New Articles

Enterprise Networking Market: Top Trends Reinforcing the Industry Forecast through 2024

Enterprise Networking

Enterprise Networking Market: Top Trends Reinforcing the Industry Forecast through 2024

According to a recent study from market research firm Graphical Research, the global enterprise networking market size is poised to expand at a substantial CAGR during the forecast period. The outbreak of the COVID-19 pandemic has been shaping the field of networking in various ways, including the emergence of completely remote offices and the development of advanced software solutions for better communication. Consequently, the demand for networking solutions across enterprises and businesses is slated to spiral throughout the world.

The top seven trends powering the enterprise networking industry outlook are as follows:

Expanding Demand for High-Speed Switches in North America

In terms of product, the North American enterprise networking market outlook has been bifurcated into network security, routers, switches, network management, and wireless. The demand for high-speed ethernet switches has been escalating in recent times in accordance with the growing utilization of network virtualization solutions.

During 2017, the market share from the switching segment accounted for more than 25% of the total regional industry. The forthcoming years are poised for considerable growth as the transformation of the enterprise network needs amid the pandemic has led to a higher preference for high-speed ethernet switches. With the proliferation of the 5G network, the demand will propagate further across the region.

North American Enterprises to Recalibrate their Cloud Strategies

Numerous businesses across the world, including those in North America, have been focusing on the recalibration of their cloud strategies as the workplace scenarios have been transforming due to the pandemic. In this scenario, virtual desktops, collaboration, and mobility are embracing the cloud deployment model for enabling a secure and distributed workforce.

The cloud model is increasingly being leveraged not just as an application destination but as a new enterprise management tool because it offers network insights efficiently. It ensures quick access to the latest features as well. This move toward the cloud deployment model is more than likely to stay afloat in the post-pandemic times.

Introduction of Native Cloud Management Platforms in Canada

By 2024, Canada is likely to emerge as one of the leading regional markets of the North American enterprise networking market. Advancing at a 6% CAGR, the regional segment has been registering a remarkable uptick in the volume of cloud service adoption by enterprises.

With the Canadian government utilizing cloud technology for responding to the growing necessity for IT services, private enterprises are turning to advanced cloud strategies. Numerous industry players have been expanding their product and service offerings. For instance, the cloud-based networking company, Extreme, announced the addition of a native cloud management platform located in Canada during December 2020, ensuring better data privacy and sovereignty for large enterprises.

Alarming Rise in Cyber Threats in the U.S.

The dramatic rise in the volume of cyber threats and cyber-attacks amidst the pandemic has been driving enterprises to adopt advanced networking solutions in the U.S. During 2017, the U.S. represented a staggering 70% of the total North America enterprise networking market share.

Clearly, cyberattacks rank as one of the fastest emerging crimes across the U.S., leading to major business disruptions. Recent surveys reveal that most enterprises are susceptible to data loss due to their poor cybersecurity practices and unprotected data. With growing concerns regarding better protection of data, the prospects for the enterprise networking industry in the U.S have improved.

Growing Adoption Across IT & Telecom in Asia

The Asia Pacific enterprise networking market size is slated to expand rapidly over the forecast years. The sector held a market share of more than 30% during 2017 and might make a significant headway by 2024. By 2024, the overall APAC industry share will have reached $20 billion.

The growth in the need for high bandwidth applications has been encouraging enterprises to switch to advanced enterprise networking solutions for addressing the current bandwidth shortage problems. As smartphones, laptops, and tablets become more commonplace with trends such as BYOD (Bring Your Own Device), the enterprises will see a higher adoption even in post-pandemic times.

Cybercriminals Capitalizing on COVID-19 fear in Japan

In Japan, cybercriminals have been capitalizing on the COVID-19-induced fear for luring victims into sophisticated traps, while hackers have been targeting victims via hoaxes and phishing emails. This will fuel the APAC enterprise networking market forecast.

As Japanese companies have been falling victim to unexpected cyberthreats and cyber-attacks, they have been striving to fortify their cybersecurity. In December 2020, the Japanese Ministry of Trade urged enterprises to exercise enhanced leadership with strengthened internal cybersecurity, as the frequency can worsen with the growth in telework.

Rising IoT devices across the Netherlands

The Netherlands enterprise network market is expected to accrue a considerable revenue by 2024, growing at a 10% CAGR through the analysis timeline. The support from government initiatives has been improving cybersecurity across enterprises.

