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data center efficiency xeon baidu case study .pdf



Nombre del archivo original: data-center-efficiency-xeon-baidu-case-study.pdf
Título: Intelligent Power Optimization for Higher Server Density Racks - A Baidu Case Study with Intel Intelligent Power Technology
Autor: Intel Corporation

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Intelligent Power Optimization for Higher
Server Density Racks – A Baidu Case Study
with Intel® Intelligent Power Technology
Executive Summary
Intel partnered with Baidu.com and conducted a proof of concept (PoC) project
using the Intel® Intelligent Power Node Manager (Node Manager) and Intel® Data
Center

Manager

to

intelligently

optimize

server

performance

and

power

consumption to optimize server density. The engineers implementing the PoC used
Node Manager to identify optimal control points, which became the basis to define
node level power optimization policies. Intel® Data Center Manager (Data Center
Manager) Reference Graphical User Interface was used to manage servers as a
group to carry out rack power policies while minimizing performance impact of all
the managed nodes in the rack. The policies enable increasing rack density and
workload yield even when the whole rack is subject to power limitations.
The PoC was conducted initially on site at Baidu in Q1’ 2008 at Intel-Baidu joint lab
with a configuration close to production configurations running Baidu search
workloads. The runs in the first round were carried out with Bensley generation
platforms. An updated set of runs was carried out in Q1’2009 using pre-production
units with the more advanced Nehalem platform.
The test servers used were provisioned with dual X5560 processors.

These

processors feature eleven ACPI p-states, allowing the frequency to vary from 2.8
GHz to 1.6 GHz instead of the three states available in the Bensley generation. The
additional p-states allow finer grained power control and an expanded power
dynamic range.
Here are some global results:

Intel Confidential

Node Manager POC with Baidu

 For the Bensley generation it was possible to trim power consumption by 40
W running the Baidu search workload while staying within the acceptable
performance boundaries determined by Baidu. For the Nehalem platform the
range has been expanded to 70 watts, allowing Baidu increased operational
flexibility.
 At rack level, up to 20% additional capacity increase could be achieved
within the same rack-level power envelope when aggregated optimal power
management policy is applied
 Comparing with today’s datacenter operation at Baidu, by using Intel Node
Manager and Intel® Data Center Manager, there could be a rack density
increase from 5 to 7/8 servers – a 40%+ improvement

2

Node Manager POC with Baidu

Contents
Executive Summary ................................................................................................ 1
Business Overview .................................................................................................. 4
Top Business Issues ........................................................................................................... 4

Intel Technology and Solution .................................................................................. 5
Intel® Intelligent Power Node Manager (Node Manager) ........................................................ 5
Intel® Data Center Manager (Data Center Manager).............................................................. 5

POC Use Cases ....................................................................................................... 6
POC Architecture .................................................................................................... 7
POC Results ........................................................................................................... 8
Conclusions ........................................................................................................... 9

3

Node Manager POC with Baidu

Business Overview
Baidu is the largest search company in China, accounting for over 60% of search market share in
China. Its market share in the Chinese domestic market has grown steadily in the past few years.
Baidu’s reach extends to international markets, with established branch offices in Japan, the U.S.
and other countries.
Currently Baidu uses A Telco carrier as its data center hosting provider. Baidu is billed by the rack.
Each rack comes with strict power limits of no more than 10 A per rack or 2.2 KW at 220 V.
Because racks are currently power limited, a significant amount of rack space can’t be used without
hitting power envelope limits. Since Baidu is also billed by the rack, Baidu is highly motivated to
maximize the number of servers per rack while staying within the 10 A current limit.
As Baidu grows, the company will eventually operate company-owned data centers. Given their
previous experience, power consumption remains one of the top platform management concerns,
and is not expected to abate even with company operated data centers.

