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12 BENEFITS AND CHALLENGES OF MULTI-CLOUD ARCHITECTURES

February 19, 2021
9 min read time
Bethany Hendricks
Snapt Nova Multi-Cloud Featured Hero


Gartner, Forrester, and all the major analysts are tracking a huge increase in
interest in multi-cloud. A growing number of CIOs, architects, and IT teams are
either considering or rolling out multi-cloud architectures for key
applications. This is understandable. No one wants to get locked into a single
cloud. Multiple clouds may offer enterprises a better way to deliver
applications closer to their customers and end users. Going multi-cloud,
however, is a complex decision involving multiple trade-offs that every CIO
should consider before launching down that path.


WHAT IS MULTI-CLOUD?

Multi-cloud is when an enterprise uses two or more public cloud computing
services, such as Amazon Web Services (AWS), Microsoft Azure, or Google Compute
Platform (GCP). Multi-cloud can also mean combining public and private cloud
computing assets, such as a virtual private cloud (VPC) running in a public
cloud or a hosted, dedicated private cloud running in your own data center or
hosting environment.


WHY ARE ENTERPRISES ADOPTING MULTI-CLOUD?

Enterprises are adopting multi-cloud environments to more efficiently distribute
computing resources, reduce risks of service interruption or data loss, improve
security and DevOps through the standardization of tools and services not tied
to a specific cloud, and take advantage of best-in-breed services. Multi-cloud
allows architects and CIOs to deliver full N+1 capability across the entire
application tier and lifecycle, simplifying compliance and business risk
management.


THE BENEFITS AND CHALLENGES OF MULTI-CLOUD

A multi-cloud strategy can overcome the limits of redundancy, scale, cost, and
features in a single cloud provider. Running in multiple clouds requires
centralized visibility and control. Otherwise, your environment, resources, and
policy will be fragmented. This creates additional complexity and cost through
the multiplication of configuration, monitoring, and scaling tasks that cannot
be automated centrally.


SIX BENEFITS OF MULTI-CLOUD

There are many clear benefits to going the multi-cloud route, including
proximity, reduced lock-in, data center selection, and the ability to pick the
best service on the market for each aspect of your application.

Here is a quick rundown of the major benefits.


1. PROXIMITY TO CUSTOMERS

While all major cloud providers have dozens of data centers, application
architects want to run their application infrastructure as close to their
customers as possible. Some cloud providers may have data centers that are
closer to key customers or key markets. This is crucial. It means your
application has less distance to travel, which reduces latency and provides a
better user experience. Also, in instances where there are problems with the
global Internet, your application is more likely to continue to perform normally
for your users because it has less exposure to those problems and can deliver
locally quite well.


2. YOU CAN PICK THE BEST DATA CENTER... OR SWITCH

Not all data centers are created equal. Some have newer hardware that may be
more reliable and run cloud applications noticeably faster. Other data centers
may have better connectivity to the global Internet with higher-capacity fiber
links. A third consideration is who are the other tenants in a data center. If
your game application’s EU presence is hosted in the same Ireland data center as
a major pharmaceutical company that runs giant batch jobs of molecular analysis
data in the late evenings, your application performance suffers due to the
“noisy neighbors” phenomenon. All of these factors are usually tribal knowledge,
and the cloud providers refuse to discuss it, but these factors are absolutely
crucial considerations in real application performance. Having a multi-cloud
strategy allows you to pick the best performing or connected data centers for a
specific geography. Even better, it allows you to switch between data centers
should the performance of one data center degrade.


3. BEST-OF-BREED CLOUD SERVICES

Say you have a lot of data that you need to back up, but you don’t care about
how quickly you can access it. And you want to pay as little as possible because
of the high volume of data. Then AWS Glacier might be your best bet. If you are
running production applications in Kubernetes and you need your cloud provider
to be able to bring your applications back up in minutes after failure, then GCP
is likely to give you better performance. Likewise, for machine learning
applications, GCP offers Tensor Processing Units (TPUs), an application-specific
integrated circuit (ASIC) for processing artificial intelligence applications.


