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What Is Driving Edge Computing and Edge Performance Monitoring?

Keith Bromley

There is a fundamental shift currently happening in operational technology today — it's the shift from core computing to edge computing. This shift is being driven by a completely massive growth in data that has already started to take place. According to Cisco Systems, network traffic will reach 4.8 zettabytes (i.e. 4.8 billion terabytes) by 2022.

Businesses cannot continue as usual and still keep up with network performance, security threats, and business decisions. So, in response, network architects are starting to move as much of the core compute resources as they can to the edge of the network. This helps IT reduce costs, improve network performance and maintain a secure network.

However, is the shifting of resources to the edge the right approach?

It could have a negative impact to the network in terms of new security holes, performance issues due to remote equipment, and reduced network visibility.

At the same time, if the network changes are done right, the pendulum could swing to the other side and great there could be great improvements to network security, performance, visibility that take place.

The answer comes down to the deployment of the new architecture. The pivotal tactic is to deploy a visibility architecture that can support the application services and monitoring functions needed. You need network visibility more than ever to: access the data you need, filter it properly, inspect for security threats, and manage SLAs to keep the latency low from the core to the edge.

Two key components are necessary to a successful visibility in this situation — a network packet broker (NPB) and SD-WAN. The NPB provides data aggregation and filtering, application filtering, and performance monitoring all the way to edge devices. SD-WAN services can (and probably should) then be layered on top of the IP-based links to guarantee link performance, as Internet-based services can introduce unacceptable levels of latency and packet loss into the network.

Edge computing deployments have already started to begin. According to a report from Gartner Research, by year-end of 2021, more than 50% of large enterprises will deploy at least one edge computing use case to support IoT or immersive experiences, versus the less than 5% that are currently performing this in 2019.

When it comes down to it, while the promise of edge computing is real, the actual deployment scenario (and whether or not you build network visibility into your network) is what is going to make or break the performance of your new architecture.

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What Is Driving Edge Computing and Edge Performance Monitoring?

Keith Bromley

There is a fundamental shift currently happening in operational technology today — it's the shift from core computing to edge computing. This shift is being driven by a completely massive growth in data that has already started to take place. According to Cisco Systems, network traffic will reach 4.8 zettabytes (i.e. 4.8 billion terabytes) by 2022.

Businesses cannot continue as usual and still keep up with network performance, security threats, and business decisions. So, in response, network architects are starting to move as much of the core compute resources as they can to the edge of the network. This helps IT reduce costs, improve network performance and maintain a secure network.

However, is the shifting of resources to the edge the right approach?

It could have a negative impact to the network in terms of new security holes, performance issues due to remote equipment, and reduced network visibility.

At the same time, if the network changes are done right, the pendulum could swing to the other side and great there could be great improvements to network security, performance, visibility that take place.

The answer comes down to the deployment of the new architecture. The pivotal tactic is to deploy a visibility architecture that can support the application services and monitoring functions needed. You need network visibility more than ever to: access the data you need, filter it properly, inspect for security threats, and manage SLAs to keep the latency low from the core to the edge.

Two key components are necessary to a successful visibility in this situation — a network packet broker (NPB) and SD-WAN. The NPB provides data aggregation and filtering, application filtering, and performance monitoring all the way to edge devices. SD-WAN services can (and probably should) then be layered on top of the IP-based links to guarantee link performance, as Internet-based services can introduce unacceptable levels of latency and packet loss into the network.

Edge computing deployments have already started to begin. According to a report from Gartner Research, by year-end of 2021, more than 50% of large enterprises will deploy at least one edge computing use case to support IoT or immersive experiences, versus the less than 5% that are currently performing this in 2019.

When it comes down to it, while the promise of edge computing is real, the actual deployment scenario (and whether or not you build network visibility into your network) is what is going to make or break the performance of your new architecture.

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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 ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

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