Skip to main content

Hybrid Cloud is Mainstream and Performance Management Software is Essential

Shamus McGillicuddy

Enterprises are aggressively deploying hybrid cloud infrastructure today, and the majority of them are deploying new network performance management software to support it.

Enterprise Management Associates (EMA) recently published its biannual network management megatrends research report, Network Management Megatrends 2016: Managing Networks in the Era of the Internet of Things, Hybrid Clouds and Advanced Network Analytics. For this research we surveyed 150 North American enterprise network management professionals, and as the title makes obvious we asked them how they are impacted by hybrid clouds, among other megatrends.

Thirty-five percent (35%) of them said that their organizations have completed a production hybrid cloud deployment. Another 35% said they are in the process of deploying a hybrid cloud today and 15% said they plan to go into production within 12 months. EMA defined a hybrid cloud as a combination of internal, private cloud infrastructure and external, public cloud infrastructure, with orchestration and connectivity across the two.

Performance monitoring is a critical technology for enterprises that are deploying or operating hybrid clouds. We asked them to identify which networking technologies they are adopting to support these environments. The number one response was network virtualization overlays (e.g. VMware NSX). The second most popular response was network performance monitoring and management software (51%).

We also asked research participants to identify the networking challenges they are facing with hybrid cloud. The number one challenge (30%) was the complexity of provisioning interconnections between public and private cloud environments. However, several other challenges emerged as nearly as common. For instance, 26% said they lacked end-to-end, multi-site network visibility and troubleshooting, and 26% said they struggled with network latency between internal and external cloud resources. Finally, 23% said that their applications were not properly architected for hybrid cloud networks. These latter three challenges point to potential application performance problems. Enterprises who are implementing hybrid clouds will need to evaluate the readiness of their performance management to support these new cloud environments. Many of them also clearly need to evaluate their application architectures before they migrate.

EMA's 2016 network megatrends research explored a number of other interesting topics that IT professionals will find informative. For instance, we found that the vast majority of network management professionals are supporting an Internet of Things (IoT) initiative. The research explores the technologies they are adopting, the use cases they are pursuing, and the challenges they are facing with IoT.

Nearly half of enterprises are applying advanced analytics to network data. And more than half are outsourcing some aspects of network management to a managed services provider. Our research explores both of these trends in depth. It also examines enterprises' evolving requirements of network management products and the overall challenges to and benefits of network operations success.

To learn more about this research, attend EMA's free research highlights webinar and download the full report.

Shamus McGillicuddy is Senior Analyst, Network Management at Enterprise Management Associates (EMA).

Hot Topics

The Latest

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...

Hybrid Cloud is Mainstream and Performance Management Software is Essential

Shamus McGillicuddy

Enterprises are aggressively deploying hybrid cloud infrastructure today, and the majority of them are deploying new network performance management software to support it.

Enterprise Management Associates (EMA) recently published its biannual network management megatrends research report, Network Management Megatrends 2016: Managing Networks in the Era of the Internet of Things, Hybrid Clouds and Advanced Network Analytics. For this research we surveyed 150 North American enterprise network management professionals, and as the title makes obvious we asked them how they are impacted by hybrid clouds, among other megatrends.

Thirty-five percent (35%) of them said that their organizations have completed a production hybrid cloud deployment. Another 35% said they are in the process of deploying a hybrid cloud today and 15% said they plan to go into production within 12 months. EMA defined a hybrid cloud as a combination of internal, private cloud infrastructure and external, public cloud infrastructure, with orchestration and connectivity across the two.

Performance monitoring is a critical technology for enterprises that are deploying or operating hybrid clouds. We asked them to identify which networking technologies they are adopting to support these environments. The number one response was network virtualization overlays (e.g. VMware NSX). The second most popular response was network performance monitoring and management software (51%).

We also asked research participants to identify the networking challenges they are facing with hybrid cloud. The number one challenge (30%) was the complexity of provisioning interconnections between public and private cloud environments. However, several other challenges emerged as nearly as common. For instance, 26% said they lacked end-to-end, multi-site network visibility and troubleshooting, and 26% said they struggled with network latency between internal and external cloud resources. Finally, 23% said that their applications were not properly architected for hybrid cloud networks. These latter three challenges point to potential application performance problems. Enterprises who are implementing hybrid clouds will need to evaluate the readiness of their performance management to support these new cloud environments. Many of them also clearly need to evaluate their application architectures before they migrate.

EMA's 2016 network megatrends research explored a number of other interesting topics that IT professionals will find informative. For instance, we found that the vast majority of network management professionals are supporting an Internet of Things (IoT) initiative. The research explores the technologies they are adopting, the use cases they are pursuing, and the challenges they are facing with IoT.

Nearly half of enterprises are applying advanced analytics to network data. And more than half are outsourcing some aspects of network management to a managed services provider. Our research explores both of these trends in depth. It also examines enterprises' evolving requirements of network management products and the overall challenges to and benefits of network operations success.

To learn more about this research, attend EMA's free research highlights webinar and download the full report.

Shamus McGillicuddy is Senior Analyst, Network Management at Enterprise Management Associates (EMA).

Hot Topics

The Latest

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...