networks

ElasticTree: Saving Energy in Data Center Networks
ElasticTree: Saving Energy in Data Center Networks
Brandon Heller⋆ , Srini Seetharaman† , Priya Mahadevan⋄ ,
Yiannis Yiakoumis⋆ , Puneet Sharma⋄ , Sujata Banerjee⋄ , Nick McKeown⋆
Stanford University, Palo Alto, CA USA
Deutsche Telekom R&D Lab, Los Altos, CA USA
Hewlett-Packard Labs, Palo Alto, CA USA
power: the network [9]. The total power consumed
by networking elements in data centers in 2006 in
the U.S. alone was 3 billion kWh and rising [7]; our
goal is to significantly reduce this rapidly growing
energy cost.
Networks are a shared resource connecting critical IT infrastructure, and the general practice is to always leave
them on. Yet, meaningful energy savings can result from
improving a network’s ability to scale up and down, as
traffic demands ebb and flow. We present ElasticTree, a
network-wide power1 manager, which dynamically adjusts the set of active network elements — links and
switches — to satisfy changing data center traffic loads.
We first compare multiple strategies for finding
minimum-power network subsets across a range of traffic patterns. We implement and analyze ElasticTree
on a prototype testbed built with production OpenFlow
switches from three network vendors. Further, we examine the trade-offs between energy efficiency, performance and robustness, with real traces from a production e-commerce website. Our results demonstrate that
for data center workloads, ElasticTree can save up to
50% of network energy, while maintaining the ability to
handle traffic surges. Our fast heuristic for computing
network subsets enables ElasticTree to scale to data centers containing thousands of nodes. We finish by showing how a network admin might configure ElasticTree to
satisfy their needs for performance and fault tolerance,
while minimizing their network power bill.
1.1 Data Center Networks
As services scale beyond ten thousand servers,
inflexibility and insufficient bisection bandwidth
have prompted researchers to explore alternatives
to the traditional 2N tree topology (shown in Figure 1(a)) [1] with designs such as VL2 [10], PortLand [24], DCell [16], and BCube [15]. The resulting networks look more like a mesh than a tree.
One such example, the fat tree [1]2 , seen in Figure
1(b), is built from a large number of richly connected
switches, and can support any communication pattern (i.e. full bisection bandwidth). Traffic from
lower layers is spread across the core, using multipath routing, valiant load balancing, or a number of
other techniques.
In a 2N tree, one failure can cut the effective bisection bandwidth in half, while two failures can disconnect servers. Richer, mesh-like topologies handle
failures more gracefully; with more components and
more paths, the effect of any individual component
failure becomes manageable. This property can also
help improve energy efficiency. In fact, dynamically
varying the number of active (powered on) network
elements provides a control knob to tune between
energy efficiency, performance, and fault tolerance,
which we explore in the rest of this paper.
Data centers aim to provide reliable and scalable
computing infrastructure for massive Internet services. To achieve these properties, they consume
huge amounts of energy, and the resulting operational costs have spurred interest in improving their
efficiency. Most efforts have focused on servers and
cooling, which account for about 70% of a data center’s total power budget. Improvements include better components (low-power CPUs [12], more efficient power supplies and water-cooling) as well as
better software (tickless kernel, virtualization, and
smart cooling [30]).
With energy management schemes for the largest
power consumers well in place, we turn to a part of
the data center that consumes 10-20% of its total
1.2 Inside a Data Center
Data centers are typically provisioned for peak
workload, and run well below capacity most of the
time. Traffic varies daily (e.g., email checking during
the day), weekly (e.g., enterprise database queries
on weekdays), monthly (e.g., photo sharing on holidays), and yearly (e.g., more shopping in December).
Rare events like cable cuts or celebrity news may hit
the peak capacity, but most of the time traffic can
be satisfied by a subset of the network links and
We use power and energy interchangeably in this paper.
Essentially a buffered Clos topology.
(a) Typical Data Center Network. (b) Fat tree. All 1G links, always on. (c) Elastic Tree. 0.2 Gbps per host
Racks hold up to 40 “1U” servers, and
across data center can be satisfied by a
two edge switches (i.e.“top-of-rack”
fat tree subset (here, a spanning tree),
yielding 38% savings.
Figure 1: Data Center Networks: (a), 2N Tree (b), Fat Tree (c), ElasticTree
Total Traffic in Gbps
Power in Watts
Bandwidth in Gbps
(a) Router port for 8 days. Input/output ratio varies.
200 300 400 500 600
Time (1 unit = 10 mins)
Figure 2: E-commerce website: 292 production web servers over 5 days. Traffic varies
by day/weekend, power doesn’t.
(b) Router port from Sunday to Monday.
marked increase and short-term spikes.
switches. These observations are based on traces
collected from two production data centers.
Trace 1 (Figure 2) shows aggregate traffic collected from 292 servers hosting an e-commerce application over a 5 day period in April 2008 [22]. A
clear diurnal pattern emerges; traffic peaks during
the day and falls at night. Even though the traffic
varies significantly with time, the rack and aggregation switches associated with these servers draw
constant power (secondary axis in Figure 2).
Trace 2 (Figure 3) shows input and output traffic
at a router port in a production Google data center
in September 2009. The Y axis is in Mbps. The 8day trace shows diurnal and weekend/weekday variation, along with a constant amount of background
traffic. The 1-day trace highlights more short-term
bursts. Here, as in the previous case, the power
consumed by the router is fixed, irrespective of the
traffic through it.
Figure 3: Google Production Data Center
switch configurations. We use switch power measurements from this study and summarize relevant
results in Table 1. In all cases, turning the switch on
consumes most of the power; going from zero to full
traffic increases power by less than 8%. Turning off a
switch yields the most power benefits, while turning
off an unused port saves only 1-2 Watts. Ideally, an
unused switch would consume no power, and energy
usage would grow with increasing traffic load. Consuming energy in proportion to the load is a highly
desirable behavior [4, 22].
Unfortunately, today’s network elements are not
energy proportional: fixed overheads such as fans,
switch chips, and transceivers waste power at low
loads. The situation is improving, as competition
encourages more efficient products, such as closerto-energy-proportional links and switches [19, 18,
26, 14]. However, maximum efficiency comes from a
1.3 Energy Proportionality
An earlier power measurement study [22] had presented power consumption numbers for several data
center switches for a variety of traffic patterns and
1 Gbps
Model A
power (W)
Model B
power (W)
Model C
power (W)
Table 1: Power consumption of various 48port switches for different configurations
combination of improved components and improved
component management.
Our choice – as presented in this paper – is to
manage today’s non energy-proportional network
components more intelligently. By zooming out to
a whole-data-center view, a network of on-or-off,
non-proportional components can act as an energyproportional ensemble, and adapt to varying traffic
loads. The strategy is simple: turn off the links and
switches that we don’t need, right now, to keep available only as much networking capacity as required.
Figure 4: System Diagram
The remainder of the paper is organized as follows: §2 describes in more detail the ElasticTree
approach, plus the modules used to build the prototype. §3 computes the power savings possible for
different communication patterns to understand best
and worse-case scenarios. We also explore power
savings using real data center traffic traces. In §4,
we measure the potential impact on bandwidth and
latency due to ElasticTree. In §5, we explore deployment aspects of ElasticTree in a real data center.
