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Application Performance Equals Business Performance

Steve Riley

At the Interop conference in April 2014, Riverbed conducted a short survey to determine whether and how application performance problems might affect an organization’s business. 210 respondents answered questions about the performance of business-critical applications, non-critical applications, and productivity applications.

We asked participants to consider their experiences at main offices, branch offices, and remote situations and evaluate how each of the following contributed to performance problems:

■ branch office infrastructure issues

■ insufficient bandwidth

■ poor application coding techniques

■ slow servers

■ too much latency in the network

We asked participants to indicate how far along they might be on projects to mitigate performance problems and to rate the effectiveness of several techniques including:

■ add more bandwidth

■ build a branch-converged infrastructure

■ distribute workloads geographically

■ deploy faster endpoints

■ deploy faster servers

■ implement application delivery controllers

■ implement performance monitoring

■ implement WAN optimization

■ rewrite applications


The Results

80% of respondents indicated that slow business-critical applications negatively affect business performance. 71% indicated that slow access to productivity applications negatively affect business performance. The top three causes of performance problems were insufficient bandwidth, too much latency, and slow servers. From this, we can observe that modern business has come to rely on highly available, high quality connectivity, and the sense that applications and data behave as if they’re local. Individuals can no longer work in isolation, disconnected from their peers. Nor can they waste time waiting for the computer to “catch up.”

Turning to mitigation techniques, we can see a curious gap emerge. The three top-rated techniques were adding bandwidth at 70%, implementing WAN optimization at 67%, and distributing workloads geographically at 52%. In all cases, however, fewer respondents indicated that they were engaged in related projects. Only 50% have added bandwidth, only 42% have implemented WAN optimization, and only 28% have distributed workloads geographically.

It isn’t all that unusual, really, for action to lag awareness. It is interesting to consider the reasons why, though. Discovering the root causes of performance problems can be challenging at times. Users often blame only one aspect: “Hey, what’s wrong with the network? Why is it always soooo sloooow?” This is a common reaction even if all except one or two applications are performing acceptably. In reality, performance problems could exist anywhere in the technology stack — the network, the application, the database, or the “glue” layers holding everything together.

Recommendations

We recommend four simple yet critical steps to help avoid unnecessary slowness, to help keep applications performing at their peak, and to help maintain a consistent end-user experience.

1. Analyze, diagnose, and resolve performance problems first

Monitoring tools can identify chatty applications, slow servers, congested networks, and other kinds of resource exhaustion. An end-to-end view provides the most visibility. Monitoring entire transactions, rather than just particular points, can reveal true causes of performance problems.

2. Remember that electrons and photons have a speed limit

And that limit is 186,282 miles per second (only under perfect conditions, naturally). Increasing the distance between users and data can negatively affect performance. It takes time for data to scoot across a continent or even a city.

3. Distribute workloads geographically when it makes good business sense

A “follow the sun” model can be a useful guide. Deploy application delivery controllers to increase availability and connect users to the data that’s closest. Take advantage of global load balancing capabilities to route requests as locally as possible, while also providing planet-wide resiliency against failures.

4. Address latency, the primary cause of poor WAN performance

Deploy WAN optimizers to reduce the amount of data traversing the WAN and to reduce the number of connections between clients and servers. These techniques minimize the effects of latency, and — almost — make it seem as if data moves faster than light.

Steve Riley is Deputy CTO for Riverbed Technology.

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Application Performance Equals Business Performance

Steve Riley

At the Interop conference in April 2014, Riverbed conducted a short survey to determine whether and how application performance problems might affect an organization’s business. 210 respondents answered questions about the performance of business-critical applications, non-critical applications, and productivity applications.

We asked participants to consider their experiences at main offices, branch offices, and remote situations and evaluate how each of the following contributed to performance problems:

■ branch office infrastructure issues

■ insufficient bandwidth

■ poor application coding techniques

■ slow servers

■ too much latency in the network

We asked participants to indicate how far along they might be on projects to mitigate performance problems and to rate the effectiveness of several techniques including:

■ add more bandwidth

■ build a branch-converged infrastructure

■ distribute workloads geographically

■ deploy faster endpoints

■ deploy faster servers

■ implement application delivery controllers

■ implement performance monitoring

■ implement WAN optimization

■ rewrite applications


The Results

80% of respondents indicated that slow business-critical applications negatively affect business performance. 71% indicated that slow access to productivity applications negatively affect business performance. The top three causes of performance problems were insufficient bandwidth, too much latency, and slow servers. From this, we can observe that modern business has come to rely on highly available, high quality connectivity, and the sense that applications and data behave as if they’re local. Individuals can no longer work in isolation, disconnected from their peers. Nor can they waste time waiting for the computer to “catch up.”

Turning to mitigation techniques, we can see a curious gap emerge. The three top-rated techniques were adding bandwidth at 70%, implementing WAN optimization at 67%, and distributing workloads geographically at 52%. In all cases, however, fewer respondents indicated that they were engaged in related projects. Only 50% have added bandwidth, only 42% have implemented WAN optimization, and only 28% have distributed workloads geographically.

It isn’t all that unusual, really, for action to lag awareness. It is interesting to consider the reasons why, though. Discovering the root causes of performance problems can be challenging at times. Users often blame only one aspect: “Hey, what’s wrong with the network? Why is it always soooo sloooow?” This is a common reaction even if all except one or two applications are performing acceptably. In reality, performance problems could exist anywhere in the technology stack — the network, the application, the database, or the “glue” layers holding everything together.

Recommendations

We recommend four simple yet critical steps to help avoid unnecessary slowness, to help keep applications performing at their peak, and to help maintain a consistent end-user experience.

1. Analyze, diagnose, and resolve performance problems first

Monitoring tools can identify chatty applications, slow servers, congested networks, and other kinds of resource exhaustion. An end-to-end view provides the most visibility. Monitoring entire transactions, rather than just particular points, can reveal true causes of performance problems.

2. Remember that electrons and photons have a speed limit

And that limit is 186,282 miles per second (only under perfect conditions, naturally). Increasing the distance between users and data can negatively affect performance. It takes time for data to scoot across a continent or even a city.

3. Distribute workloads geographically when it makes good business sense

A “follow the sun” model can be a useful guide. Deploy application delivery controllers to increase availability and connect users to the data that’s closest. Take advantage of global load balancing capabilities to route requests as locally as possible, while also providing planet-wide resiliency against failures.

4. Address latency, the primary cause of poor WAN performance

Deploy WAN optimizers to reduce the amount of data traversing the WAN and to reduce the number of connections between clients and servers. These techniques minimize the effects of latency, and — almost — make it seem as if data moves faster than light.

Steve Riley is Deputy CTO for Riverbed Technology.

Hot Topics

The Latest

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 ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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 ...