4. BEST-OF-BREED SERVICES OUTSIDE THE CLOUD

The best service or tool for what you need may not exist inside of a single
cloud. And if a cloud provider offers it, rarely will a tool or service work
across competing clouds. In addition, when you are building an application for a
single cloud, the easy choice is to adopt all the tooling and services found on
the cloud for your application—even if these are not the best in the market. For
example, all cloud providers have their own tools for providing visibility into
application performance, CPU and RAM usage, and data storage. They even have
different tools for different applications. For instance, GCP has StackDriver
for Kubernetes logging and monitoring, while Amazon pushes users toward
CloudWatch. These monitoring tools are limited and less flexible than more
advanced monitoring or logging systems that DevOps teams build themselves quite
easily with Prometheus and Grafana, which is quickly becoming the industry
standard for monitoring and dashboarding. Another example: automation
capabilities differ greatly from cloud to cloud, and some clouds integrate more
tightly or better with specific DevOps tools for continuous integration and
delivery (Jenkins, Gerrit, Salt, CircleCI, etc.). With a multi-cloud strategy,
you can pick your favorite services and tools to run across all your clouds.


5. RESILIENCE AND FASTER RECOVERY

Many organizations that run multi-cloud systems create replicas of the entire
application delivery, data tier, and compute tier across multiple clouds. This
affords them N+1 or N+2 resilience and rapid failover in case one cloud goes
down (such as when GCP was hit with an extended blog outage in August 2020 or
when AWS allegedly lost customer data after a power outage fried hardware in a
data center in 2019). If you elect to keep the application warm and running in
multiple clouds, then failover can happen almost instantly and recovery from a
potentially catastrophic failure is swift.


6. REDUCED VENDOR LOCK-IN

Running an application in a single cloud locks you into what might be expensive
business decisions. For example, in 2020, one of the major cloud service
providers roughly doubled the price of one of their commonly used relational
databases. For customers with data on that platform who had architected their
application to run on that database, this represented an eye-popping bump in
their infrastructure costs. Another risk of lock-in is if a cloud provider
elects to reduce the emphasis on a service or product and its feature sets don’t
keep up with the market. In this situation, moving your application out of that
service may be too expensive or time-consuming, but you will be trapped with an
inferior offering over time. Worst of all, a cloud provider may elect to
end-of-life (EOL) a product or service. While providers always offer early
notification of any EOL processes, dealing with a shutdown of a key service for
your application can introduce unpleasant stress and complexity.


SIX CHALLENGES OF MULTI-CLOUD

There are also several negative impacts that, if not properly managed, can
result from a switch to multi-cloud. Increased complexity, lack of
compatibility, and challenges with uniform security and monitoring are all
common when an enterprise elects to go multi-cloud. Here is a rundown of some of
the potential problems.


1. INCREASED COMPLEXITY

One of the hidden costs of multi-cloud computing can be increased complexity.
Each cloud provider has its own conventions, commands, and ways of doing things.
For example, AWS has three types of load balancers, while GCP has six, each
performing different aspects of load balancing. Some work at Layer 7 and others
at Layer 4. One of the GCP products has an integrated firewall, which means it
must be managed differently. Deciding how to load balance and secure your
application in one cloud is complex enough, given the multiple offerings. Doing
so across multiple clouds introduces escalating complexity. Multiply this
complexity across numerous services, and your organization could quickly be
overwhelmed.


2. DIFFERENCES IN SERVICE CAPABILITIES ACROSS CLOUDS

Some clouds are better than others for different things. We talked about that in
the best-of-breed section above. At a more granular level, different clouds have
very different capabilities, even in core offerings, such as computing, storage,
and networking. For example, in theory, Kubernetes should be managed similarly
across all clouds. In practice, each major cloud provider has key differences in
how they manage Kubernetes, the monitoring and security tools for these managed
offerings, and performance levels. So even when a service is, on the face of it,
exactly the same and built on the same core technology, there may be key
differences underneath that can have a real impact on performance, resilience,
and how you architect an application.


3. HARDER TO MANAGE COSTS

Cloud provider pricing has become harder and harder to understand and manage
over time. Cloud providers charge for differences in computing, storage,
networking, memory type (SSD vs spinning disk), region or zone, data transit,
and number of requests. Let’s just consider load balancing. Most cloud providers
charge not only for the size of the load balancer instance but also for the type
of instance (spot or reserved), how many requests an instance will handle per
server per second, and whether the load balancer is moving data within a region
or across regions, and how many rules a load balancer can apply to moving data.
Charges for all of these aspects vary from cloud to cloud, and definitions of
services do not even match up perfectly. For example, in GCP, there are
different tiers for networking, which do not exist in Azure. Managing and
forecasting costs in one cloud are challenging but often more addressable by the
providers’ management and projection tools. Across clouds, all of this breaks
down, in particular, if you are shifting your application infrastructure, data,
or any other element between clouds regularly to try to arbitrage costs.