We present related work in §6 and discuss lessons
learned in §7.
1.4 Our Approach
ElasticTree is a network-wide energy optimizer
that continuously monitors data center traffic conditions. It chooses the set of network elements that
must stay active to meet performance and fault tolerance goals; then it powers down as many unneeded
links and switches as possible. We use a variety of
methods to decide which subset of links and switches
to use, including a formal model, greedy bin-packer,
topology-aware heuristic, and prediction methods.
We evaluate ElasticTree by using it to control the
network of a purpose-built cluster of computers and
switches designed to represent a data center. Note
that our approach applies to currently-deployed network devices, as well as newer, more energy-efficient
ones. It applies to single forwarding boxes in a network, as well as individual switch chips within a
large chassis-based router.
While the energy savings from powering off an
individual switch might seem insignificant, a large
data center hosting hundreds of thousands of servers
will have tens of thousands of switches deployed.
The energy savings depend on the traffic patterns,
the level of desired system redundancy, and the size
of the data center itself. Our experiments show that,
on average, savings of 25-40% of the network energy in data centers is feasible. Extrapolating to all
data centers in the U.S., we estimate the savings to
be about 1 billion KWhr annually (based on 3 billion kWh used by networking devices in U.S. data
centers [7]). Additionally, reducing the energy consumed by networking devices also results in a proportional reduction in cooling costs.
ElasticTree is a system for dynamically adapting
the energy consumption of a data center network.
ElasticTree consists of three logical modules - optimizer, routing, and power control - as shown in Figure 4. The optimizer’s role is to find the minimumpower network subset which satisfies current traffic
conditions. Its inputs are the topology, traffic matrix, a power model for each switch, and the desired
fault tolerance properties (spare switches and spare
capacity). The optimizer outputs a set of active
components to both the power control and routing
modules. Power control toggles the power states of
ports, linecards, and entire switches, while routing
chooses paths for all flows, then pushes routes into
the network.
We now show an example of the system in action.
2.1 Example
Figure 1(c) shows a worst-case pattern for network
locality, where each host sends one data flow halfway
across the data center. In this example, 0.2 Gbps
of traffic per host must traverse the network core.
When the optimizer sees this traffic pattern, it finds
which subset of the network is sufficient to satisfy
the traffic matrix. In fact, a minimum spanning tree
(MST) is sufficient, and leaves 0.2 Gbps of extra
capacity along each core link. The optimizer then
informs the routing module to compress traffic along
the new sub-topology, and finally informs the power
control module to turn off unneeded switches and
links. We assume a 3:1 idle:active ratio for modeling
switch power consumption; that is, 3W of power to
have a switch port, and 1W extra to turn it on, based
on the 48-port switch measurements shown in Table
1. In this example, 13/20 switches and 28/48 links
stay active, and ElasticTree reduces network power
by 38%.
As traffic conditions change, the optimizer continuously recomputes the optimal network subset.
As traffic increases, more capacity is brought online,
until the full network capacity is reached. As traffic
decreases, switches and links are turned off. Note
that when traffic is increasing, the system must wait
for capacity to come online before routing through
that capacity. In the other direction, when traffic
is decreasing, the system must change the routing
- by moving flows off of soon-to-be-down links and
switches - before power control can shut anything
Of course, this example goes too far in the direction of power efficiency. The MST solution leaves the
network prone to disconnection from a single failed
link or switch, and provides little extra capacity to
absorb additional traffic. Furthermore, a network
operated close to its capacity will increase the chance
of dropped and/or delayed packets. Later sections
explore the tradeoffs between power, fault tolerance,
and performance. Simple modifications can dramatically improve fault tolerance and performance at
low power, especially for larger networks. We now
describe each of ElasticTree modules in detail.
Traffic Matrix
Traffic Matrix
Port Counters
Table 2: Optimizer Comparison
but finding the optimal flow assignment alone is an
NP-complete problem for integer flows. Despite this
computational complexity, the formal model provides a valuable tool for understanding the solution
quality of other optimizers. It is flexible enough to
support arbitrary topologies, but can only scale up
to networks with less than 1000 nodes.
The model starts with a standard multicommodity flow (MCF) problem. For the precise
MCF formulation, see Appendix A. The constraints
include link capacity, flow conservation, and demand
satisfaction. The variables are the flows along each
link. The inputs include the topology, switch power
model, and traffic matrix. To optimize for power, we
add binary variables for every link and switch, and
constrain traffic to only active (powered on) links
and switches. The model also ensures that the full
power cost for an Ethernet link is incurred when either side is transmitting; there is no such thing as a
half-on Ethernet link.
The optimization goal is to minimize the total network power, while satisfying all constraints. Splitting a single flow across multiple links in the topology might reduce power by improving link utilization
overall, but reordered packets at the destination (resulting from varying path delays) will negatively impact TCP performance. Therefore, we include constraints in our formulation to (optionally) prevent
flows from getting split.
The model outputs a subset of the original topology, plus the routes taken by each flow to satisfy
the traffic matrix. Our model shares similar goals to
Chabarek et al. [6], which also looked at power-aware
routing. However, our model (1) focuses on data
centers, not wide-area networks, (2) chooses a subset of a fixed topology, not the component (switch)
configurations in a topology, and (3) considers individual flows, rather than aggregate traffic.
We implement our formal method using both
MathProg and General Algebraic Modeling System
(GAMS), which are high-level languages for optimization modeling. We use both the GNU Linear
Programming Kit (GLPK) and CPLEX to solve the
2.2 Optimizers
We have developed a range of methods to compute a minimum-power network subset in ElasticTree, as summarized in Table 2. The first method is
a formal model, mainly used to evaluate the solution
quality of other optimizers, due to heavy computational requirements. The second method is greedy
bin-packing, useful for understanding power savings
for larger topologies. The third method is a simple
heuristic to quickly find subsets in networks with
regular structure. Each method achieves different
tradeoffs between scalability and optimality. All
methods can be improved by considering a data center’s past traffic history (details in §5.4).
2.2.1 Formal Model
We desire the optimal-power solution (subset and
flow assignment) that satisfies the traffic constraints,
Optimal 3
Bounded percentage from optimal, configured to 10%.
2.2.2 Greedy Bin-Packing
For even simple traffic patterns, the formal
model’s solution time scales to the 3.5th power as a
function of the number of hosts (details in §5). The
greedy bin-packing heuristic improves on the formal
model’s scalability. Solutions within a bound of optimal are not guaranteed, but in practice, high-quality
subsets result. For each flow, the greedy bin-packer
evaluates possible paths and chooses the leftmost
one with sufficient capacity. By leftmost, we mean
in reference to a single layer in a structured topology, such as a fat tree. Within a layer, paths are
chosen in a deterministic left-to-right order, as opposed to a random order, which would evenly spread
flows. When all flows have been assigned (which is
not guaranteed), the algorithm returns the active
network subset (set of switches and links traversed
by some flow) plus each flow path.
