Top Factors That Impact Application Performance 2016 - Part 2

In 2013, APMdigest published a list called 15 Top Factors That Impact Application Performance. Even today, this is one of the most popular pieces of content on the site. And for good reason – the whole concept of Application Performance Management (APM) starts with identifying the factors that impact application performance, and then doing something about it. However, in the fast moving world of IT, many aspects of application performance have changed in the 3 years since the list was published. And many new experts have come on the scene. So APMdigest is updating the list for 2016, and you will be surprised how much it has changed.

Start with Top Factors That Impact Application Performance 2016 - Part 1

Part 2 of this list covers more challenges in the environment, including containers, microservices and issues with the network.

6. VIRTUALIZATION AND CONTAINERIZATION

Applications today are disaggregated into multiple components that could be deployed as highly virtualized or containerized workloads. As a result, gaining visibility into the flows to understand the interaction between different components is paramount for IT operations to provide the best user experience for the applications.
Ananda Rajagopal
VP of Product Management, Gigamon

Over the last decade, we have seen the commoditization of the Cloud, and the trend towards running applications on virtualized hardware continues and evolves to an even higher level of modularization and compartmentalization: containers, micro-services, software-defined networks, virtual storage, and more. There is a trend towards small, self-contained, independent components that act as recyclable, multi-purpose building blocks. All that might allow for faster and cheaper development and operation of complex systems, but complexity increases when it comes to APM, tuning, monitoring, logging, debugging. The respective tools need to be able to see and analyze all physical and virtual components and how they interact, and allow developers and ops teams to make sense of all those data points.
Sven Dummer
Senior Director of Product Marketing, Loggly

7. MICROSERVICES

As enterprises are adopting microservices and continuous delivery methodologies, the number of independent applications and web services are growing exponentially. Isolating any application performance issues in an application environment with hundreds and thousands of interdependent services can be challenging if not instrumented and monitored in real-time. Manually instrumenting these microservices and setting static thresholds can be a very difficult task, if not impossible. Enterprises need to automatically discover these large numbers of microservices, dynamically baseline their performance, collect deep diagnostics and alerts when the performance deviates from the normal baseline.
Anand Akela
Director, Product Marketing, AppDynamics

Modern applications increasingly relying on stateless microservices, are often paired with stateful data services (like NoSQL, Kafka, Hadoop etc.), and are being deployed on containers or leverage serverless architectures. As the application substrate is changing, so are the factors that impact performance of these applications. These factors include how the various microservices are interacting with each other, their availability, and correlating issues like errors, latency and throughput across these services. The individual service performance is not paramount in itself. Also as orchestration systems like Mesos, Kubernetes and Docker Swarm become more critical, application performance will increasingly rely on how effectively these orchestration systems can manage resources, both applications as well as underlying infrastructure.
Sachin Agarwal
VP Marketing, OpsClarity

8. SERVICE DESIGN

In today's Everything-As-A-Service hybrid application world the biggest impact on performance is ignoring core principles of proximity and context of the services to the users. Gone are the days of tightly coupled applications, data and infrastructure. Whether born in the cloud or connected to it – the biggest impact I have seen with today's modern applications is ignoring solid user centered design principles. Services that integrate multiple applications, micro-services and clouds require more finesse in balancing the last mile connectivity from the user to the service but also the services, micro-services and data to each other. Experience teaches us that applying same assumptions one would of traditional client/server applications will not work with today's modern applications. In lieu of just looking at a specific micro-service, service or component – performance testing must take into account impact to overall performance or risk production issues that are difficult to identify or pinpoint because of the multiple faceted nature of these solutions. Improper design of a service from a user location/experience is a recipe for disaster.
Jeanne Morain
Strategist and Author, iSpeak Cloud

9. SERVER SIDE CODE

Although we are seeing less tolerance for low applications by users, the primary reason for poor performance continues to be inefficient server side code. As components are increasingly interconnected determining the cause of slowness or faults continues to take longer, creating the need for end-to-end APM.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

10. NETWORK LATENCY

Latency is the top factor that impacts application performance. The most well-developed application will be terribly slow if latency between users and servers is high. And the most poorly-developed application can garner all kinds of praises when everything is local. I believe low latency is the single most important asset that IT managers can have on their networks. You should focus on any ways to reduce end-to-end delay. This includes reducing the various contributors to latency, such as processing delay, queuing delay, serialization delay, and last, but certainly not least, propagation delay.
Jean Tunis
Senior Consultant and Founder of RootPerformance