Issues such as the rising phishing through text messaging, misuse of vulnerabilities in Dutch government’s servers, misuse of the ICT infrastructure, and large-scale distributed denial of service (DDoS) attacks are urgently being addressed by enterprises to avoid losses that can have an impact beyond the financial aspects. The considerable addition of numerous IoT devices to the technological infrastructure in the region, promoted by the deployment of LPWAN technology has also been fostering networking growth.


5 Strategies to Reduce Cloud Cost

After initial migration to the cloud, companies often discover that their infrastructure costs are surprisingly high. No matter how good the initial planning and cost estimation process was, the final costs almost always come in above expectations.

On-demand provisioning of cloud resources can be used to save money, but initially, it contributes to increased infrastructure usage due to the ease and speed at which the resources can be provisioned. But companies shouldn’t be discouraged by that. And infrastructure teams shouldn’t use it as a reason to tighten security policies or take flexibility back from the engineering teams. There are ways to achieve both high flexibility and low cost but it requires experience, the right tooling, and small changes to the development process and company culture.

In this article, we present five strategies that we use to help companies reduce their cloud costs and effectively plan for cloud migration.

Lightweight CICD

In one of our recent articles we discussed how companies can migrate to microservices but often forget to refactor the release process. The monolithic release process can lead to bloated integration environments. Unfortunately, after being starved for test environments in the data center, teams often overcompensate when migrating to the cloud by provisioning too many environments. The ease with which it can be done in the cloud makes the situation even worse.

Unfortunately, a high number of non-production environments don’t even help with increasing speed to market. Instead, it can lead to a longer and more brittle release process, even if all parts of the process are automated.

If you notice that your non-production infrastructure costs are getting high, you may be able to reduce your total cloud costs by implementing a lightweight continuous delivery process. To implement it, the key changes would include:

-Shifting testing to the level of individual microservices or applications in isolation. If designed right, the majority of defects can be found at the service-level testing. Proper implementation of stubs and test data would ensure high test coverage.

-Reducing the number of integration testing environments, including functional integration, performance integration, user acceptance, and staging.

-Embracing service mesh and smart routing between applications and microservices. The service mesh can allow multiple logical “environments” to safely exist within the perimeter of production environments and allows testing of services in the “dark launch” mode directly in production.

-Onboarding modern continuous delivery tooling such as to streamline the CICD pipeline, implement safe dark launches in the production environment, and enable controlled and monitored canary releases.

See our previous article that goes into more detail on the subject.

Application modernization: containers, serverless, and cloud-native stack

The lift and shift strategy of cloud migration is becoming less and less popular but only a few companies choose to do deep application modernization and migrate their workloads to containers or serverless computing. Deploying applications directly on VMs is a viable approach, which can align with immutable infrastructure, infrastructure-as-code, and lightweight CICD requirements. For some applications, including many stateful components, it is the only reliable choice. However, VM-based deployment brings infrastructure overheads.

Resource (memory, CPU) overhead of container clusters may be less for 30% or more due to denser packing, larger machines and asynchronous workload scavenging unused capacity.

Containers improve resource (memory, CPU) utilization for approximately 30% compared to VM-based workloads because of denser packing and larger machines. Asynchronous jobs further improve efficiency by scavenging unused capacity.

The good news is that container platforms have matured significantly over the last few years. Most cloud providers support Kubernetes as a service with Amazon EKS, Google GKE, and Azure AKS. With only rare exceptions of sine packaged legacy applications or non-standard technology stacks, the Kubernetes-based platform can support most application workloads and satisfy enterprise requirements.

Whether to host stateful components such as databases, caches, and message queues in containers is still open for choice but even migrating stateless applications will reduce infrastructure costs. In case stateful components are not hosted in container platforms, cloud services such as Amazon RDS, Amazon DynamoDB, Amazon Kinesis, Google Cloud SQL, Google Spanner, Google Pub/Sub, Azure SQL, Azure CosmosDB, and many others can be used. We have recently published an article comparing a subset of cloud databases and EDWs.

More advanced modernization can include migration to serverless deployments with Amazon Lambdas, Google Cloud Functions, or Azure Functions. Modern cloud container runtimes like Google Cloud Run or AWS Fargate offer a middle ground between opinionated serverless platforms and regular Kubernetes infrastructure. Depending on the use case, they can also contribute to infrastructure cost savings. As an added benefit, usage of cloud services reduces human costs associated with provisioning, configuration, and maintenance.