For some traffic matrices, the greedy approach will
not find a satisfying assignment for all flows; this
is an inherent problem with any greedy flow assignment strategy, even when the network is provisioned
for full bisection bandwidth. In this case, the greedy
search will have enumerated all possible paths, and
the flow will be assigned to the path with the lowest
load. Like the model, this approach requires knowledge of the traffic matrix, but the solution can be
computed incrementally, possibly to support on-line
switches in the aggregation layer is then equal to the
number of links required to support the traffic of
the most active source above or below (whichever is
higher), assuming flows are perfectly divisible. For
example, if the most active source sends 2 Gbps of
traffic up to the aggregation layer and each link is
1 Gbps, then two aggregation layer switches must
stay on to satisfy that demand. A similar observation holds between each pod and the core, and the
exact subset computation is described in more detail
in §5. One can think of the topology-aware heuristic
as a cron job for that network, providing periodic
input to any fat tree routing algorithm.
For simplicity, our computations assume a homogeneous fat tree with one link between every connected pair of switches. However, this technique
applies to full-bisection-bandwidth topologies with
any number of layers (we show only 3 stages), bundled links (parallel links connecting two switches),
or varying speeds. Extra “switches at a given layer”
computations must be added for topologies with
more layers. Bundled links can be considered single faster links. The same computation works for
other topologies, such as the aggregated Clos used
by VL2 [10], which has 10G links above the edge
layer and 1G links to each host.
We have implemented all three optimizers; each
outputs a network topology subset, which is then
used by the control software.
2.2.3 Topology-aware Heuristic
2.3 Control Software
The last method leverages the regularity of the fat
tree topology to quickly find network subsets. Unlike
the other methods, it does not compute the set of
flow routes, and assumes perfectly divisible flows. Of
course, by splitting flows, it will pack every link to
full utilization and reduce TCP bandwidth — not
exactly practical.
However, simple additions to this “starter subset” lead to solutions of comparable quality to other
methods, but computed with less information, and
in a fraction of the time. In addition, by decoupling
power optimization from routing, our method can
be applied alongside any fat tree routing algorithm,
including OSPF-ECMP, valiant load balancing [10],
flow classification [1] [2], and end-host path selection [23]. Computing this subset requires only port
counters, not a full traffic matrix.
The intuition behind our heuristic is that to satisfy
traffic demands, an edge switch doesn’t care which
aggregation switches are active, but instead, how
many are active. The “view” of every edge switch in
a given pod is identical; all see the same number of
aggregation switches above. The number of required
ElasticTree requires two network capabilities:
traffic data (current network utilization) and control
over flow paths. NetFlow [27], SNMP and sampling
can provide traffic data, while policy-based routing can provide path control, to some extent. In
our ElasticTree prototype, we use OpenFlow [29] to
achieve the above tasks.
OpenFlow: OpenFlow is an open API added
to commercial switches and routers that provides a
flow table abstraction. We first use OpenFlow to
validate optimizer solutions by directly pushing the
computed set of application-level flow routes to each
switch, then generating traffic as described later in
this section. In the live prototype, OpenFlow also
provides the traffic matrix (flow-specific counters),
port counters, and port power control. OpenFlow
enables us to evaluate ElasticTree on switches from
different vendors, with no source code changes.
NOX: NOX is a centralized platform that provides network visibility and control atop a network
of OpenFlow switches [13]. The logical modules
in ElasticTree are implemented as a NOX application. The application pulls flow and port counters,
Figure 6: Measurement Setup
Figure 5: Hardware Testbed (HP switch for
k = 6 fat tree)
Virtual Switches
The larger configuration is a complete k = 6
three-layer fat tree, split into 45 independent sixport virtual switches, supporting 54 hosts at 1 Gbps
apiece. This configuration runs on one 288-port HP
ProCurve 5412 chassis switch or two 144-port 5406
chassis switches, running OpenFlow v0.8.9 firmware
provided by HP Labs.
Table 3: Fat Tree Configurations
directs these to an optimizer, and then adjusts flow
routes and port status based on the computed subset. In our current setup, we do not power off inactive switches, due to the fact that our switches
are virtual switches. However, in a real data center deployment, we can leverage any of the existing
mechanisms such as command line interface, SNMP
or newer control mechanisms such as power-control
over OpenFlow in order to support the power control
2.5 Measurement Setup
Evaluating ElasticTree requires infrastructure to
generate a small data center’s worth of traffic, plus
the ability to concurrently measure packet drops and
delays. To this end, we have implemented a NetFPGA based traffic generator and a dedicated latency
monitor. The measurement architecture is shown in
Figure 6.
NetFPGA Traffic Generators. The NetFPGA
Packet Generator provides deterministic, line-rate
traffic generation for all packet sizes [28]. Each
NetFPGA emulates four servers with 1GE connections. Multiple traffic generators combine to emulate
a larger group of independent servers: for the k=6
fat tree, 14 NetFPGAs represent 54 servers, and for
the k=4 fat tree,4 NetFPGAs represent 16 servers.
At the start of each test, the traffic distribution for each port is packed by a weighted round
robin scheduler into the packet generator SRAM. All
packet generators are synchronized by sending one
packet through an Ethernet control port; these control packets are sent consecutively to minimize the
start-time variation. After sending traffic, we poll
and store the transmit and receive counters on the
packet generators.
Latency Monitor. The latency monitor PC
sends tracer packets along each packet path. Tracers
enter and exit through a different port on the same
physical switch chip; there is one Ethernet port on
the latency monitor PC per switch chip. Packets are
2.4 Prototype Testbed
We build multiple testbeds to verify and evaluate
ElasticTree, summarized in Table 3, with an example shown in Figure 5. Each configuration multiplexes many smaller virtual switches (with 4 or 6
ports) onto one or more large physical switches. All
communication between virtual switches is done over
direct links (not through any switch backplane or intermediate switch).
The smaller configuration is a complete k = 4
three-layer homogeneous fat tree4 , split into 20 independent four-port virtual switches, supporting 16
nodes at 1 Gbps apiece. One instantiation comprised 2 NEC IP8800 24-port switches and 1 48port switch, running OpenFlow v0.8.9 firmware provided by NEC Labs. Another comprised two Quanta
LB4G 48-port switches, running the OpenFlow Reference Broadcom firmware.
Refer [1] for details on fat trees and definition of k
logged by Pcap on entry and exit to record precise
timestamp deltas. We report median figures that are
averaged over all packet paths. To ensure measurements are taken in steady state, the latency monitor starts up after 100 ms. This technique captures
all but the last-hop egress queuing delays. Since
edge links are never oversubscribed for our traffic
patterns, the last-hop egress queue should incur no
added delay.
% original network power
In this section, we analyze ElasticTree’s network
energy savings when compared to an always-on baseline. Our comparisons assume a homogeneous fat
tree for simplicity, though the evaluation also applies
to full-bisection-bandwidth topologies with aggregation, such as those with 1G links at the edge and
10G at the core. The primary metric we inspect is
% original network power, computed as:
50% Far, 50% Mid
50% Near, 50% Mid
Traffic Demand (Gbps)
Figure 7: Power savings as a function of demand, with varying traffic locality, for a 28Knode, k=48 fat tree
3.1.1 Uniform Demand, Varying Locality
First, consider two extreme cases: near (highly
localized) traffic matrices, where servers communicate only with other servers through their edge
switch, and far (non-localized) traffic matrices
where servers communicate only with servers in
other pods, through the network core. In this pattern, all traffic stays within the data center, and
none comes from outside. Understanding these extreme cases helps to quantify the range of network
energy savings. Here, we use the formal method as
the optimizer in ElasticTree.
