Cloud .NET Development Risks: Belitsoft’s Mitigation Strategy

Written by Dzmitry Baraishuk

The Belitsoft custom software development company notes that making decisions about cloud technology without thorough knowledge of architecture or design can lead to problems that worsen over time, especially for businesses that use cheap .NET development vendors to save money in the beginning. This technical debt worsens over time, and it costs businesses three to ten times as much to fix as it did to make the software in the first place. It is common for low-cost .NET developers to create apps that are one big piece (monolithic programs) that cannot grow. Clients have to completely rebuild the software (re-architecture) soon after it comes out because of this. To mitigate these risks, businesses can work with development companies that have specific knowledge of cloud-native architecture.

When a senior technical leader or C-level executive is trying to figure out how to build a complicated system, they are making a mental picture of the vendor to see if they really know what they’re talking about, not just what they say in their sales pitch. They know that outsourcing the first day can lead to years of technical debt, lost revenue, and losing their competitive edge if they make a bad choice.

An effort to build or migrate to the cloud is more complex than a straightforward technical improvement. Budgets may be inflated due to the path’s complexity and numerous business-critical hazards. The first step in reducing these Big Five Risks is to understand them. These five difficulties are not unique. Every solution to one issue may cause or exacerbate another, resulting in a web of trade-offs as they interact and compound one another.

Risk 1: Performance, Downtime, and Latency

Any business that uses the cloud is always worried about problems like high latency, unexpected outages, and slow application response times (performance). Although cloud companies have strong infrastructure, they can still experience issues. Although such problems do not occur frequently, major outages can happen and have severe effects on businesses, and performance can fluctuate.

There is no way to completely eliminate physical distance. If your data center is in London and your user is in Sydney, there will be a lot of latency. This is because it takes a long time for light to move through fiber optic cables that are thousands of miles long. The provider isn’t trying to hide this; the company has to make a choice.

Most of the time, the provider is not to blame for problems with performance. The application architecture is often the real issue, regardless of the technology used; a poorly built application will be slow. A public cloud allows an organization to share its infrastructure. Sometimes, a high-traffic application owned by another client may temporarily slow down the performance of other users on the same physical hardware. Even though the app itself is fast, the user will think it is slow if it is always waiting on a database that is busy or slow.

An acceptable answer combines ideas about how to design applications with ways to manage providers, like due diligence, ongoing monitoring, performance testing, and geo-replication. For real success, you need both good architecture, which makes the application scalable by using microservices and loose coupling, and good management, which keeps an eye on, tests, and selects the right infrastructure, such as geo-replication and the right data center regions, to support that architecture.

Risk 2: The Myth of Scalability

The ability to scale up and down easily is what makes cloud service providers like Microsoft Azure, Google Cloud, and Amazon Web Services so appealing. The intriguing concept is that their systems may instantly and automatically expand or contract to accommodate varying user demands. Although their infrastructure is scalable, this claim gives non-experts the impression that they can simply transfer their current apps to the cloud, and those apps will automatically become scalable.

The issue is that older apps are monolithic, which means they have a lot of different parts. A monolith is a big program that was made to work as one unit. Its parts, such as the user interface, data processing, and user logins, all work together as one large system. If a business simply lifts and moves this monolith to a cloud server, the underlying problem with the program will not be resolved. Its architecture, or internal design, remains inflexible.

The application’s monolithic architecture makes it unable to handle pressure when it is heavily used. The system would crash if one part of the application received too much data, like a monolithic back end that couldn’t handle it, because all of the parts depend on each other. The application itself is the issue, so the robust cloud infrastructure behind it is irrelevant.

A cloud provider does not sell scalability as a service. From the start, the application’s architecture must include scalability as a key feature. This is the result of architecture. To achieve this, the many jobs in the application need to be independent and only loosely connected. This means taking the big monolith apart and making smaller pieces that can talk to each other but don’t need each other to work.

Microservices are the most popular and specialized solution. This means redesigning the app and breaking it up into a lot of small, separate programs called microservices. Instead of one app, a business might have separate services for logging in, paying, and searching. This design can handle a large number of users without any trouble.

A hybrid cloud method is the last option. This method makes the monolith’s old, rigid structure even more flexible by allowing a company to use both public and private clouds, like AWS and the company’s own cloud. This really lets the business choose the best places to offer the right services.