Poorly peered Internet relationships and congestion remain the top contributor to latency in web and mobile apps, even when using a Content Delivery Network. Seventy-five percent of an application's page load time comes from the latency of the network. Even after solving the poorly constructed HTML, and ensuring your app has no blocking calls, you still have the Internet to deal with.
Josh Gray
Chief Architect, Cedexis

11. LOAD

Applications can become overloaded based on changes in the business environment. More jobs, workloads, or users can in turn negatively affect performance. For example, if your Microsoft Exchange send and receive queue lengths grow for mailbox databases, or if users are experiencing logon latency, you must look at not only the processing and memory resources for Exchange servers, but also the availability of flash storage.
Goran Garevski
VP of Engineering, Comtrade Software

12. RESOURCE AVAILABILITY

Application performance is most impacted by resource availability. With applications migrating to digital media, it can be difficult for businesses to gauge the appropriate resources necessary to deliver a consistent performance metric. It is critical to leverage solutions that can dynamically detect and scale resource requirements through the use of virtualization, orchestration, and automation to provide the cloud-like elasticity and agility that organizations require for successful and consistent application performance.
Frank Yue
Director of Application Delivery Solutions, Radware

13. CACHING BOTTLENECKS

Application speed and scalability are forever intertwined with each other. With load triggered elastic scaling in the cloud, finding every application bottleneck and applying strategic caching architectures are more important than ever. At times, code or database results in a given framework or architecture cannot be refactored quickly enough. Failure to cache wherever possible, relates directly to a higher number of host instances, which means a higher cost of doing business in the cloud. Bottlenecks tend move around within an evolving application with a high release rate and with a lot of developers. A solid APM technology can quickly and automatically identify a deviation from application baseline performance at any tier, essentially safeguarding user experience, brand reputation and digital trust. Hosting an application in the cloud without an APM tool watching for bottlenecks is throwing money away.
Matt Hintze
CTO, HITS Inc.

Read Top Factors That Impact Application Performance 2016 - Part 3 covering how the application interacts with the backend and the front end.

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Top Factors That Impact Application Performance 2016 - Part 2

In 2013, APMdigest published a list called 15 Top Factors That Impact Application Performance. Even today, this is one of the most popular pieces of content on the site. And for good reason – the whole concept of Application Performance Management (APM) starts with identifying the factors that impact application performance, and then doing something about it. However, in the fast moving world of IT, many aspects of application performance have changed in the 3 years since the list was published. And many new experts have come on the scene. So APMdigest is updating the list for 2016, and you will be surprised how much it has changed.

Start with Top Factors That Impact Application Performance 2016 - Part 1

Part 2 of this list covers more challenges in the environment, including containers, microservices and issues with the network.

6. VIRTUALIZATION AND CONTAINERIZATION

Applications today are disaggregated into multiple components that could be deployed as highly virtualized or containerized workloads. As a result, gaining visibility into the flows to understand the interaction between different components is paramount for IT operations to provide the best user experience for the applications.
Ananda Rajagopal
VP of Product Management, Gigamon

Over the last decade, we have seen the commoditization of the Cloud, and the trend towards running applications on virtualized hardware continues and evolves to an even higher level of modularization and compartmentalization: containers, micro-services, software-defined networks, virtual storage, and more. There is a trend towards small, self-contained, independent components that act as recyclable, multi-purpose building blocks. All that might allow for faster and cheaper development and operation of complex systems, but complexity increases when it comes to APM, tuning, monitoring, logging, debugging. The respective tools need to be able to see and analyze all physical and virtual components and how they interact, and allow developers and ops teams to make sense of all those data points.
Sven Dummer
Senior Director of Product Marketing, Loggly

7. MICROSERVICES

As enterprises are adopting microservices and continuous delivery methodologies, the number of independent applications and web services are growing exponentially. Isolating any application performance issues in an application environment with hundreds and thousands of interdependent services can be challenging if not instrumented and monitored in real-time. Manually instrumenting these microservices and setting static thresholds can be a very difficult task, if not impossible. Enterprises need to automatically discover these large numbers of microservices, dynamically baseline their performance, collect deep diagnostics and alerts when the performance deviates from the normal baseline.
Anand Akela
Director, Product Marketing, AppDynamics