Near traffic is a best-case — leading to the largest
energy savings — because ElasticTree will reduce
the network to the minimum spanning tree, switching off all but one core switch and one aggregation
switch per pod. On the other hand, far traffic is a
worst-case — leading to the smallest energy savings
— because every link and switch in the network is
needed. For far traffic, the savings
P Pdepend heavily
j λij
on the network utilization, u = Total
hosts (λij is the
traffic from host i to host j, λij < 1 Gbps). If u is
close to 100%, then all links and switches must remain active. However, with lower utilization, traffic
can be concentrated onto a smaller number of core
links, and unused ones switch off. Figure 7 shows
the potential savings as a function of utilization for
both extremes, as well as traffic to the aggregation
layer Mid), for a k = 48 fat tree with roughly 28K
servers. Running ElasticTree on this configuration,
with near traffic at low utilization, we expect a network energy reduction of 60%; we cannot save any
further energy, as the active network subset in this
case is the MST. For far traffic and u=100%, there
are no energy savings. This graph highlights the
power benefit of local communications, but more im-
Power consumed by ElasticTree × 100
Power consumed by original fat-tree
This percentage gives an accurate idea of the overall power saved by turning off switches and links
(i.e., savings equal 100 - % original power). We
use power numbers from switch model A (§1.3) for
both the baseline and ElasticTree cases, and only
include active (powered-on) switches and links for
ElasticTree cases. Since all three switches in Table 1 have an idle:active ratio of 3:1 (explained in
§2.1), using power numbers from switch model B
or C will yield similar network energy savings. Unless otherwise noted, optimizer solutions come from
the greedy bin-packing algorithm, with flow splitting
disabled (as explained in Section 2). We validate the
results for all k = {4, 6} fat tree topologies on multiple testbeds. For all communication patterns, the
measured bandwidth as reported by receive counters
matches the expected values. We only report energy
saved directly from the network; extra energy will be
required to power on and keep running the servers
hosting ElasticTree modules. There will be additional energy required for cooling these servers, and
at the same time, powering off unused switches will
result in cooling energy savings. We do not include
these extra costs/savings in this paper.
3.1 Traffic Patterns
Energy, performance and robustness all depend
heavily on the traffic pattern. We now explore the
possible energy savings over a wide range of communication patterns, leaving performance and robustness for §4.
% original network power
0.2 0.3 0.4 0.5 0.6
Avg. network utilization
Figure 8: Scatterplot of power savings with
random traffic matrix. Each point on the
graph corresponds to a pre-configured average data center workload, for a k = 6 fat tree
Figure 9: Power savings for sinusoidal traffic
variation in a k = 4 fat tree topology, with 1
flow per host in the traffic matrix. The input
demand has 10 discrete values.
portantly, shows potential savings in all cases. Having seen these two extremes, we now consider more
realistic traffic matrices with a mix of both near and
far traffic.
time scales. Figure 9 shows a time-varying utilization; power savings from ElasticTree that follow the
utilization curve. To crudely approximate diurnal
variation, we assume u = 1/2(1 + sin(t)), at time t,
suitably scaled to repeat once per day. For this sine
wave pattern of traffic demand, the network power
can be reduced up to 64% of the original power consumed, without being over-subscribed and causing
We note that most energy savings in all the above
communication patterns comes from powering off
switches. Current networking devices are far from
being energy proportional, with even completely idle
switches (0% utilization) consuming 70-80% of their
fully loaded power (100% utilization) [22]; thus powering off switches yields the most energy savings.
3.1.2 Random Demand
Here, we explore how much energy we can expect
to save, on average, with random, admissible traffic matrices. Figure 8 shows energy saved by ElasticTree (relative to the baseline) for these matrices,
generated by picking flows uniformly and randomly,
then scaled down by the most oversubscribed host’s
traffic to ensure admissibility. As seen previously,
for low utilization, ElasticTree saves roughly 60% of
the network power, regardless of the traffic matrix.
As the utilization increases, traffic matrices with significant amounts of far traffic will have less room for
power savings, and so the power saving decreases.
The two large steps correspond to utilizations at
which an extra aggregation switch becomes necessary across all pods. The smaller steps correspond
to individual aggregation or core switches turning on
and off. Some patterns will densely fill all available
links, while others will have to incur the entire power
cost of a switch for a single link; hence the variability in some regions of the graph. Utilizations above
0.75 are not shown; for these matrices, the greedy
bin-packer would sometimes fail to find a complete
satisfying assignment of flows to links.
3.1.4 Traffic in a Realistic Data Center
In order to evaluate energy savings with a real
data center workload, we collected system and network traces from a production data center hosting an
e-commerce application (Trace 1, §1). The servers
in the data center are organized in a tiered model as
application servers, file servers and database servers.
The System Activity Reporter (sar) toolkit available
on Linux obtains CPU, memory and network statistics, including the number of bytes transmitted and
received from 292 servers. Our traces contain statistics averaged over a 10-minute interval and span 5
days in April 2008. The aggregate traffic through
all the servers varies between 2 and 12 Gbps at any
given time instant (Figure 2). Around 70% of the
3.1.3 Sine-wave Demand
As seen before (§1.2), the utilization of a data center will vary over time, on daily, seasonal and annual
measured, 70% to Internet, x 20, greedy
measured, 70% to Internet, x 10, greedy
measured, 70% to Internet, x 1, greedy
% original network power
% original network power
# hosts in network
100 200 300 400 500 600 700 800
Time (1 unit = 10 mins)
Figure 11: Power cost of redundancy
Figure 10: Energy savings for production
data center (e-commerce website) traces, over
a 5 day period, using a k=12 fat tree. We
show savings for different levels of overall
traffic, with 70% destined outside the DC.
Statistics for Trace 1 for a day
70% to Internet, x 10, greedy
70% to Internet, x 10, greedy + 10% margin
70% to Internet, x 10, greedy + 20% margin
70% to Internet, x 10, greedy + 30% margin
70% to Internet, x 10, greedy + 1
70% to Internet, x 10, greedy + 2
70% to Internet, x 10, greedy + 3
% original network power
traffic leaves the data center and the remaining 30%
is distributed to servers within the data center.
In order to compute the energy savings from ElasticTree for these 292 hosts, we need a k = 12 fat
tree. Since our testbed only supports k = 4 and
k = 6 sized fat trees, we simulate the effect of ElasticTree using the greedy bin-packing optimizer on
these traces. A fat tree with k = 12 can support up
to 432 servers; since our traces are from 292 servers,
we assume the remaining 140 servers have been powered off. The edge switches associated with these
powered-off servers are assumed to be powered off;
we do not include their cost in the baseline routing
power calculation.
The e-commerce service does not generate enough
network traffic to require a high bisection bandwidth
topology such as a fat tree. However, the timevarying characteristics are of interest for evaluating
ElasticTree, and should remain valid with proportionally larger amounts of network traffic. Hence,
we scale the traffic up by a factor of 20.
For different scaling factors, as well as for different
intra data center versus outside data center (external) traffic ratios, we observe energy savings ranging
from 25-62%. We present our energy savings results
in Figure 10. The main observation when visually
comparing with Figure 2 is that the power consumed
by the network follows the traffic load curve. Even
though individual network devices are not energyproportional, ElasticTree introduces energy proportionality into the network.
