The document discusses adaptive computing as a method that optimizes resource utilization, focusing on quality of service, fault tolerance, and monitoring, particularly in virtualized data centers. It describes the capabilities of PlateSpin Orchestrate in automating workload management, including a real-world scenario for VM migration based on host load, and introduces solutions for dynamic thermal load balancing to address cooling challenges in data centers. Ultimately, it highlights PlateSpin Orchestrate's role in providing policy-based automation for real-world adaptive computing functions.

1. Adaptive Computing Using
PlateSpin Orchestrate ®
Gryphon McArthur
Software Engineer Consultant
gmcarthur@novell.com

2. Abstract
Adaptive Computing:
Goes beyond just intelligently utilizing available resources; it
encompasses quality of service (QoS) targets, fault tolerance
(high availability), monitoring, and iterative analysis of the
resulting dataset to determine what corrective measures
(adaptations) should occur at any given moment. As
virtualization becomes widespread in the data center, the
need for automating the placement and configuration of
workloads (virtual machines) using an adaptive computing
model becomes vitally important. This session demonstrates
how to use events, introduced in PlateSpin Orchestrate
®
2.0.2, to create rules that trigger workload provisioning,
migration, and other virtual machine life-cycle operations.
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3. Overview
Overview of PlateSpin Orchestrate ®
Adaptive Computing and IWM
A Real-world Scenario
Steps to implement the solution
Advanced Workload Management
Thermal load balancing
Conclusion
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4. Overview of PlateSpin Orchestrate ®
Orchestration Server
VMHost systems running the Orchestration Agent
Graphical clients for:
Virtual machine management
Development of policies, jobs, etc.
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5. Adaptive Computing
Definition of Adaptive Computing:
“Adaptive computing focuses on the methodology and
implementation of systems that adjust to different situations.
An adaptive system may change its own behavior to the
goals, tasks, interests, and other features of individual
users and the environment. Adaptivity is important for
ubiquitous and pervasive computing.”
Helsinki Institute for Information Technology
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6. Intelligent Workload Management
Intelligent Workload
Management enables IT
organizations to manage and
optimize computing resources in
a policy-driven, secure and
compliant manner across
physical, virtual and cloud
environments to deliver business Intelligent
services for end customers. WORKLOAD
Management
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7. Scenario/Goal
Automatically migrate VM's off of a VM host once the
host's load exceeds 75% capacity for more than 5 minutes.
Constantly monitor/evaluate VM host loads
Migrate workloads to other hosts when load is too high
Allow workloads to return once load returns to normal
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8. Implementation
Create and Configure Objects using the “Development Client”
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9. Implementation Steps
Step #1: Setup monitoring on VM hosts
Automatically configured when installed with
PlateSpin Orchestrate agent
®
Step #2: Create an RRD metric definition
Use the “load_one” metric with a 5 minute aggregation period
Step #3: Create an event
Step #4: Create the job for handling the event
Step #5: Create a schedule and trigger
for the event created in step #3 and the job created in step #4.
9 © Novell, Inc. All rights reserved.

10. Deploying RRD Metrics Definition
Metric Editor in the “Development Client” GUI
Defining a “load_one” metric (RRD data aggregation)
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11. Creating the Event
Event Editor in the “Development Client” GUI
Defining policy/constraints for triggering a migration
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12. Creating the Job
Job Editor in the “Development Client” GUI
Using JDL to migrate VM's from a VM host
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13. Connecting It All Together
Using the “Development Client” GUI
– Create the schedule
– Create the trigger
– Select the event
– Associate the job
Optionally:
– Put everything in a single “.job” file so it all
deploys automatically
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14. Creating the Schedule/Trigger
Scheduler View in the “Development Client” GUI
Allows the “migrate” job to be invoked by the event
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15. Example in Action
VM Hosts View in the “Development Client” GUI
Workloads are migrated away until load returns to “normal”
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16. Advanced Workload Management
Thermal Load Balancing
A “proof of concept” created by Adam Spiers
Addresses the problem of “hot spots” in the data center
using a novel software-based solution.
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17. The Problem: Data Center Cooling
• Cooling is expensive
– In a typical data center, more power is spent on cooling than
on servers. Estimates range from 44%1 to as high as 63%.2
• Cooling is unavoidable
– 10% of racks have ambient temperatures of 75°F or higher at
the air intake at the top of the rack. High temperatures are
causes of decreased hardware reliability. Intermittent ghosts
and outright hardware failures are three times more prevalent
in the top third of racks than the bottom two-thirds.3
• Air conditioning units are critical to service availability
4
– Servers can redline within 90 seconds of an AC unit failure.
1
See, Simon. Is there a pathway to a Green Grid? Sun, 2008 http://www.ibergrid.eu/2008/presentations/Dia%2013/4.pdf
2
http://h20331.www2.hp.com/ERC/cache/438048-0-0-225-121.html
3
Sullivan, Robert F., Ph.D. Reducing Bypass Airflow Is Essential for Eliminating Hotspots. Upsite Technologies http://www.42u.com/data-center-hot-spots.htm
4
Sharma, Ratnesh et al. Balance of Power: Dynamic Thermal Management for Internet Data Centers. HP Labs, 2003
http://www.hpl.hp.com/techreports/2003/HPL-2003-5.pdf
17 © Novell, Inc. All rights reserved.

18. The Problem: Data Center Cooling
(Continued)
• Many data centers cool incorrectly
– Cooling over-capacity is very common, and is not a predictor of
successful cooling. In one study, nineteen rooms studied ran on
average 2.7 times more cooling equipment than required to cool
the computer heat load. Two rooms ran 16 times more cooling
than required, yet one had 20% hot racks/cabinets and the
other had 7% hot racks/cabinets.1
• There are over 80 energy efficiency incentive or
rebate programs offered by local utilities or state
energy efficiency programs in the US alone.2
1
Sullivan, Robert F., Ph.D. Reducing Bypass Airflow Is Essential for Eliminating Hotspots. Upsite Technologies http://www.42u.com/data-center-hot-spots.htm
2
The green data center. IBM, May 2007 http://www-900.ibm.com/cn/systems/migratetoibm/pdf/Energy-file03_OIW03002USEN.pdf
18 © Novell, Inc. All rights reserved.

19. A Novel (Novell ) Solution: ®
Dynamic Thermal Load Balancing
• Virtualization and live migration allows dynamic
relocation of workloads with no impact on service
– Policy-based migration of VMs from hot spots to cool spots
• Energy consumption can be reduced by more
than 14% by intelligent workload placement.1
• Risk of service outage due to computer room air
conditioning unit failures can be mitigated by
migrating workloads away from the failed unit.1
• Automatic VM live migration based on thermal
policies can be implemented easily using
PlateSpin Orchestrate from Novell.
®
1
Sharma, Ratnesh et al. Balance of Power: Dynamic Thermal Management for Internet Data Centers. HP Labs, 2003
http://www.hpl.hp.com/techreports/2003/HPL-2003-5.pdf
19 © Novell, Inc. All rights reserved.

20. Thermal Load Balancing
Cooling Units
“Rack” of VM host
hardware operating
below capacity
VM host hardware
operating at or
near capacity
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21. VM Migration
VM Hosts View in the “Development Client” GUI
Workloads are migrated away from the “hotspots”
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22. Conclusion
PlateSpin Orchestrate provides the policy based data
®
center automation capabilities needed to implement
real-world adaptive computing scenarios
Orchestrate provides a set of tools and extensible
framework for implementing your own unique solutions
This is just one component of Intelligent
Workload Management
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23. Questions and Answers

25. Unpublished Work of Novell, Inc. All Rights Reserved.
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