Figure 1: The Baidu.com Search Portal

Top Business Issues
As mentioned above, data center hosting represents a major operational cost for Baidu, costs being
proportional to the number of racks leased from the Telco carrier. Power constraints limit the
number of servers that can be placed in a rack, and a significant amount of space goes unused and
wasted. An easy way to increase the compute yield is to tightly manage the number of servers
within the power envelope. Unfortunately currently there is no accurate means to measure power
consumption against the power limit. Hooking a power meter would only give a snapshot in time in

4

Node Manager POC with Baidu

what is a very dynamic environment. The closest data would be using a derated nameplate figure.
This figure is still overly conservative because it needs to account for the worst case in power
consumption. In other words, there is no dynamic power management technology which allows
Baidu to optimize power utilization. To summarize, Baidu is facing following power management
challenges:


Over-allocation of power: Power allocation to servers does not match actual server power
consumption. Power is typically allocated for worst case scenario based on server
nameplate. Static allocation of power budget based on worst case scenario leads to
inefficiencies and does not maximize use of available power capacity and rack space.



Under-population of rack space: As a direct result of the over-allocation problem, there is a
lot of empty space on racks. When Baidu needs more compute capacity, they have to pay
more for additional racks. The Telco carrier on the other hand, is operating at capacity and
Baidu has leased most of the available racks in Beijing. Available datacenter space is
limiting factor to Baidu’s business growth.



Capacity planning: Baidu does not have means to forecast and optimize power and
performance dynamically at rack level. To improve power utilization, datacenters needs to
track actual power and cooling consumption and dynamically adjust workload and power
distribution for optimal performance at rack and datacenter levels.

Intel Technology and Solution
Intel® Intelligent Power Node Manager (Node Manager)
Node Manager is an out-of-band (OOB) power management policy engine embedded in Intel server
chipsets. Processors carry the capability to regulate their power consumption through the
manipulation of the P- and T-states. Node Manager works with the BIOS and OS power
management (OSPM) to perform this manipulation and dynamically adjust platform power to
achieve maximum performance and power for a single node. Node Manager has the following
features:


Dynamic Power Monitoring: Measures actual power consumption of a server platform within
acceptable error margin of +/- 10%. Node Manager gathers information from PSMI
instrumented power supplies, provides real-time power consumption data singly or as a
time series, and reports through IPMI interface.



Platform Power Capping: Sets platform power to a targeted power budget while maintaining
maximum performance for the given power level. Node Manager receives power policy from
an external management console through IPMI interface and maintains power at targeted
level by dynamically adjusting CPU p-states.



Power Threshold Alerting: Node Manager monitors platform power against targeted power
budget. When the target power budget cannot be maintained, Node Manager sends out
alerts to the management console

Intel® Data Center Manager (Data Center Manager)
Intel(R) Data Center Manager is software technology that provides power and thermal monitoring
and management for servers, racks and groups of servers in datacenters. It builds on Intel®
Intelligent Power Node Manager and customers existing management consoles to bring platform
power efficiency to End Users. Data Center Manager implements group level policies that aggregate
node data across the entire rack or data center to track metrics, historical data and provide alerts
to IT managers. This allows IT managers to establish group level power policies to limit
consumption while dynamically adapting to changing server loads. The wealth of data and control
that Data Center Manager provides allows data centers to increase rack density, manage power

5

Node Manager POC with Baidu

peaks, and right size the power and cooling infrastructure. It is a software development kit (SDK)
designed to plug-in to software management console products. It also has a reference user
interface which was used in this POC as proxy for a management software product. Key Intel®
Datacenter Manager features are:


Group (server, rack, row, PDU and logical group) level monitoring and aggregation of power
and thermals



Log and query for trend data for upto one year



Policy driven intelligent group power capping



User defined group level power alerts and notifications



Support of distributed architectures (across multiple racks)

POC Use Cases
In this POC we focused on use cases to test Node Manager features at node level first. A baseline
test is needed to identify the optimal control points at the node level for Baidu search workload. We
then used these optimal control points as the base for rack level policy definition. A summary of
use cases is listed below:

Use Case Title

6

Description

Get power
consumption on
each server

Using the Intel Node Manager features to dynamically
gather point in time power consumption from each
server on the rack

Estimate total power
consumption of a
rack

Estimate rack level power consumption by summing
up node level power consumption; display on, and
notify console as appropriate.

Optimize rack level
policy within a given
power envelop and
server workload

At rack level, analyze the power consumption of each
server, overall power consumption, rack level power
envelope, and targeted performance goals (utilization,
response time, query queue length, etc.) as well as
other factors important to Baidu to determine the
optimal power distribution policy. Baidu will set the
policy and optimization strategy based on their work
load and priority.