60 80 100 120 140 160
Time (1 unit = 10 mins)
Figure 12: Power consumption in a robust
data center network with safety margins, as
well as redundancy. Note “greedy+1” means
we add a MST over the solution returned by
the greedy solver.
We stress that network energy savings are workload dependent. While we have explored savings
in the best-case and worst-case traffic scenarios as
well as using traces from a production data center,
a highly utilized and “never-idle” data center network would not benefit from running ElasticTree.
3.2 Robustness Analysis
Typically data center networks incorporate some
level of capacity margin, as well as redundancy in
the topology, to prepare for traffic surges and network failures. In such cases, the network uses more
switches and links than essential for the regular production workload.
Consider the case where only a minimum spanning
Std. Dev
Table 4: Latency baselines for Queue Test Setups
Figure 13: Queue Test Setups with one (left)
and two (right) bottlenecks
tree (MST) in the fat tree topology is turned on (all
other links/switches are powered off); this subset
certainly minimizes power consumption. However,
it also throws away all path redundancy, and with
it, all fault tolerance. In Figure 11, we extend the
MST in the fat tree with additional active switches,
for varying topology sizes. The MST+1 configuration requires one additional edge switch per pod,
and one additional switch in the core, to enable any
single aggregation or core-level switch to fail without disconnecting a server. The MST+2 configuration enables any two failures in the core or aggregation layers, with no loss of connectivity. As the
network size increases, the incremental cost of additional fault tolerance becomes an insignificant part
of the total network power. For the largest networks,
the savings reduce by only 1% for each additional
spanning tree in the core aggregation levels. Each
+1 increment in redundancy has an additive cost,
but a multiplicative benefit; with MST+2, for example, the failures would have to happen in the same
pod to disconnect a host. This graph shows that the
added cost of fault tolerance is low.
Figure 12 presents power figures for the k=12 fat
tree topology when we add safety margins for accommodating bursts in the workload. We observe
that the additional power cost incurred is minimal,
while improving the network’s ability to absorb unexpected traffic surges.
Latency median
Traffic demand (Gbps)
Figure 14: Latency vs demand, with uniform
by the kernel, and we record the latency of each received packet, as well as the number of drops. This
test is useful mainly to quantify PC-induced latency
variability. In the single-bottleneck case, two hosts
send 0.7 Gbps of constant-rate traffic to a single
switch output port, which connects through a second
switch to a receiver. Concurrently with the packet
generator traffic, the latency monitor sends tracer
packets. In the double-bottleneck case, three hosts
send 0.7 Gbps, again while tracers are sent.
Table 4 shows the latency distribution of tracer
packets sent through the Quanta switch, for all three
cases. With no background traffic, the baseline latency is 36 us. In the single-bottleneck case, the
egress buffer fills immediately, and packets experience 474 us of buffering delay. For the doublebottleneck case, most packets are delayed twice, to
914 us, while a smaller fraction take the singlebottleneck path. The HP switch (data not shown)
follows the same pattern, with similar minimum latency and about 1500 us of buffer depth. All cases
show low measurement variation.
The power savings shown in the previous section
are worthwhile only if the performance penalty is
negligible. In this section, we quantify the performance degradation from running traffic over a network subset, and show how to mitigate negative effects with a safety margin.
4.1 Queuing Baseline
Figure 13 shows the setup for measuring the buffer
depth in our test switches; when queuing occurs,
this knowledge helps to estimate the number of hops
where packets are delayed. In the congestion-free
case (not shown), a dedicated latency monitor PC
sends tracer packets into a switch, which sends it
right back to the monitor. Packets are timestamped
4.2 Uniform Traffic, Varying Demand
In Figure 14, we see the latency totals for a uniform traffic series where all traffic goes through the
core to a different pod, and every hosts sends one
flow. To allow the network to reach steady state,
measurements start 100 ms after packets are sent,
overload (Mbps / Host)
Average Latency (usec)
Loss percentage (%)
Figure 15: Drops vs overload with varying
safety margins
overload (Mbps / Host)
Figure 16: Latency vs overload with varying
safety margins
and continue until the end of the test, 900 ms later.
All tests use 512-byte packets; other packet sizes
yield the same results. The graph covers packet
generator traffic from idle to 1 Gbps, while tracer
packets are sent along every flow path. If our solution is feasible, that is, all flows on each link sum to
less than its capacity, then we will see no dropped
packets, with a consistently low latency.
Instead, we observe sharp spikes at 0.25 Gbps,
0.33 Gbps, and 0.5 Gbps. These spikes correspond
to points where the available link bandwidth is exceeded, even by a small amount. For example, when
ElasticTree compresses four 0.25 Gbps flows along
a single 1 Gbps link, Ethernet overheads (preamble,
inter-frame spacing, and the CRC) cause the egress
buffer to fill up. Packets either get dropped or significantly delayed.
This example motivates the need for a safety
margin to account for processing overheads, traffic
bursts, and sustained load increases. The issue is
not just that drops occur, but also that every packet
on an overloaded link experiences significant delay.
Next, we attempt to gain insight into how to set the
safety margin, or capacity reserve, such that performance stays high up to a known traffic overload.
Computation time (s)
LP GLPK, without split
LP GLPK, with split
LP GAMS, with split
Greedy, without split
Greedy, with split
Topo-aware Heuristic
Total Hosts
Figure 17: Computation time for different optimizers as a function of network size
spread evenly across all flows sent by that host. For
example, at zero overload, a solution with a safety
margin of 100 Mbps will prevent more than 900
Mbps of combined flows from crossing each link. If
a host sends 4 flows (as in these plots) at 100 Mbps
overload, each flow is boosted by 25 Mbps. Each
data point represents the average over 5 traffic matrices. In all matrices, each host sends to 4 randomly
chosen hosts, with a total outgoing bandwidth selected uniformly between 0 and 0.5 Gbps. All tests
complete in one second.
Drops Figure 15 shows no drops for small
overloads (up to 100 Mbps), followed by a steadily
increasing drop percentage as overload increases.
Loss percentage levels off somewhat after 500 Mbps,
as some flows cap out at 1 Gbps and generate no
extra traffic. As expected, increasing the safety
margin defers the point at which performance
4.3 Setting Safety Margins
Figures 15 and 16 show drops and latency as a
function of traffic overload, for varying safety margins. Safety margin is the amount of capacity reserved at every link by the optimizer; a higher safety
margin provides performance insurance, by delaying
the point at which drops start to occur, and average latency starts to degrade. Traffic overload is
the amount each host sends and receives beyond the
original traffic matrix. The overload for a host is
set computation for 10K hosts takes less than 10
seconds for a single-core, unoptimized, Python implementation – faster than the fastest switch boot
time we observed (30 seconds for the Quanta switch).
This result implies that the topology-aware heuristic approach is not fundamentally unscalable, especially considering that the number of operations increases linearly with the number of hosts. We next
describe in detail the topology-aware heuristic, and
show how small modifications to its “starter subset”
can yield high-quality, practical network solutions,
in little time.
Latency In Figure 16, latency shows a trend similar to drops, except when overload increases to 200
Mbps, the performance effect is more pronounced.
