Figure 2. The coffee shop with patterns applied. Numbers correspond to one of the five cloud application design
phases: (1) decomposition, (2) workload, (3) data, (4) component refinement, and (5) elasticity and resiliency. (Patterns
are derived from our previous work: www.cloudcomputingpatterns.org.)
In IT applications, we often find
a similar impact from workload,
where input complexity is similar for
all requests, but request handling in
the back end has higher complexity.
Assigning all the components of such
an application to one server and scal
-
ing the application by adding servers
is suboptimal under such conditions.
This would equate to building addi
-
tional coffee shops in warmer regions
to handle the higher demand for frozen
coffee products, rather than just adding
more blenders to one coffee shop.
Phase 3: Data (State)
As a prerequisite for later scalabil
-
ity and resiliency considerations, the
location where data are stored and
data consistency are extremely impor
-
tant. This is because the elastic scal
-
ing of resources and their replacement
in case of failures are significantly
affected by state information, which
might need to synchronize with newly
provisioned resources or could be lost
during a resource failure. We differen
-
tiate two types of state.
Session state
is the state of a user’s interaction with
an application — for example, a shop
-
ping basket in an online shop.
Appli
-
cation state
is the data an application
handles, such as the customer data, the
currently processed orders, and so on.
Two patterns characterize the means
by which an application handles these
state types
2
:
•
Stateful component.
Multiple
in
stances of a scaled‐out applica
-
tion component synchronize their
internal state to provide a unified
behavior.
Christoph Fehling, Frank Leymann, and Ralph Retter •
University of Stuttgart
IT applications and physical businesses often face similar challenges: customers
have to be served quickly; throughput and availability should be increased. Here,
the authors cover the architectural design phases of a cloud application and
describe common best practices relevant in each phase. To avoid IT complexity,
they use a coffee shop as a real-world analogy.
T experts have a long‐established relationship
with coffee and related products — you could
even call it love. Coffee shops making our
favorite products are places to relax and work,
but they can also inspire IT architectures.
1
We
can transfer a coffee shop’s efforts at increasing
the amount of processed orders while optimizing
resource use to IT application architectures.
Here, we examine cloud computing patterns,
2
capturing best practices for building and managing
cloud applications. We abstracted these patterns
from existing applications, architecture guidelines,
and documentation of cloud offerings to express
common, underlying architectural concepts. Each
pattern is a similarly structured and human‐read
-
able document describing an abstract solution
to address reoccurring architectural problems in
cloud applications. Patterns avoid provider‐ or
implementation-language‐specific terminology,
thus documenting design knowledge gained from
practice in a provider‐independent form.
Patterns and the Coffee
Shop Analogy
Patterns can describe the following:
•
cloud environments from different providers
and how they’re hosted;
•
the behavior of different cloud offerings and
when to use a certain offering; and
•
how to design applications using these offer
-
ings, and manage them at runtime.
Using patterns, IT architects can compare cloud
providers in terms of those patterns they support,
and can design applications without consider
-
ing the specific offering’s idiosyncrasies too early
in the design process. Furthermore, the design
knowledge captured in patterns can be more easily
applied to different cloud providers.
For fundamental cloud computing patterns, we
show that coffee shops similarly handle the five
design phases of cloud applications: decomposi
-
tion, workload, data (state), component refinement,
and elasticity and resiliency. In other words, you
can experience all the design phases first-hand by
buying a coffee.
Figure 1 shows the coffee shop we consider
as an analogy. Customers entering the shop form
a line waiting to place their orders at a set of
cash registers. Employees take the order, collect
the payment, and write the customer’s name and
desired product on a cup. They then place this cup
on a separate table, where it waits for processing
by baristas handling the coffee machine for regu
-
lar coffee products, such as drip coffee or espresso.
Baristas also operate a set of blenders for frozen
coffee products. After processing, a barista places
the finished product on a second table, where the
customer retrieves it; here, the information on the
cup is used as a correlation identifier.
3
Phase 1: Decomposition
By nature, clouds are large distributed systems pro
-
viding resources for computation, communication,
and storage.
4
Monolithic applications that com
-
prise one component and use a single server for
hosting do not benefit from this distributed envi
-
ronment. Instead, cloud application architectures
must distribute an application’s functions among
IC-18-05-VftC.indd 52
04/08/14 2:14 PM
Images can mislead
the viewer by modifying, inserting or removing objects from the scene.
Many photo editing applications include tools that can remove objects
cleanly from their surroundings with a few clicks. This is known as
inpainting.
An early method was to fill the void left in the image by smoothly
interpolating inwards, based on sampling the pixels at the edge of the
missing area. Other techniques include
seam carving,
content-aware image resizing in which an algorithm establishes the
image's important areas in order to remove or expand sections around
them without affecting the subject of the image.
Read more at:
http://phys.org/news/2014-11-forging-photo-easy-fake.html#jCp
Not
all is rosy. The tech taxi provider is in the line of fire for many
other reasons. Industry watchers would recall Uber and Lyft really go to
war in the US over drivers. Whoever gets more drivers will be able to
meet more demand, and ultimately make more money, was the rule.