Cloud services play a vital role in
overall implementation of cloud infrastructure. Here performance measurement
and is mostly used to retrieve details of current status and to identify scope
of improvement to provide reliable cloud services. Values of parameters related
to return on investment and financial value estimate both are playing curtail role
in the stage for a strong benefits reliable process. The main parameters which are
highly associated can be identified and the same can be reviews and its results
are being used for verification different organization that has actually
adopted the new processes and is delivering the targeted result. If not, then
once again parameters of various resources can be focused to find the scope of
improvement or related problem and can
be resolved by taking required actions to take in range before the
financial results come in.
words: Cloud services, performance, testing,
SLA, service indicator
Minimum acceptable level of cloud
service can be defined with reference to various landmarks and its related
benchmarks. It helps to build confidence in cloud service consumer community
and also attracts related people to migrate from their existing implementation to
the cloud service framework. It is difficult to immediately tie business
finance outcome to a particular business migrate requirement, the model built
for the business review comes in the picture. Directly measured main performance
related parameters shows improvements and can be converted to estimates of the
value in the income statement and balance sheet.
It not only requires rigorous study
of related parameters associated with cloud service performance modeling but
also analysis of macro performance parameters which effects cloud service
performance. The goal is to simply demonstrate a model that should provide believable
rough route map with its related approximate values to achieve expected
performance level of cloud services.
analysis is a subset of performance engineering. Analysis of performance testing
leads us to measure application’s quality of service based on actual
application’s actions. In the span of last few years, cloud
computing has evolved into a rapidly growing sector with an ability to handle
large volumes of data in an impeccable manner, while ensuring enhanced
performance and scalability.
Cloud service performance evaluation
testing focus the application has to be tested for various features like availability,
security, scalability, fault tolerance etc. while it is being hosted on the
Cloud. It has to be evaluated for system throughput, latency, the number of
parallel users using the app, and the speed under different load conditions
along with other performance metrics. The bugs and issues need to be detected
and fixed suitably before the app goes live. The cloud performance testing relies
that the cloud service runs efficiently and shall remain available at critical
Done aptly, this considerably
reduces costs and risks and requires lower maintenance. Data backup, disaster
recovery, and business continuity is far simpler and less expensive with the
cloud. Moreover, vast amounts of data can be provisioned in no time, further
offering enhanced scalability, productivity, and performance.
a Cloud Performance Testing Strategy:
It is important to find the right strategy for conducting
performance testing in the cloud. According to various literature surveys different
techniques were proposed with different constraints but it is defined for
specific criteria only. The project environments, business drivers, technology
stack, acceptance factors, skill set and resource availability are some of the
factors that should be taken into account before building up a strategy.
Also, the ease of infrastructure access, cost savings,
shorter cycle times, and the cloud type must be synced well with the strategy. Here
various customized test should be generated to consider effective tests of such
cloud service applications, and different implications of testing on a public
cloud against a private cloud must be taken into account. It should also be
considered that there are performance tests that are not completely applicable
in a Cloud set-up, and they should be dealt with accordingly.
Let’s focus on typical tests applicable in the context of
cloud, and the strategies that should be applied:
Load Testing: In
case of the public cloud platform, information should be extracted from the
cloud provider on the load statistics of the other customers sharing the
platform to get a deep insight into the kind of response time that can be
expected. It should be considered that the response time in a cloud environment
may differ from the time taken in a non-cloud environment.
The tests must be run repeatedly when the load is expected
to be high to get a range of response times — to establish minimum, maximum and
average response time. This shall be helpful in mimicking real-time load
situations and keeping a track on the app’s response time.
2. Stress Testing: Stress tests confirm the performance
characteristics of the system or app under test when it is subjected to
conditions past the anticipated ones during production operations. It
determines the maximum load the system may handle before it breaks.
As the public cloud is shared by multiple users unlike
private cloud, hence, testing in public cloud requires robust planning and
execution. A test plan should be prepared with clearly defined goals, and tools
must be evaluated by measuring their effectiveness in materializing the goals.
3. Scalability and Elasticity
Testing: Scalability allows
expansion in SLA. An additional load can be managed by considering important
feature namely elasticity. Its testing ensures if the performance is in synchronization
with the given SLA, and is additionally scalable up to required expected level.
It should be conducted by gradually increasing the load to
cross the threshold of the current limit, to verify if the system scales up as
the load increases and scales down as the load decreases. The boundary value
analysis testing technique might come handy to help determine the threshold.