For the 250 Mbps margin line, a 200 Mbps overload results in 1% drops, however latency increases
by 10x due to the few congested links. Some margin
lines cross at high overloads; this is not to say that a
smaller margin is outperforming a larger one, since
drops increase, and we ignore those in the latency
Interpretation Given these plots, a network operator can choose the safety margin that best balances the competing goals of performance and energy efficiency. For example, a network operator
might observe from past history that the traffic average never varies by more than 100 Mbps in any
10 minute span. She considers an average latency
under 100 us to be acceptable. Assuming that ElasticTree can transition to a new subset every 10 minutes, the operator looks at 100 Mbps overload on
each plot. She then finds the smallest safety margin
with sufficient performance, which in this case is 150
Mbps. The operator can then have some assurance
that if the traffic changes as expected, the network
will meet her performance criteria, while consuming
the minimum amount of power.
5.2 Topology-Aware Heuristic
We describe precisely how to calculate the subset
of active network elements using only port counters.
Links. First, compute LEdgeup
p,e , the minimum
number of active links exiting edge switch e in pod
p to support up-traffic (edge → agg):
F (e → a))/r⌉
p,e = ⌈(
Ap is the set of aggregation switches in pod p,
F (e → a) is the traffic flow from edge switch e to
aggregation switch a, and r is the link rate. The
total up-traffic of e, divided by the link rate, equals
the minimum number of links from e required to
satisfy the up-traffic bandwidth. Similarly, compute
p,e , the number of active links exiting edge
switch e in pod p to support down-traffic (agg →
F (a → e))/r⌉
= ⌈(
Here, we address some of the practical aspects of
deploying ElasticTree in a live data center environment.
5.1 Comparing various optimizers
We first discuss the scalability of various optimizers in ElasticTree, based on solution time vs network
size, as shown in Figure 17. This analysis provides
a sense of the feasibility of their deployment in a
real data center. The formal model produces solutions closest to optimal; however for larger topologies (such as fat trees with k >= 14), the time to
find the optimal solution becomes intractable. For
example, finding a network subset with the formal
model with flow splitting enabled on CPLEX on a
single core, 2 Ghz machine, for a k = 16 fat tree,
takes about an hour. The solution time growth
of this carefully optimized model is about O(n3.5 ),
where n is the number of hosts. We then ran the
greedy-bin packer (written in unoptimized Python)
on a single core of a 2.13 Ghz laptop with 3 GB of
RAM. The no-split version scaled as about O(n2.5 ),
while the with-split version scaled slightly better,
as O(n2 ). The topology-aware heuristic fares much
better, scaling as roughly O(n), as expected. Sub-
The maximum of these two values (plus 1, to ensure a spanning tree at idle) gives LEdgep,e , the minimum number of links for edge switch e in pod p:
LEdgep,e = max{LEdgeup
p,e , LEdgep,e , 1}
Now, compute the number of active links from
each pod to the core. LAggpup is the minimum number of links from pod p to the core to satisfy the
up-traffic bandwidth (agg → core):
LAggpup = ⌈(
F (a → c))/r⌉
c∈C,a∈Ap ,a→c
Hence, we find the number of up-links, LAggpdown
used to support down-traffic (core → agg) in pod p:
LAggpdown = ⌈(
F (c → a))/r⌉
c∈C,a∈Ap ,c→a
The maximum of these two values (plus 1, to ensure a spanning tree at idle) gives LAggp , the mini12
switches in real hardware and find that the dominant time is waiting for the switch to boot up, which
ranges from 30 seconds for the Quanta switch to
about 3 minutes for the HP switch. Powering individual ports on and off takes about 1 − 3 seconds.
Populating the entire flow table on a switch takes under 5 seconds, while reading all port counters takes
less than 100 ms for both. Switch models in the future may support features such as going into various
sleep modes; the time taken to wake up from sleep
modes will be significantly faster than booting up.
ElasticTree can then choose which switches to power
off versus which ones to put to sleep.
Further, the ability to predict traffic patterns for
the next few hours for traces that exhibit regular
behavior will allow network operators to plan ahead
and get the required capacity (plus some safety margin) ready in time for the next traffic spike. Alternately, a control loop strategy to address performance effects from burstiness would be to dynamically increase the safety margin whenever a threshold set by a service-level agreement policy were exceeded, such as a percentage of packet drops.
mum number of core links for pod p:
LAggp = max{LEdgeup
p , LEdgep
Switches. For both the aggregation and core layers, the number of switches follows directly from the
link calculations, as every active link must connect
to an active switch. First, we compute N Aggpup , the
minimum number of aggregation switches required
to satisfy up-traffic (edge → agg) in pod p:
N Aggpup = max{LEdgeup
p,e }
Next, compute N Aggpdown , the minimum number
of aggregation switches required to support downtraffic (core → agg) in pod p:
N Aggpdown = ⌈(LAggpdown/(k/2)⌉
C is the set of core switches and k is the switch
degree. The number of core links in the pod, divided
by the number of links uplink in each aggregation
switch, equals the minimum number of aggregation
switches required to satisfy the bandwidth demands
from all core switches. The maximum of these two
values gives N Aggp , the minimum number of active
aggregation switches in the pod:
5.4 Traffic Prediction
N Aggp = max{N Aggpup , N Aggpdown , 1}
In all of our experiments, we input the entire traffic matrix to the optimizer, and thus assume that
we have complete prior knowledge of incoming traffic. In a real deployment of ElasticTree, such an
assumption is unrealistic. One possible workaround
is to predict the incoming traffic matrix based on
historical traffic, in order to plan ahead for expected
traffic spikes or long-term changes. While prediction techniques are highly sensitive to workloads,
they are more effective for traffic that exhibit regular
patterns, such as our production data center traces
(§3.1.4). We experiment with a simple auto regressive AR(1) prediction model in order to predict traffic to and from each of the 292 servers. We use traffic traces from the first day to train the model, then
use this model to predict traffic for the entire 5 day
period. Using the traffic prediction, the greedy binpacker can determine an active topology subset as
well as flow routes.
While detailed traffic prediction and analysis are
beyond the scope of this paper, our initial experimental results are encouraging. They imply that
even simple prediction models can be used for data
center traffic that exhibits periodic (and thus predictable) behavior.
Finally, the traffic between the core and the mostactive pod informs N Core, the number of core
switches that must be active to satisfy the traffic
N Core = ⌈max(LAggpup )⌉
Robustness. The equations above assume that
100% utilized links are acceptable. We can change
r, the link rate parameter, to set the desired average link utilization. Reducing r reserves additional
resources to absorb traffic overloads, plus helps to
reduce queuing delay. Further, if hashing is used to
balance flows across different links, reducing r helps
account for collisions.
To add k-redundancy to the starter subset for improved fault tolerance, add k aggregation switches
to each pod and the core, plus activate the links
on all added switches. Adding k-redundancy can be
thought of as adding k parallel MSTs that overlap
at the edge switches. These two approaches can be
combined for better robustness.
