Scaling in cloud

1. Scaling in the Cloud

2. Index:
• Introduction
• WHAT ISSCALING?
• SCALING IN TRADITIONAL COMPUTING
• SCALING INCLOUD COMPUTING
Scaling in Cloud is Reversible
• FOUNDATION OF CLOUD SCALING
• SCALABLE APPLICATION
• SCALING STRATEGIESIN CLOUD
Manual Automatically
• Proactive Scaling
Proactive cyclic Proactive event- based •
• Reactive Scaling
The Combination
• AUTO SCALING IN CLOUD
Scaling Boundaries
• TYPESOF SCALING
Vertical Scaling
• ComparisonBetween
Vertical and Horizontal Scaling
• HORIZONTAL SCALING ISMORE
CLOUD-NATIVE APPROACH
• PERFORMANCE AND SCALABILITY
• THERESOURCE CONTENTION PROBLEM
CLOUD BURSTING:
A SCENARIO OF FLEXIBLESCALING

3. introduction

4. WHAT ISSCALING?
• The ability of expanding and shrinking of a system as per workload
is known as scaling.
• Dynamic resource provisioning plays a key role in building of a
scalable system but that alone cannot ensure the scaling.
• scaling is defined as the ability of being enlarged (or shrunk)
for accommodating growth (or fall-off) to fulfill the business needs.
A system or application architecture can be termed as scalable if its
performance improves on adding new resources and the
improvement is proportional to the capacity added.

5. Cont…
The point at which a system or application can not
handle additional workload efficiently, any more, is
known as its limit of scalability.
The main reason behind this problem is that the
infrastructure architecture is not dynamic in traditional
computing system, which prevents implementation of
dynamic scaling.
Static scaling requires system shut-down (system to
restart) and hence is avoided unless it becomes
extremely essential.
In the traditional static scaling approach, computing
system requires a ‘restart’ for the scaling effect to take
place which causes service disruption

6. SCALING IN CLOUD
COMPUTING
• Scaling in cloud is dynamic in nature and apart from a few
special cases it is automatic too.
• In dynamic-automatic scaling, the system resource capacity
can be altered while a system is running. Large pool of
virtualized resources promises to adjust variable workload by
allowing optimum resource utilization.
• Offered in transparent manner.
• automatic scaling.
.Infinite scalability is not true.
Even the largest players may at some moment face a
scalability problem if cloud computing usage rate increases
abruptly, beyond anticipation

7. SCALING IN CLOUD
COMPUTING
•Dynamic scaling enables a system to keep performing
consistently during times of massive demand by expanding it
at pace with growing demand.
• is particularly helpful for applications and services to meet
unpredictable business demand.
•Service providers can create the illusion of infinite resources
during service delivery, as cloud consumers remain unaware
about the transparent scaling feature.
•Scaling in Cloud is Reversible.
Implementation of reversible scaling is a critical act
as system performance should not be hampered while
releasing the resources.

8. SCALING IN CLOUD
COMPUTING
•.
•Scaling is one of the attractive attributes of cloud
computing. Scaling in cloud is dynamic in nature and apart
from a few special cases it is automatic too.
• In dynamic-automatic scaling, the system resource
capacity can be altered while a system is running.
• Large pool of virtualized resources promises to adjust
variable workload by allowing optimum resource utilization.

9. FOUNDATIONS OF CLOUD
COMPUTING
Scalable cloud computing system also need:
■ Capacity planning on regular basis and ■ Load balancing

10. SCALABLE APPLICATION
•Application architecture should be well-suited to scale in a
scalable architectural environment.
•Scaling of application depends on two layers which are,
scalable application architecture and scalable system
architecture.
• It is not possible to take full advantage of the scalable
computing infrastructure if the application architecture is
not scalable. Both have to work together to maximize the
gain.

11. SCALING STRATEGIES IN CLOUD
Manual Automatically
In the dynamic scaling approach, a system can be re-sized
during its execution without restarting or interrupting any
service. This critical task of dynamic capacity alteration
can be done in two ways:
■ Manually: when a system can be scaled while running
by executing appropriate commands through the
application interface.
■ Automatically: when this type of scaling of the system
can be implemented through programs that can
automatically adjust system capacity by observing the
actual demand.

