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In Hyperscale Environments Network Visibility Remains Vital

Mike Heumann

Hyperscale infrastructure was once widely considered to be designed for large, web-facing organizations or top-tier colocation and cloud providers. But as the enterprise starts to confront the realities of big data and mobile, dynamic workloads, it seems inevitable that even medium-sized organizations will have to encounter hyperscale environments at some point, either as greenfield deployments or in a hosted/cloud capacity – and we’re seeing this come to fruition. According to an October 2014 Emulex survey of more than 1,600 US and European IT professionals who provided insight into their enterprise data center networking environments, 57 percent of respondents have adopted hyperscale networking environments.

What are the ramifications of this? More than half of these (51 percent) named increasing bandwidth as a major challenge in moving to hyperscale environments, which is driving massive network upgrades. In fact, more than 77 percent of respondents running hyperscale environments say the move to the cloud has already necessitated the upgrade of their networks to at least 40Gb Ethernet. Big data and analytics are driving high volumes of data storage, capture, movement and archiving which all drive an increased need for both higher bandwidth and lower latency on 10Gb and 40Gb Ethernet using RDMA.

We’re seeing hyperscale network environments grow across enterprises because it has a number of benefits: It can be a key piece of smarter, more manageable application ecosystems. On a network level, hyperscale can also aid business continuity and disaster recovery efforts by providing redundant failover architecture and rapid recovery. Hyperscale can also accentuate the benefits that technologies like virtualization and software-defined networking provide, making it a potentially vital configuration in the more scalable data center machinery of the future.

As we know, the loss of application or network services for even a few minutes can mean huge and immediate revenue losses, and can impact long-term customer loyalty. As many enterprises go through these rapid network changes and as hyperscale environments become more common, it is important that IT networking and application staffs prioritize network visibility as a means to identify any network issues that need to be identified quickly. Many organizations are struggling to accurately monitor increasingly complex enterprise networks as they shift to 10Gb Ethernet or even higher speeds.  

For those running hyperscale environments, 97 percent said it has necessitated a move to 10GbE, 40GbE or higher speeds to meet demands of high-performance applications such as big data, analytics and content distribution, compared to only 48 percent of respondents from non-hyperscale organizations. As a result, the underlying communication networks matter, and perhaps not surprisingly, capturing network behavior for incident detection and monitoring network flows for anomalous behavior are just as important. The ability to do both of these is essential in many high-speed networks (such as trading platforms and e-commerce portals) in order to spot cyber threats like DDoS attacks. The migration to larger, faster networks only exacerbates the threat of missing these attacks, and increases the need for clear, deep visibility.

Mike Heumann is VP of Product Marketing and Alliances at Emulex.

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In Hyperscale Environments Network Visibility Remains Vital

Mike Heumann

Hyperscale infrastructure was once widely considered to be designed for large, web-facing organizations or top-tier colocation and cloud providers. But as the enterprise starts to confront the realities of big data and mobile, dynamic workloads, it seems inevitable that even medium-sized organizations will have to encounter hyperscale environments at some point, either as greenfield deployments or in a hosted/cloud capacity – and we’re seeing this come to fruition. According to an October 2014 Emulex survey of more than 1,600 US and European IT professionals who provided insight into their enterprise data center networking environments, 57 percent of respondents have adopted hyperscale networking environments.

What are the ramifications of this? More than half of these (51 percent) named increasing bandwidth as a major challenge in moving to hyperscale environments, which is driving massive network upgrades. In fact, more than 77 percent of respondents running hyperscale environments say the move to the cloud has already necessitated the upgrade of their networks to at least 40Gb Ethernet. Big data and analytics are driving high volumes of data storage, capture, movement and archiving which all drive an increased need for both higher bandwidth and lower latency on 10Gb and 40Gb Ethernet using RDMA.

We’re seeing hyperscale network environments grow across enterprises because it has a number of benefits: It can be a key piece of smarter, more manageable application ecosystems. On a network level, hyperscale can also aid business continuity and disaster recovery efforts by providing redundant failover architecture and rapid recovery. Hyperscale can also accentuate the benefits that technologies like virtualization and software-defined networking provide, making it a potentially vital configuration in the more scalable data center machinery of the future.

As we know, the loss of application or network services for even a few minutes can mean huge and immediate revenue losses, and can impact long-term customer loyalty. As many enterprises go through these rapid network changes and as hyperscale environments become more common, it is important that IT networking and application staffs prioritize network visibility as a means to identify any network issues that need to be identified quickly. Many organizations are struggling to accurately monitor increasingly complex enterprise networks as they shift to 10Gb Ethernet or even higher speeds.  

For those running hyperscale environments, 97 percent said it has necessitated a move to 10GbE, 40GbE or higher speeds to meet demands of high-performance applications such as big data, analytics and content distribution, compared to only 48 percent of respondents from non-hyperscale organizations. As a result, the underlying communication networks matter, and perhaps not surprisingly, capturing network behavior for incident detection and monitoring network flows for anomalous behavior are just as important. The ability to do both of these is essential in many high-speed networks (such as trading platforms and e-commerce portals) in order to spot cyber threats like DDoS attacks. The migration to larger, faster networks only exacerbates the threat of missing these attacks, and increases the need for clear, deep visibility.

Mike Heumann is VP of Product Marketing and Alliances at Emulex.

Hot Topics

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...