5.3 Response Time
The ability of ElasticTree to respond to spikes in
traffic depends on the time required to gather statistics, compute a solution, wait for switches to boot,
enable links, and push down new routes. We measured the time required to power on/off links and
5.5 Fault Tolerance
ElasticTree modules can be placed in ways that
mitigate fault tolerance worries. In our testbed, the
choosing the chassis and linecard configuration to
best meet the expected demand. In contrast, our
formulation optimizes a data center local area network, finds the power-optimal network subset and
routing to use, and includes an evaluation of our
prototype. Further, we detail the tradeoffs associated with our approach, including impact on packet
latency and drops.
Nedevschi et al. [26] propose shaping the traffic
into small bursts at edge routers to facilitate putting
routers to sleep. Their research is complementary to
ours. Further, their work addresses edge routers in
the Internet while our algorithms are for data centers. In a recent work, Ananthanarayanan [3] et
al. motivate via simulation two schemes - a lower
power mode for ports and time window prediction
techniques that vendors can implemented in future
switches. While these and other improvements can
be made in future switch designs to make them more
energy efficient, most energy (70-80% of their total
power) is consumed by switches in their idle state.
A more effective way of saving power is using a traffic routing approach such as ours to maximize idle
switches and power them off. Another recent paper [25] et al. discusses the benefits and deployment
models of a network proxy that would allow endhosts to sleep while the proxy keeps the network
connection alive.
Other complementary research in data center networks has focused on scalability [24][10], switching
layers that can incorporate different policies [20], or
architectures with programmable switches [11].
routing and optimizer modules run on a single host
PC. This arrangement ties the fate of the whole system to that of each module; an optimizer crash is
capable of bringing down the system.
Fortunately, the topology-aware heuristic – the
optimizer most likely to be deployed – operates independently of routing. The simple solution is to move
the optimizer to a separate host to prevent slow
computation or crashes from affecting routing. Our
OpenFlow switches support a passive listening port,
to which the read-only optimizer can connect to grab
port statistics. After computing the switch/link subset, the optimizer must send this subset to the routing controller, which can apply it to the network.
If the optimizer doesn’t check in within a fixed period of time, the controller should bring all switches
up. The reliability of ElasticTree should be no worse
than the optimizer-less original; the failure condition
brings back the original network power, plus a time
period with reduced network capacity.
For optimizers tied to routing, such as the formal model and greedy bin-packer, known techniques
can provide controller-level fault tolerance. In active
standby, the primary controller performs all required
tasks, while the redundant controllers stay idle. On
failing to receive a periodic heartbeat from the primary, a redundant controller becomes to the new primary. This technique has been demonstrated with
NOX, so we expect it to work with our system. In
the more complicated full replication case, multiple
controllers are simultaneously active, and state (for
routing and optimization) is held consistent between
them. For ElasticTree, the optimization calculations
would be spread among the controllers, and each
controller would be responsible for power control for
a section of the network. For a more detailed discussion of these issues, see §3.5 “Replicating the Controller: Fault-Tolerance and Scalability” in [5].
The idea of disabling critical network infrastructure in data centers has been considered taboo. Any
dynamic energy management system that attempts
to achieve energy proportionality by powering off a
subset of idle components must demonstrate that
the active components can still meet the current offered load, as well as changing load in the immediate future. The power savings must be worthwhile,
performance effects must be minimal, and fault tolerance must not be sacrificed. The system must produce a feasible set of network subsets that can route
to all hosts, and be able to scale to a data center
with tens of thousands of servers.
To this end, we have built ElasticTree, which
through data-center-wide traffic management and
control, introduces energy proportionality in today’s
non-energy proportional networks. Our initial results (covering analysis, simulation, and hardware
prototypes) demonstrate the tradeoffs between per-
This paper tries to extend the idea of power proportionality into the network domain, as first described by Barroso et al. [4]. Gupta et al. [17] were
amongst the earliest researchers to advocate conserving energy in networks. They suggested putting
network components to sleep in order to save energy and explored the feasibility in a LAN setting
in a later paper [18]. Several others have proposed
techniques such as putting idle components in a
switch (or router) to sleep [18] as well as adapting
the link rate [14], including the IEEE 802.3az Task
Force [19].
Chabarek et al. [6] use mixed integer programming
to optimize router power in a wide area network, by
formance, robustness, and energy; the safety margin parameter provides network administrators with
control over these tradeoffs. ElasticTree’s ability to
respond to sudden increases in traffic is currently
limited by the switch boot delay, but this limitation can be addressed, relatively simply, by adding
a sleep mode to switches.
ElasticTree opens up many questions. For example, how will TCP-based application traffic interact
with ElasticTree? TCP maintains link utilization in
sawtooth mode; a network with primarily TCP flows
might yield measured traffic that stays below the
threshold for a small safety margin, causing ElasticTree to never increase capacity. Another question is the effect of increasing network size: a larger
network probably means more, smaller flows, which
pack more densely, and reduce the chance of queuing
delays and drops. We would also like to explore the
general applicability of the heuristic to other topologies, such as hypercubes and butterflies.
Unlike choosing between cost, speed, and reliability when purchasing a car, with ElasticTree one
doesn’t have to pick just two when offered performance, robustness, and energy efficiency. During
periods of low to mid utilization, and for a variety
of communication patterns (as is often observed in
data centers), ElasticTree can maintain the robustness and performance, while lowering the energy bill.
The authors want to thank their shepherd, Ant
Rowstron, for his advice and guidance in producing the final version of this paper, as well as the
anonymous reviewers for their feedback and suggestions. Xiaoyun Zhu (VMware) and Ram Swaminathan (HP Labs) contributed to the problem formulation; Parthasarathy Ranganathan (HP Labs)
helped with the initial ideas in this paper. Thanks
for OpenFlow switches goes to Jean Tourrilhes and
Praveen Yalagandula at HP Labs, plus the NEC
IP8800 team. Greg Chesson provided the Google
[1] M. Al-Fares, A. Loukissas, and A. Vahdat. A Scalable,
Commodity Data Center Network Architecture. In ACM
SIGCOMM, pages 63–74, 2008.
[2] M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang,
and A. Vahdat. Hedera: Dynamic Flow Scheduling for Data
Center Networks. In USENIX NSDI, April 2010.
[3] G. Ananthanarayanan and R. Katz. Greening the Switch.
In Proceedings of HotPower, December 2008.
[4] L. A. Barroso and U. Hölzle. The Case for
Energy-Proportional Computing. Computer, 40(12):33–37,
[5] M. Casado, M. Freedman, J. Pettit, J. Luo, N. McKeown,
and S. Shenker. Ethane: Taking control of the enterprise.
In Proceedings of the 2007 Conference on Applications,
Technologies, Architectures, and Protocols for Computer
Communications, page 12. ACM, 2007.
J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang,
and S. Wright. Power Awareness in Network Design and
Routing. In IEEE INFOCOM, April 2008.
U.S. Environmental Protection Agency’s Data Center
Report to Congress. http://tinyurl.com/2jz3ft.
S. Even, A. Itai, and A. Shamir. On the Complexity of
Time Table and Multi-Commodity Flow Problems. In 16th
Annual Symposium on Foundations of Computer Science,
pages 184–193, October 1975.
A. Greenberg, J. Hamilton, D. Maltz, and P. Patel. The
Cost of a Cloud: Research Problems in Data Center
Networks. In ACM SIGCOMM CCR, January 2009.