12. SCALING STRATEGIES IN CLOUD
Manual Automatically
•The ability of manual capacity adjustment of a system during
its operation is a huge task for any computing environment.
•The dynamic auto-scaling is generally referred as auto-
scaling which is also known as cloud scaling.
•Auto-scaling can be implemented in two different ways:
■ Scaling based on a predefined schedule known as proactive
scaling.
■ Scaling based on current actual demand known as reactive
scaling.

13. Proactive Scaling
•Application demand generally varies with time.
• when the expected increase or decrease of demand is
known a pre-programmed plan is placed to automatically
alter the resource capacity.
•Such scaling strategy that does not wait for workload to
change, rather alters capacity in advance based on a
predefined schedule is known as proactive scaling.

14. Proactive Scaling
Proactive cyclic Proactive event-based

15. Proactive Scaling
Proactive scaling schedules are implemented in two different
ways as
■ Proactive cyclic scaling: This type of proactive scaling
event takes place at fixed regular intervals and by pre-defined
times of the day, week, month or year.
■ Proactive event-based scaling: Major variations in traffic
load may occur due to some scheduled business events like
promotional campaigns or new product launch and else. For
those cases, event-based proactive scaling is the best way out.
•Proactive scaling strategy does not wait for demand to
increase or decrease in expected circumstances. Such standard
situations are handled through pre-defined plans.

16. Reactive Scaling
The Combination
•In this strategy, the system reacts immediately
to changing demand of resources by adding or
removing capacity on its own.
•Reactive scaling should be applied as the last
layer of protection to scale a system and
should not be used unless unavoidable.

17. Reactive Scaling
•The decision is taken based on resource utilization.
Under this scaling technique, depending on a situation where the
suitable parameters are identified at first to activate the auto-
scaling process.
•System scales in response to the changing conditions of those
parameters. This eliminates the need for any pre-scheduled action
to handle scaling as it always remains unknown when those
conditions may change.
• Reactive scaling approach should be seen as a safeguard for
absolutely unavoidable scenarios.
•Too much dependency on the reactive scaling strategy, without
performing appropriate capacity planning to facilitate proactive
scaling of a system may turn suicidal.
•Auto-scaling implementation requires mixture of both reactive
and proactive scaling approaches

18. Classification of Dynamic Scaling
approaches

19. AUTO SCALING INCLOUD
Auto-scaling allows scaling of computing resources
both in the predictable and unpredictable
circumstances. Scaling in these two situations happens
in following fashion:
■ Unpredictably, based on specified conditions and
■ Predictably, according to defined schedule

20. AUTO SCALING IN
CLOUD Scaling Boundaries
Virtual server1 Virtual server2 Virtual server3 Virtual server4
Read load all of the servers
Load Monitoring System Action Module
Chceck Load condition/
Check Pre-defined Schedule
Action
Maintain
Status
Launch
Server
Remove
Server
or
or
User Application A

21. AUTO SCALING INCLOUD
•The monitoring and the checking modules
of auto-scaling unit play vital roles in the
autoscaling process.
• Monitoring module keeps on sending the
load status to the checking module at
regular intervals.
• Checking module decides the appropriate
action depending on the current load status
and stored pre-defined schedules. I

22. TYPES OF SCALING
Vertical Scaling

23. Horizontal Scaling

24. Comparison Between
Vertical and Horizontal Scaling

25. Comparison

26. HORIZONTAL SCALING ISMORE
CLOUD-NATIVE APPROACH

27. PERFORMANCE AND SCALABILITY

28. THE RESOURCE CONTENTION PROBLEM
■ Process speed of application server
■ Memory capacity and speed of application server
■ Speed of the disk I/O operation of database server
■ Network bandwidth

29. CLOUD BURSTING:
A SCENARIO OF FLEXIBLE
SCALING

30. Conclusion
 Scaling is the ability of a system to adjust itself to changing workload.
 Cloud computing offers dynamic and automatic scaling
 Dynamic auto-scaling is implemented in two ways.
 A computing system can be scaled in two ways
 Horizontal scalingvertical scaling
 The infinite scalability feature of cloud computing is achievable only
through horizontal scaling.