Apply a common
policy to the servers
in a rack

Using Intel Data Center Manager, set policy to each
rack in terms of particular power budget target that
the server has to observe

Node-level
monitoring and
tracking against
policy

Leveraging Node Manager features to adjust server
power consumption to the target set by the policy
within 60 seconds and maintain at the target until
further notice

Node-level alert and
notification

Use Node Manager feature to detect and send alert
when a server fail to reach policy target in 60 seconds
or maintain the target during operation.

Alert handling and
mitigation

Once an alert is received, the console needs to
automatically decide upon a course of action to
mitigate the risk – ignore, set a new policy, or

Node Manager POC with Baidu

shutdown the troubled server, etc.
Note: The last two use cases “Node-level alert and notification” and “Alert handling and mitigation” are not covered in this POC.

POC Architecture
This POC was set up at Intel-Baidu joint lab on Baidu campus. The rig consisted of four Nehalem-EP
based servers used in this POC with 2 Intel Quad-Core Xeon® processors with eleven p-states (2.8
down to 1.6 GHz). Each server was configured with 18 GB of DDR3 memory and PSMI 1.44
instrumented power supplies. The servers are installed on a rack as a server group, managed by
Intel® Data Center Manager, as shown in Figure 2.
The servers in the rack were loaded with the same operating system configuration, Red Hat Linux
Enterprise Version 4.0 Update 3 with customized kernel patches from Baidu. Each server was
configured to run Baidu’s workload stress test which measured the number concurrent queries a
server could process every second. Each server under test also had Node Manager configured.
Intel Data Center Manager was used to see server and rack level actual power consumption data.
The Reference User Interface was used as the group management console. Data Center Manager
monitored the actual power consumption on each server and aggregated total power consumption
for the group (rack level.) Data Center Manager combines instances Node Manager across multiple
servers to set appropriate policies thereby providing a mechanism to optimize power consumption
for each node, yet stay within the constraint of a the rack-level power budget.
For test purposes, we also connected watt meters to the rack and servers under test to monitor the
rack level power consumption as an independent confirmation to the power numbers reported by
the instrumented PSMI power supplies and the aggregated numbers provided by Data Center
Manager. In most cases, there is a small discrepancy between watt meter and Node Manager
figures. Baseline numbers were noted before running the actual tests.
A load runner server was used to generate graduated loads for the servers in the rack. The tool
also had a capability to generate execution statistics from the various workload tests carried out.

7

Node Manager POC with Baidu

Figure 2: POC Architecture

POC Results
System architects wonder about the performance tradeoffs involved when voltage and frequency
scaling are applied to a CPU running a workload. The figure below depicts parametric sensitivity
curves of response times versus power capping.
An initial set of calibration runs to establish asymptotic values for workloads in terms of number of
threads (one query per thread) and response times at full power. The asymptotic query time is
about 0.6 ms. At this point the system is considered fully loaded. Increasing the number of
queries per second beyond this point starts degrading the response times appreciably.
Additional plots were made with reduced number of threads to depict performance sensitivity to the
number of threads. Plots were made at baseline, and 95, 90 and 85 percent of the number of
original queries per second.
Each curve represents the system response time as a function of power capping, from no capping
at all to capping level set at 160 watts while the workload is held constant. Note a gentle
degradation in response time until the power capping level reaches 230 watts.
Baidu set the threshold for acceptable response time at 2 milliseconds. This threshold sets a
practical limit for a power rollback of about 70 watts before the system crosses the threshold.
Counter intuitively, for moderate power capping response time is slightly better for the lower thread
counts plotted. This may indicate some thread processing overhead.
The compression after the cap is set to 180 watts is due to the lower range of power control
authority being reached.

