Skip to main content

Reducing the Risks Associated with Deploying New Network-Centric Applications

Mike Heumann

It has been clear for quite some time that the network has become the lifeblood of nearly all enterprises. This is not just true for obvious network-centric enterprises such as retail sites or content distributors, but also for enterprises that utilize distributed applications such as SAP, Oracle, or any number of other network-centric applications.

Over the past few years, a number of new enterprise technologies have emerged that are critically reliant on network performance, but where this reliance is not necessarily obvious. These technologies include enterprise-class Voice over IP (VOIP) telephony solutions, Virtual Desktop Infrastructure (VDI) solutions, and enterprise collaboration tools.

While the vulnerability of "classical" distributed applications to network performance issues are well-understood, it is quite a different matter for a VDI session to momentarily "freeze", or for the CEO's VOIP call to get disrupted due to network issues. In short, these issues are far more visible than those associated with classical distributed applications, and as technologies such as software-defined networks (SDN) and hybrid private-public enterprise clouds become more prevalent, these issues are likely to become more rather than less pronounced.

So what do IT departments need to ensure that they can provide the levels of performance from these new technologies that users expect?

The most obvious answer is that they need to know what is really going on in their networks. While this sounds trite, it is far more difficult than one might expect. Causative factors such as microbursts, timeouts, and protocol errors can be difficult to detect with conventional application performance tools, and tying these causative events to the specific "new technology" outages can be even harder.

Given that many of these causative factors can be intermittent in nature certainly doesn't help. This is one of the primary reasons that many enterprises have introduced dedicated "network visibility fabrics" that provide instrumentation at key points in the network, exposing the full set of network packets and flow data that underlie these causative issues. While network visibility fabrics do not prevent these issues from occurring, they do speed the ability to resolve issues, which helps to avoid "outages" of network-centric technologies such as VDI, VoIP, network collaboration tools, and SDN frameworks.

As with most human endeavors, one of the best practices for making good decisions is to have the right data. Even the best decision-making processes can lead to wrong decisions by not having the right data. As networks (and the applications that depend on them) become more complex and carry more types of data, it becomes imperative to have the right data to avoid making guesses as to what is causing network issues. Look to see more enterprises implementing network visibility fabrics as dense 10Gb Ethernet networks become more prevalent, and more enterprises start to deploy these new technologies.

Mike Heumann is Sr. Director, Marketing (Endace) for Emulex.

Related Links:

www.emulex.com/

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

Reducing the Risks Associated with Deploying New Network-Centric Applications

Mike Heumann

It has been clear for quite some time that the network has become the lifeblood of nearly all enterprises. This is not just true for obvious network-centric enterprises such as retail sites or content distributors, but also for enterprises that utilize distributed applications such as SAP, Oracle, or any number of other network-centric applications.

Over the past few years, a number of new enterprise technologies have emerged that are critically reliant on network performance, but where this reliance is not necessarily obvious. These technologies include enterprise-class Voice over IP (VOIP) telephony solutions, Virtual Desktop Infrastructure (VDI) solutions, and enterprise collaboration tools.

While the vulnerability of "classical" distributed applications to network performance issues are well-understood, it is quite a different matter for a VDI session to momentarily "freeze", or for the CEO's VOIP call to get disrupted due to network issues. In short, these issues are far more visible than those associated with classical distributed applications, and as technologies such as software-defined networks (SDN) and hybrid private-public enterprise clouds become more prevalent, these issues are likely to become more rather than less pronounced.

So what do IT departments need to ensure that they can provide the levels of performance from these new technologies that users expect?

The most obvious answer is that they need to know what is really going on in their networks. While this sounds trite, it is far more difficult than one might expect. Causative factors such as microbursts, timeouts, and protocol errors can be difficult to detect with conventional application performance tools, and tying these causative events to the specific "new technology" outages can be even harder.

Given that many of these causative factors can be intermittent in nature certainly doesn't help. This is one of the primary reasons that many enterprises have introduced dedicated "network visibility fabrics" that provide instrumentation at key points in the network, exposing the full set of network packets and flow data that underlie these causative issues. While network visibility fabrics do not prevent these issues from occurring, they do speed the ability to resolve issues, which helps to avoid "outages" of network-centric technologies such as VDI, VoIP, network collaboration tools, and SDN frameworks.

As with most human endeavors, one of the best practices for making good decisions is to have the right data. Even the best decision-making processes can lead to wrong decisions by not having the right data. As networks (and the applications that depend on them) become more complex and carry more types of data, it becomes imperative to have the right data to avoid making guesses as to what is causing network issues. Look to see more enterprises implementing network visibility fabrics as dense 10Gb Ethernet networks become more prevalent, and more enterprises start to deploy these new technologies.

Mike Heumann is Sr. Director, Marketing (Endace) for Emulex.

Related Links:

www.emulex.com/

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...