Dynamically Scaling
Applications in the Cloud
Presented by Paul
Problem
•
The cloud offers
Near infinite computing capabilities
o Near infinite storage
o On-demand access to new VMs
o Pay-as-you-go model
o
•
But how do you build an application to
automatically take advantage of those
resources?
[1]
Overview of scaling options
[1]
Horizontal Scaling
•
VM Replication
Duplicate servers and create load balancers to
distribute incoming request
o Most common approach to scaling
o Will look in more detail...
o
•
Network Scalability
o
As an application scales up, the bandwidth
requirements for intercloud communication and app
requests increases, [1] assesses the possibility of
requesting network resources in conjunction with
cloud resources Network as a Server (NaaS)
[1]
Vertical Scaling
•
VM resizing (live)
o
•
VM replacement
o
•
Taking a VM with given processing power, and
upgrading it with more resources while it's still
running
Replacing less powerful VM with more powerful VM
(shutting down original VM)
Might require reboots, and application
tolerance
[1]
"Rule of thumb"
•
CPU-Intensive
o
•
•
Best to load balance and split computation among
many instances
Network intensive
o
Better to use single CPU heavy instance
More network intensive
o
DNS-based load balancing on powerful CPUs
[1]
Decisions, decisions
•
1 Cloud to rule them all?
o
o
•
Do you want to use multiple cloud providers?
Does part of your application require running on
private cloud?
How much control over automated scaling
would you like?
o
o
o
If you don't want any control, maybe PaaS is better
option
Do you simply want horizontal scaling, and will
handle load balancing on your own?
How much $$$ do you have?
Methods for Scaling
•
•
•
•
•
Automatic VM Scaling
Dynamic Workload-pattern Matching
Whole Application Scaling
Non-scalable Load Balancing (e.g. Elastic
Load Balancing)
DNS-based Load Balancing
[1]
Basic: Automatic VM Scaling
•
•
Services that scale based on predefined VMrelated performance metrics
Offered by Amazon and Rightscale
o
•
Amazon Autoscaling comes with Cloudwatch
 Set conditions based on Cloudwatch variables,
react to latency, CPU speed, etc.
What you might imagine for scaling - like a
state machine
o
If conditions a,b,c are met, do x,y,z
[1]
The "Controller"
"Non-Scalable" Load Balancers
•
Amazon offers load balancing service
(Elastic Load Balancing)
o
•
The reason it's "non-scalable" is this is a single load
balancer
Could be used in conjunction with horizontal
scaling
o
Scale up/down the number of instances, configure
load balancer to balance across running instances
Elastic Beanstalk
•
•
•
Automatically scale up your application
depending on services it's using, and
beanstalk controller parameters
o
Only pay for underlying AWS resources
Can upload .NET, PHP/Python, and Java
apps for integration with Elastic Beanstalk
Closer in function to a PaaS
o
Still have access to AWS instances, can interact
with elastic beanstalk controller
Whole Application Scaling
[3]
Whole Application Scaling
[3]
Dynamic Workload-pattern
Matching
[2]
Scalability Controller
Dynamic Workload-pattern
Matching
[2]
Final Observations
•
•
•
You must first decide how many clouds you
want to use
Scaling is still an art
o
At the core, there is always a scaling
controller
o
•
Systems for automated scaling still in their infancy
Either you configure and operate the controller, or
the IaaS provider does (e.g. Elastic Beanstalk)
You pay for automation
o
o
Either with your time or your money
More customized automation == more complexity
Sources
[1] L. M. Vaquero, L. Rodero-Merino, and R. Buyya. "Dynamically
scaling applications in the cloud," SIGCOMM Comput. Commun.
Rev., 41:45-52.
[2] P. Marshall, K. Keahey, and T. Freeman, "Elastic site:Using clouds
to elastically extend site resources," Cluster Computing and the
Grid, IEEE International Symposium on, vol. 0, pp. 43-52, 2010.
[3]R. Buyya, R. Ranjan, and R. Calheiros, “Intercloud: Utility-oriented
federation of cloud computing environments for scaling of
application services.” in ICA3PP 2010: The 10th International
Conference on Algorithms and Architectures for Parallel Processing,
2010, pp. 19–24.
Links for papers
[1]
http://www.cloudbus.org/papers/ScalabilityI
nCloud2011.pdf
[2]
http://www.nimbusproject.org/files/elasticsit
e_ccgrid_2010.pdf
[3] http://arxiv.org/pdf/1003.3920.pdf