4. SLOWER TIME-TO-MARKET ON MAJOR APPLICATION CHANGES

Deploying an application on multiple clouds can slow down shipping new features
and functionality because you need to thoroughly test the changes in each cloud
environment. In theory, the use of comparable applications at each tier running
on containers should mitigate this risk. In practice, even containerized
applications perform differently on different clouds. For mission-critical
applications and capabilities, you need to budget time and expect more effort
for testing.


5. INCREASED SECURITY RISK AND ATTACK SURFACE

Increased complexity can lead to increased security risk. In a multi-cloud
environment, your security team will need to monitor twice or three times as
many services running in different clouds. This makes it easier for attackers to
disguise their attacks and go unnoticed. Security teams must also configure and
test 2x or 3x more security appliances and tools. This creates a lot more stress
on already overloaded security teams and increases the likelihood of human error
resulting from a misconfiguration or a missed update. DevOps teams dealing with
multi-cloud may get frustrated with the complexity and create workarounds that
increase the attack surface area and add risk. In addition, data moving between
clouds means more exposure and an increased attack surface.


6. DOES NOT ADDRESS BEST-OF-BREED LOCK-IN

If you treat multi-cloud as a true N+1 solution with complete and completely
separate application replicas running in multiple clouds, then lock-in risk is
addressed. However, many enterprises adopt a best-of-breed strategy for
different aspects of cloud computing. They may have AWS as their primary data
store, for example, or GCP as their primary compute cloud for ML applications.
It can be worse than that. You may have one team running its ML work in GCP and
another in Azure. This papers over lock-in by fragmenting it at the service
level.


HOW MULTI-CLOUD WITH SNAPT NOVA SOLVES THE CHALLENGES

A multi-cloud application delivery and security solution, such as Snapt Nova,
provides the centralized visibility and control necessary to get the benefits of
a multi-cloud strategy. Nova reduces complexity and cost by enabling seamless
multi-cloud redundancy, autoscaling, monitoring and optimization, and
“least-cost routing,” all from a single dashboard.

 * In a multi-cloud deployment, you must enforce security policy in every cloud
   and limit your exposure to threats. Nova allows you to set policy centrally
   and propagate it to every node in every cloud simultaneously. This radically
   simplifies life for your security team and reduces the time spent manually
   managing rules and policies across clouds. Nova also improves security by
   reducing the attack surface and providing integrated security capabilities,
   such as firewall (WAF), threat intelligence data, and signatures collected
   from a global network of deployed ADC.
 * In a multi-cloud deployment, you need to monitor the health and performance
   of every cloud and your application performance as a whole. Nova provides a
   holistic view of your network and applications in a single pane of glass,
   regardless of location, environment, or infrastructure. This reduces the
   complexity of building and maintaining a monitoring environment and delivers
   clear apples-to-apples performance comparisons of different clouds.
 * In a multi-cloud deployment, you must ensure seamless failover between clouds
   to achieve the highest redundancy and disaster mitigation. Nova monitors the
   health and availability of every cloud in your application architecture.
   Within five minutes or less, Nova can redeploy resources and reroute traffic
   to a healthy cloud in the event of a failure.
 * In a multi-cloud deployment, you need to scale in and scale out while
   controlling costs. Nova can autoscale resources across multiple clouds to
   meet demand and target the lowest cost provider in real-time. Nova has no
   “warm-up” period and can scale up instance sizes in minutes in the event of a
   demand spike, outage, or security incident.

To be clear, an application services platform like Snapt Nova cannot address
every challenge when going multi-cloud. For example, the risks carried by a
best-in-breed approach can be mitigated only by careful planning and
organization.

That said, Snapt Nova does address the majority of multi-cloud challenges by
reducing complexity and cost and by improving security and control over
applications, no matter whether you want to deploy in public clouds or your own
data center on VMware.

Snapt gets you closer to the freedom and flexibility of cloud agnosticism with
minimal pain.

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