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NETE4631Managing the Cloud

and Capacity Planning

Lecture Notes #8

Lecture Outline Managing the cloud

Administrating the cloud Managing responsibilities Lifecycle management Emerging cloud management standards

Capacity Planning Steps for capacity planner Scenario Load testing Resource ceiling Scaling

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Administrating the Cloud

Network management systems are often described as FCAPS (ISO) Fault/ Configuration/ Accounting/

Performance/ Security Fundamental features

Administrating/ Configuring / Provisioning of resources, Enforcing security policy, monitoring operations, Optimizing performance, Policy management, Performance maintenance, etc.

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Administrating the Cloud (2)

Network management framework tools BMC ProactiveNet Performance

Management HP OpenView/ HP manager products IBM Tivoli Service Automation

Manager CA (Computer Associates) Unicenter Microsoft System Center

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Administrating the Cloud (3)

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Management Responsibilities

What is different from traditional network management? Cloudy characteristics

Billing is on a pay-as-you-go basis. The management service is extremely scalable. The management service is ubiquitous. Communication between the cloud and other

systems uses cloud networking standards. The type of Cloud affects which tools for

monitoring Level of controlling aspects of operations –

IaaS>PaaS>SaaS

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Management Responsibilities by service model types

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What to be Monitored for Cloud?

End-users services such as HTTP, TCP, POP3/ SMTP, etc.

Browser performance on the client Application monitoring in the cloud such as

Apache, MySQL, and so on Cloud infrastructure monitoring of services

such as Amazon Web Services Machine instance monitoring where the

service measures processor utilization, memory usage, disk consumption, queue lengths, etc.

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Lifecycle Management

Six different stages in the lifecycle The definition of the services as a template for

creating instances Client interactions with the service, usually through

an SLA (Service Level Agreement) The deployment of an instance to the cloud and

the runtime management of instances The definition of the attributes of the service while

in operation and performance of modification of properties

Management of the operation of instance and routine maintenance

Retirement of service

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Cloud Management Products

Very young industry List of products -> Chapter 11 of Course Book

Core management features Support of different cloud types Creation and provisioning of different types of

cloud resources such as machine instances, storage, or staged applications

Performance reporting including availability and uptime, response time, resource quota usage

The creation of dashboards that can be customized for a particular client’s needs

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Example - CloudKick

www.cloudclick.com 11

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Emerging Cloud Management Standards

Distributes Management Task Force (DMTF) An industry organization that develops

industry system management standards for platform interoperability

Create a working group to help develop interoperability standards for managing transactions between and in public, private, and hybrid cloud systems

Describing resource management and security protocols, packaging methods and network management technologies.

Distributes Management Task Force (DMTF)

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Emerging Cloud Management Standards (2)

Cloud Commons Initiated by CA and donates to Software

Engineering Institute (SEI), CMU, USA Establishes cloud-based metrics for

file creation and deletion/ Email availability/ console response time/ storage and database benchmark

Using dashboard called CloudSensor to monitor cloud-based services in real time

Cloud Commons

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Capacity Planning

Capacity Planning Match demand to available resources Identify critical resources that has

resource ceiling and add more resources to remove the bottleneck of higher demands

Not focus on performance tuning or optimization

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Steps for Capacity Planner

Iterative process with the following steps Examine what systems are in place (characteristics) Measuring their workload for the different resources in

the system: CPU, RAM, disk, network and so forth Load the system until it is overloaded, determine when

it breaks, and specify what is required to maintain acceptable performance/ what factors are responsible for the failure (resource ceiling)

Determining usage pattern & predict future demand Add or tear down resources to meet demand

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Scenario

Example (LAMP) Capacity planner

works with a system that has a website on Apache

Also, a site has been processing database transactions (MySQL)

Application-level metrics

Page views (hits/s) Transactions (trans/s)

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Scenario (2) System-level metrics

What each system is capable of How resources of such a system affect

system-level performance Example

A machine instance (physical or virtual) CPU Memory (RAM) Disk Network Connectivity

Measured by tools such as sar command/ Microsoft task manager/ RRDTool for Linux

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RRDTool

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Load Testing Load testing seeks to answer the following question.

What is the maximum load that my current system can support?

Which resources represent the bottleneck in the current system that limits the system’s performance? (resource ceiling)

Can I alter the configuration of my server in order to increase capacity?

How does this server’s performance relate to your other servers that might have different characteristics.

Tools HTTPerf, Siege, Autobench, IBM Rational Performance Tester,

HP LodeRunner, Jmeter, OpenSTA

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Resource Ceiling (1)

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Resources Ceiling (2)

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Network Capacity Three aspects to assessing network

capacity Network traffic to and from the network

interface at the server (physical or virtual) system utilities (I/O), Network monitor (traffic)

Network traffic from the cloud to the network interface

Tools such as those from Apparel Networks Network traffic from the cloud through your

ISP to your local network interface The connection from the backbone to your

computer (through ISP)

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Scaling

Scale vertically (scale up) Add resources to a system to make it powerful A virtual system can run more virtual

machines (operating system instance), more RAM, faster compute times

Example – rendering or memory-limited apps Scale horizontally (scale out)

Add more nodes to remove I/O bottleneck Easy to pull resources and partition Example – web server apps

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Scaling Comparison Cost

Scale up pays more than scale out. Maintenance

Scale out increases the number of systems you must manage.

Communication Scale out increases the number of

communication between systems. Scale out introduces additional latency

to your system.

References

Chapter 6, 11 of Course Book: Cloud Computing Bible, 2011, Wiley Publishing Inc.

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