Reactive and proactive scalability

There are two types of scalability that companies can implement to improve the utilization of cloud resources and reduce cloud costs: reactive auto-scaling and predictive AI-based scaling. Reactive autoscaling is the easiest to implement, but only works for stateless applications that don’t require long start-up and warm-up times. Since it is based on run-time metrics, it doesn’t handle well sudden bursts of traffic. In this case, either too many instances can be provisioned when they are not needed, or new instances can be provisioned too late, and customers will experience degraded performance. Applications that are configured for auto-scaling should be designed and implemented to start and warm up quickly.

Predictive scaling works for all types of applications including databases, other stateful components, and applications that take a long time to boot and warm up. Predictive scaling relies on AI and machine learning that analyzes past traffic, performance, and utilization and provides predictions on the required infrastructure footprint to handle upcoming surges or slow downs in traffic.

In our past implementations, we found that most applications have well-defined daily, weekly, and annual usage patterns. It applies to both customer-facing and internal applications but works best for customer applications due to natural fluctuations in how customers engage with companies. In more advanced cases, internal promotions and sales data can be used to predict future demand and traffic patterns.

A word of caution should be added about scalability, regarding both auto-scaling and predictive scaling. Most cloud providers provide discounts for stable continuous usage of CPU capacity or other cloud resources. If scalability can’t provide better savings than cloud discounts, it doesn’t have to be implemented.

On-demand and low-priority workloads

To take advantage of both dynamic scalability and cloud discounts for continued usage of resources, a company can implement on-demand provisioning of low-priority workloads. Such workloads can include in-depth testing, batch analytics, reporting, etc. For example, even with lightweight CICD, a company would still need to perform service-level testing or integration testing, in test or production environments. The CICD process can be designed in such a way that heavy testing will be aligned with the low production traffic. For customer-facing applications, it would often correspond to the night time. Most cloud providers allow discounts for continued usage even when a VM is taken down and then reprovisioned with a different workload, so a company would not need to sacrifice flexibility in deployments and reusing existing provisioning and deployment automation.

The important aspect of on-demand provisioning of environments is to destroy them as soon as they are not needed. Our experience shows that engineers often forget to shut down environments when they don’t need them. To avoid reliance on people, we implement shutdown either as a part of a continuous delivery pipeline and implement an environment leasing system. In the latter case, each newly created on-demand environment will get a lease and if an owner doesn’t explicitly renew the lease it will get destroyed when the lease expires. Separate monitoring processes and garbage collection of cloud resources are also often needed to ensure that every unused resource will get destroyed.

An additional cost-saving measure that we effectively used in several client implementations is usage of deeply discounted cloud resources that are provided with limited SLA guarantees. Examples of such resources are spot (AWS) or preemptible (GCP) VM instances. They represent unused capacity that are a few times cheaper than regular VM instances. Such instances can be used for build-test automation and various batch jobs that are not sensitive to restarts.

Monitoring 360

The famous maxim that you can’t manage what you can’t measure applies to cloud costs as well. When it comes to monitoring of cloud infrastructure, an obvious choice is to use cloud tools. To make the most out of cost monitoring, cloud resources have to be organized in the right way to be able to measure costs by:



-Application or microservice



While the first points might be obvious, the last one might require additional clarification. In modern continuous delivery implementations, nearly every commit to source code repository triggers continuous integration and continuous delivery pipeline, which in turn provisions cloud infrastructure for test environments. This means that every change has an associated infrastructure cost, which should be measured and optimized. We have written more extensively about measuring change-level metrics and KPIs in the Continuous Delivery Blueprint book.

Multiple techniques exist to properly measure cloud infrastructure costs:

-Organizing cloud projects by departments, teams, or applications, and associating the cost and billing of such projects with department or team budgets.

-Tagging cloud resources with department, team, application, environment, or change tags.

-Using tools, including cloud cost analysis and optimization tools, or tools such as, which provides continuous efficiency features to measure, report, and optimize infrastructure costs.

With the proper cost monitoring and the right tooling, the company should be able to get a proper understanding of inefficiencies and apply one of the cost optimization techniques we have outlined above.


Cloud migration is a challenging endeavor for any organization. While it’s important to estimate cloud infrastructure costs in advance, the companies shouldn’t be discouraged when they start getting higher invoices than originally expected. The first priority should be to get the applications running and avoid disruption to the business. The company can then use the strategies outlined above to optimize the cloud infrastructure footprint and reduce cloud costs. Grid Dynamics has helped numerous Fortune-1000 companies optimize cloud costs during and after the initial phases of cloud migration. Feel free to reach out to us if you have any questions or if you need help optimizing your cloud infrastructure footprint.