Risk 3: Data and Privacy Safety 

The primary issue is that sensitive data is now stored off-site, which means that a company must trust a third-party, the cloud provider, to keep the data private. 

The web delivery model and browser usage create a large attack surface because any system that is open to the public internet can be attacked. Inadequate identity and access management (IAM), poorly set permissions, and inadequate API security all contribute to the attack surface in the cloud. If you make even small mistakes when managing identification, access restrictions, and compliance with laws such as HIPAA, GDPR, and PCI-DSS, you could have big security problems.

Industries that are regulated should work with a trustworthy partner to set up and use cloud services in a way that is in line with HIPAA, GDPR, and PCI-DSS rules.

Some possible fixes are reverse proxies and SSL encryption, IAM (with multi-factor authentication and least-privilege access), encrypting data both in transit and at rest, keeping detailed logs and monitoring (like SIEM systems), and having backups and a way to get back up after a disaster to protect against ransomware. 

To make cloud security more resilient, you need to do more than just separate workloads, use cloud access security brokers (CASB), prevent data loss (DLP), automate compliance, and respond to incidents in a way that is already built-in.

Risk 4: Vendor Dependency

Vendor lock-in happens when a company relies too heavily on one cloud provider, like Microsoft Azure, Google Cloud, or AWS. It prevents the company’s systems from working with other providers and makes it difficult and costly for them to transfer their data and applications to another location. Approximately three-quarters of businesses are very concerned about this issue.

Organizations choose a certain supplier because its ecosystem offers real benefits, such as better integration of services, easier operations, and faster development of proprietary features. Lock-in becomes a problem only when the provider’s prices increase, the quality of their services deteriorates, or their method of doing business no longer meets the needs of the business.

Cloud pricing models are meant to make it hard to leave. Companies that split up their workloads miss out on important volume-based savings, and multi-year contracts often have high penalties for ending early. Additionally, very high data egress fees, which are fees for moving data outside of the provider’s network, can make it harder for people to move. Businesses also have assets that are tied up in debts they do not want to give up, such as reserved instances or prepaid credits.

Over time, teams also learn more about the platform they use every day and obtain provider-specific qualifications. The technologies from that one vendor are used to create whole operational frameworks, such as systems for monitoring, protocols for responding to incidents, and routines for following rules. Teams naturally become accustomed to and prefer platforms they know, which makes them less likely to want to change. Custom connections are set up to link cloud services to systems within the company.

Containers fix the problem of basic infrastructure that doesn’t always lock businesses in. Serverless computing functions, AI and machine learning platforms, as well as proprietary databases, are what really cause dependency. Even if an application runs in a portable container, it will still be locked in if it needs a database API or AI service that is only available from that provider.

Also, there are costs to trying to completely avoid lock-in. If a business only uses standard services from a supplier, it misses out on the most cutting-edge and creative features. Running a real multi-cloud system is very hard due to extra tools and coordination costs. It usually costs 20-30% more to run.

It is better to use abstraction layers in applications to keep important logic separate from provider-specific services than to not use them at all. This means allowing for strategic lock-in with very useful services while ensuring that important systems can be migrated. Companies should regularly practice moving, even if they do not plan to do so in the near future, to ensure that their teams are still able to do so.

Risk 5: The Project Could Fail and Go Over Budget

The most obvious problem with a failing cloud project is that it costs more than planned. These overruns, though, are often signs of bigger problems that are occurring. Before starting the project, the group did not clearly articulate what it wanted to achieve, how large it would be, or what resources it would need.

Other root causes include when teams have different goals that hinder the project, when managers and employees actively or passively oppose new ways of working, and when the cloud strategy is incorrect (for example, simply moving existing apps to the cloud without redesigning them to take advantage of cloud capabilities). 

The people who work for the company often do not have the technical skills necessary to set up or maintain cloud technology.

A thorough Total Cost of Ownership (TCO) estimate is an important part of making plans. However, a lot of companies make TCO estimates based on incorrect assumptions, such as thinking that optimization occurs immediately or failing to realize the costs associated with paying for unused processing resources and moving data out of the cloud.

The company needs to fill in the gaps in its own skills. Partnering with an expert team, which involves employing an outside business or team of consultants with the requisite experience, is the advised strategy. To build skills within the company, businesses need to use a hybrid strategy that includes both targeted hiring and training programs and selective consulting. FinOps methods, which include ongoing financial operations and cost optimization, are also something they need to use, not just plan ahead.