8

Node Manager POC with Baidu

Figure 3: Effect of power capping on search response time

Conclusions
The 70 watt number is a precise actual number for power consumption derived from the
instrumented power supplies in the Nehalem-based servers. This knowledge allows increasing the
number of servers in a rack without exceeding the power limits imposed by the hosting provider.
There are two approaches that we can apply to benefit from the results of this measurement.
As indicated in Figure 4, the hosting provider imposes a 10A current limit per rack or about 10 A x
220 V = 2.2 KW ceiling per rack. The circuit breakers are actually built with 100 percent
headroom. However, servers are provisioned to not exceed the nominal limit of 10 A, and will trip
instantly if the current draw is doubled.
Without the monitoring facilitated by the instrumented power supplies, the rack provisioning
planners need to assume the more conservative figure of a derated nameplate figure, which
amounts to 400 watts per server.
Alternatively, for legacy platforms without power monitoring capability, this number is calculated
through a pre-test measurement, adding extra power draw to account for the worst case from
configuration changes during the server lifetime plus an additional safety margin.
In this baseline case, if the best known figure is 400 watts, this means that a rack cannot be loaded
beyond five servers without exceeding the power quota. For servers with the common 2U form
factor a rack can usually take 20 servers. This means the rack must be left 75 percent empty with
a significant waste of available rack space.
Having the more precise power consumption figures from Node manager, the two strategies to
improve the rack-level power utilization are the following:
-

9

Safeguard strategy: This is a way to set up a safeguard limit for the maximum power
consumption value for a given workload. In other words, we look at the historical record of
actual power consumption readings provided by Node Manager and set a cap at the maximum
power level to minimize power excursions beyond this set limit. This number is still
significantly lower than the derated nameplate number. From the POC, we measured that a
server typically consumes 300W or less. Hence if each server is operated with a Node Manager

Node Manager POC with Baidu

imposed ceiling of 300W, now we can load up the rack with seven servers for a total rack
consumption of 300 watts x 7 = 2.1 KW, or 40 percent increase over the original, more
conservative assumption of 400 watts per rack because we know that the servers won’t
consume more than 300 watts, and even if they do, the 300 watt policy limit will make
excursions very unlikely. We assume that the hosting data center has sufficient thermal
capacity to sustain the extra servers. Because the power limit has been set to the expected
peak power usage, power capping will kick on only very rarely, and no performance impact is
expected.
-

Fine tuned strategy: This strategy requires several experiments of power capping at different
levels yielding results similar to the ones described under the PoC results. The goal of these
experiments is to determine the feasible capping range that still yields acceptable performance.
These experiments take work but will deliver a finely tuned system that not only stays within
the power envelope but also delivers acceptable performance according to preset criteria. This
strategy requires active power capping at all times. For the experiments we performed, we
learned that we could roll back power consumption down to 230W without undue deterioration
in search response times. With power capped at 230 watts, we can now install eight servers in
the rack for a total consumption of 1.9 KW (8x230W). If we set rack-level policy at 1,900W, we
can now install three extra servers in the rack up from the original five. In other words, other
things being equal, rack capacity had been increased by 60 percent.

Figure 4: Different power manager strategies and capacity increases
Looking into the PoC results and subsequent analysis, it is clear that Intel Node Manager and Data
Center Manager allow a significant increase in server density and compute capacity of a rack
through dynamic power management policies. The rack consumption still stays within the present

10

Node Manager POC with Baidu

power envelope with acceptable performance impact. Node Manager Technology provides a
monitoring capability and a defined power consumption ceiling allowing data center managers to
safely increase rack loading between 40 and 60 percent.
The power management approaches described in this document are relatively simple, derived from
an initial study. The results reported in this study are by no means definitive. For workloads of
different characteristics, additional, more aggressive power management approaches are possible
that promise to increase the rack yield even further.

11

Node Manager POC with Baidu

This paper is for informational purposes only. THIS DOCUMENT IS PROVIDED "AS IS" WITH NO WARRANTIES WHATSOEVER, INCLUDING ANY
WARRANTY OF MERCHANTABILITY, NONINFRINGEMENT, FITNESS FOR ANY PARTICULAR PURPOSE, OR ANY WARRANTY OTHERWISE ARISING OUT OF
ANY PROPOSAL, SPECIFICATION OR SAMPLE. Intel disclaims all liability, including liability for infringement of any proprietary rights, relating to use of
information in this specification. No license, express or implied, by estoppel or otherwise, to any intellectual property rights is granted herein.
Intel, the Intel logo, Core 2 Duo, Celeron, and vPro are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United
States and other countries.
*Other names and brands may be claimed as the property of others.
Copyright © 2008 Intel Corporation. All rights reserved.

12


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