A. Greenberg, N. Jain, S. Kandula, C. Kim, P. Lahiri,
D. Maltz, P. Patel, and S. Sengupta. VL2: A Scalable and
Flexible Data Center Network. In ACM SIGCOMM,
August 2009.
A. Greenberg, P. Lahiri, D. A. Maltz, P. Patel, and
S. Sengupta. Towards a Next Generation Data Center
Architecture: Scalability and Commoditization. In ACM
PRESTO, pages 57–62, 2008.
D. Grunwald, P. Levis, K. Farkas, C. M. III, and
M. Neufeld. Policies for Dynamic Clock Scheduling. In
OSDI, 2000.
N. Gude, T. Koponen, J. Pettit, B. Pfaff, M. Casado, and
N. McKeown. NOX: Towards an Operating System for
Networks. In ACM SIGCOMM CCR, July 2008.
C. Gunaratne, K. Christensen, B. Nordman, and S. Suen.
Reducing the Energy Consumption of Ethernet with
Adaptive Link Rate (ALR). IEEE Transactions on
Computers, 57:448–461, April 2008.
C. Guo, G. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian,
Y. Zhang, and S. Lu. BCube: A High Performance,
Server-centric Network Architecture for Modular Data
Centers. In ACM SIGCOMM, August 2009.
C. Guo, H. Wu, K. Tan, L. Shi, Y. Zhang, and S. Lu.
DCell: A Scalable and Fault-Tolerant Network Structure
for Data Centers. In ACM SIGCOMM, pages 75–86, 2008.
M. Gupta and S. Singh. Greening of the internet. In ACM
SIGCOMM, pages 19–26, 2003.
M. Gupta and S. Singh. Using Low-Power Modes for
Energy Conservation in Ethernet LANs. In IEEE
INFOCOM, May 2007.
IEEE 802.3az. ieee802.org/3/az/public/index.html.
D. A. Joseph, A. Tavakoli, and I. Stoica. A Policy-aware
Switching Layer for Data Centers. SIGCOMM Comput.
Commun. Rev., 38(4):51–62, 2008.
S. Kandula, D. Katabi, S. Sinha, and A. Berger. Dynamic
Load Balancing Without Packet Reordering. SIGCOMM
Comput. Commun. Rev., 37(2):51–62, 2007.
P. Mahadevan, P. Sharma, S. Banerjee, and
P. Ranganathan. A Power Benchmarking Framework for
Network Devices. In Proceedings of IFIP Networking, May
J. Mudigonda, P. Yalagandula, M. Al-Fares, and J. C.
Mogul. SPAIN: COTS Data-Center Ethernet for
Multipathing over Arbitrary Topologies. In USENIX
NSDI, April 2010.
R. Mysore, A. Pamboris, N. Farrington, N. Huang, P. Miri,
S. Radhakrishnan, V. Subramanya, and A. Vahdat.
PortLand: A Scalable Fault-Tolerant Layer 2 Data Center
Network Fabric. In ACM SIGCOMM, August 2009.
S. Nedevschi, J. Chandrashenkar, B. Nordman,
S. Ratnasamy, and N. Taft. Skilled in the Art of Being Idle:
Reducing Energy Waste in Networked Systems. In
Proceedings Of NSDI, April 2009.
S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, and
D. Wetherall. Reducing Network Energy Consumption via
Sleeping and Rate-Adaptation. In Proceedings of the 5th
USENIX NSDI, pages 323–336, 2008.
Cisco IOS NetFlow. http://www.cisco.com/web/go/netflow.
NetFPGA Packet Generator. http://tinyurl.com/ygcupdc.
The OpenFlow Switch. http://www.openflowswitch.org.
C. Patel, C. Bash, R. Sharma, M. Beitelmam, and
R. Friedrich. Smart Cooling of data Centers. In
Proceedings of InterPack, July 2003.
Deactivated links have no traffic: Flow is restricted to only those links (and consequently
the switches) that are powered on. Thus, for all
links (u, v) used by commodity i, fi (u, v) = 0,
when Xu,v = 0. Since the flow variable f is
positive in our formulation, the linearized constraint is:
Our model is a multi-commodity flow formulation,
augmented with binary variables for the power state
of links and switches. It minimizes the total network
power by solving a mixed-integer linear program.
A.1 Multi-Commodity Network Flow
∀i, ∀(u, v) ∈ E,
Flow network G(V, E), has edges (u, v) ∈ E
with capacity c(u, v). There are k commodities
K1 , K2 , . . . , Kk , defined by Ki = (si , ti , di ), where,
for commodity i, si is the source, ti is the sink, and
di is the demand. The flow of commodity i along
edge (u, v) is fi (u, v). Find a flow assignment which
satisfies the following three constraints [8]:
The optimization objective inherently enforces
the converse, which states that links with no
traffic can be turned off.
Link power is bidirectional: Both “halves” of
an Ethernet link must be powered on if traffic
is flowing in either direction:
∀(u, v) ∈ E, Xu,v = Xv,u
Correlate link and switch decision variable:
When a switch u is powered off, all links
connected to this switch are also powered off:
fi (u, v) ≤ c(u, v)
Flow conservation: Commodities are neither
created nor destroyed at intermediate nodes.
∀u ∈ V, ∀w ∈ Vv , Xu,w = Xw,u ≤ Yu
Similarly, when all links connecting to a switch
are off, the switch can be powered off. The linearized constraint is:
∀u ∈ V, Yu ≤
fi (u, w) = 0, when u 6= si and u 6= ti
Demand satisfaction: Each source and sink sends
or receives an amount equal to its demand.
fi (si , w) =
fi (u, v) ≤ c(u, v) × Xu,v
Capacity constraints: The total flow along each
link must not exceed the edge capacity.
∀(u, v) ∈ V,
A.3 Flow Split Constraints
fi (w, ti ) = di
Splitting flows is typically undesirable due to TCP
packet reordering effects [21]. We can prevent flow
splitting in the above formulation by adopting the
following constraint, which ensures that the traffic
on link (u, v) of commodity i is equal to either the
full demand or zero:
A.2 Power Minimization Constraints
Our formulation uses the following notation:
Set of all switches
Set of nodes connected to a switch u
a(u, v) Power cost for link (u, v)
Power cost for switch u
Binary decision variable indicating
whether link (u, v) is powered ON
Binary decision variable indicating
whether switch u is powered ON
Set of all unique edges used by flow i
ri (u, v) Binary decision variable indicating
whether commodity i uses link (u, v)
∀i, ∀(u, v) ∈ E, fi (u, v) = di × ri (u, v)
The regularity of the fat tree, combined with restricted tree routing, helps to reduce the number of
flow split binary variables. For example, each interpod flow must go from the aggregation layer to the
core, with exactly (k/2)2 path choices. Rather than
consider binary variable r for all edges along every
possible path, we only consider the set of “unique
edges”, those at the highest layer traversed. In the
inter-pod case, this is the set of aggregation to edge
links. We precompute the set of unique edges Ei
usable by commodity i, instead of using all edges in
E. Note that the flow conservation equations will
ensure that a connected set of unique edges are traversed for each flow.
The objective function, which minimizes the total
network power consumption, can be represented as:
Minimize (u,v)∈E Xu,v ×a(u, v)+ u∈V Yu ×b(u)
The following additional constraints create a dependency between the flow routing and power states:
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