Modern applications increasingly relying on stateless microservices, are often paired with stateful data services (like NoSQL, Kafka, Hadoop etc.), and are being deployed on containers or leverage serverless architectures. As the application substrate is changing, so are the factors that impact performance of these applications. These factors include how the various microservices are interacting with each other, their availability, and correlating issues like errors, latency and throughput across these services. The individual service performance is not paramount in itself. Also as orchestration systems like Mesos, Kubernetes and Docker Swarm become more critical, application performance will increasingly rely on how effectively these orchestration systems can manage resources, both applications as well as underlying infrastructure.
Sachin Agarwal
VP Marketing, OpsClarity

8. SERVICE DESIGN

In today's Everything-As-A-Service hybrid application world the biggest impact on performance is ignoring core principles of proximity and context of the services to the users. Gone are the days of tightly coupled applications, data and infrastructure. Whether born in the cloud or connected to it – the biggest impact I have seen with today's modern applications is ignoring solid user centered design principles. Services that integrate multiple applications, micro-services and clouds require more finesse in balancing the last mile connectivity from the user to the service but also the services, micro-services and data to each other. Experience teaches us that applying same assumptions one would of traditional client/server applications will not work with today's modern applications. In lieu of just looking at a specific micro-service, service or component – performance testing must take into account impact to overall performance or risk production issues that are difficult to identify or pinpoint because of the multiple faceted nature of these solutions. Improper design of a service from a user location/experience is a recipe for disaster.
Jeanne Morain
Strategist and Author, iSpeak Cloud

9. SERVER SIDE CODE

Although we are seeing less tolerance for low applications by users, the primary reason for poor performance continues to be inefficient server side code. As components are increasingly interconnected determining the cause of slowness or faults continues to take longer, creating the need for end-to-end APM.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

10. NETWORK LATENCY

Latency is the top factor that impacts application performance. The most well-developed application will be terribly slow if latency between users and servers is high. And the most poorly-developed application can garner all kinds of praises when everything is local. I believe low latency is the single most important asset that IT managers can have on their networks. You should focus on any ways to reduce end-to-end delay. This includes reducing the various contributors to latency, such as processing delay, queuing delay, serialization delay, and last, but certainly not least, propagation delay.
Jean Tunis
Senior Consultant and Founder of RootPerformance

Poorly peered Internet relationships and congestion remain the top contributor to latency in web and mobile apps, even when using a Content Delivery Network. Seventy-five percent of an application's page load time comes from the latency of the network. Even after solving the poorly constructed HTML, and ensuring your app has no blocking calls, you still have the Internet to deal with.
Josh Gray
Chief Architect, Cedexis

11. LOAD

Applications can become overloaded based on changes in the business environment. More jobs, workloads, or users can in turn negatively affect performance. For example, if your Microsoft Exchange send and receive queue lengths grow for mailbox databases, or if users are experiencing logon latency, you must look at not only the processing and memory resources for Exchange servers, but also the availability of flash storage.
Goran Garevski
VP of Engineering, Comtrade Software

12. RESOURCE AVAILABILITY

Application performance is most impacted by resource availability. With applications migrating to digital media, it can be difficult for businesses to gauge the appropriate resources necessary to deliver a consistent performance metric. It is critical to leverage solutions that can dynamically detect and scale resource requirements through the use of virtualization, orchestration, and automation to provide the cloud-like elasticity and agility that organizations require for successful and consistent application performance.
Frank Yue
Director of Application Delivery Solutions, Radware

13. CACHING BOTTLENECKS

Application speed and scalability are forever intertwined with each other. With load triggered elastic scaling in the cloud, finding every application bottleneck and applying strategic caching architectures are more important than ever. At times, code or database results in a given framework or architecture cannot be refactored quickly enough. Failure to cache wherever possible, relates directly to a higher number of host instances, which means a higher cost of doing business in the cloud. Bottlenecks tend move around within an evolving application with a high release rate and with a lot of developers. A solid APM technology can quickly and automatically identify a deviation from application baseline performance at any tier, essentially safeguarding user experience, brand reputation and digital trust. Hosting an application in the cloud without an APM tool watching for bottlenecks is throwing money away.
Matt Hintze
CTO, HITS Inc.

Read Top Factors That Impact Application Performance 2016 - Part 3 covering how the application interacts with the backend and the front end.

Related Links

Hot Topics

The Latest

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

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Building AI Agents That Build Trust

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Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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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Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

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