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Understand Your Infrastructure's Usage and Load Characteristics for Online Success

Sven Hammar

Cyber Monday is here and it is instrumental to keep your e-commerce platform in perfect health. Key areas to keep an eye on are usage and load characteristics — they are gauges for interpreting how much work an online platform performs, and how well it performs under stress.

Understanding how your digital infrastructure performs is not as simple as assessing your car’s odometer to measure distance traveled, or its speedometer to measure the maximum speed. Usage and load characteristics provide insight into the performance of the platform in real-world use cases, like analyzing that metaphorical car’s journey from point A to point B.

Understanding what kind of usage and load capacity your service concurrently supports, as well as changes to those factors in the future, is a vital part of providing an excellent online user experience. Cloud computing and scaling services are great assets because you have almost unlimited server resources to handle traffic spikes and growth; however, your service may suffer if you are not configured to use it.

On the other side, you do not want to be paying for more power than you need. Use your interpretation of usage and load characteristics to know your limits, check up on the user experience, and evaluate poor performance issues.

Usage: How Much Are You Utilizing?

Usage characteristics are a practical way to measure how much server power you need to run your web or mobile platform. Your usage characteristics are going to break down into CPU, memory, storage, pageviews and network load statistics which can be measured over time or by time increments. The usage data sheds light on how much information your platform is moving to end users, as well as when it moves.

Usage can also tell you how many users are accessing your service at a specific time and compare that against usage statistics to see how hard they are pushing the system. An example usage characteristic would be your web application moving 100GB of data within a month and 10,000 pageviews per hour.

Load: Can You Take the Heat?

Load characteristics can tell you how well your platform performs depending on how many end users are accessing the service concurrently, as well as the maximum amount of work the service can handle before it starts to experience performance problems. Whereas usage testing identifies how much information moves, load testing examines how efficiently the service moves that information.

Load testing, whether performed during development or on a live, fully functioning application, is like test-driving the user experience to make sure everything runs smoothly on a larger scale.

Using load testing analytics, you can identify capacity shortcomings and single out bottleneck points where the platform or server instances can be improved. Load testing gauges how well a platform holds up in terms of service capacity, long-term high use endurance conditions, and demand spikes. It is great for identifying problems with latency as well — something usage data does not provide any insight into. An example load characteristic is the latency between users when a typical number is simultaneously using the service.

Combining Both for Hosting Capacity and Programming Efficiency Analysis

Looking at your web service’s usage and load characteristics helps answer the question of whether your platform needs to make programming efficiency improvements and adjust hosting resources.

If your service passes the test with little headroom, it is an indication that future growth will disrupt service quality. The performance data helps businesses avoid being victims of their own success. Unpredictable load and rapid use expansion can cause the service to falter if the hosting services are not prepared.

For example, when Pinterest first launched, they used a gated account approval method at first for gradually allowing new users to access the service. This prevented them from overloading the application and creating a poor user experience.

Take advantage of the information that usage and load characteristics provide, adjusting service capabilities and your auto scaling settings to address problems with real-world service use. Do not become a victim of your own online success!

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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 ...

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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 ...

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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 ...

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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 ...

Understand Your Infrastructure's Usage and Load Characteristics for Online Success

Sven Hammar

Cyber Monday is here and it is instrumental to keep your e-commerce platform in perfect health. Key areas to keep an eye on are usage and load characteristics — they are gauges for interpreting how much work an online platform performs, and how well it performs under stress.

Understanding how your digital infrastructure performs is not as simple as assessing your car’s odometer to measure distance traveled, or its speedometer to measure the maximum speed. Usage and load characteristics provide insight into the performance of the platform in real-world use cases, like analyzing that metaphorical car’s journey from point A to point B.

Understanding what kind of usage and load capacity your service concurrently supports, as well as changes to those factors in the future, is a vital part of providing an excellent online user experience. Cloud computing and scaling services are great assets because you have almost unlimited server resources to handle traffic spikes and growth; however, your service may suffer if you are not configured to use it.

On the other side, you do not want to be paying for more power than you need. Use your interpretation of usage and load characteristics to know your limits, check up on the user experience, and evaluate poor performance issues.

Usage: How Much Are You Utilizing?

Usage characteristics are a practical way to measure how much server power you need to run your web or mobile platform. Your usage characteristics are going to break down into CPU, memory, storage, pageviews and network load statistics which can be measured over time or by time increments. The usage data sheds light on how much information your platform is moving to end users, as well as when it moves.

Usage can also tell you how many users are accessing your service at a specific time and compare that against usage statistics to see how hard they are pushing the system. An example usage characteristic would be your web application moving 100GB of data within a month and 10,000 pageviews per hour.

Load: Can You Take the Heat?

Load characteristics can tell you how well your platform performs depending on how many end users are accessing the service concurrently, as well as the maximum amount of work the service can handle before it starts to experience performance problems. Whereas usage testing identifies how much information moves, load testing examines how efficiently the service moves that information.

Load testing, whether performed during development or on a live, fully functioning application, is like test-driving the user experience to make sure everything runs smoothly on a larger scale.

Using load testing analytics, you can identify capacity shortcomings and single out bottleneck points where the platform or server instances can be improved. Load testing gauges how well a platform holds up in terms of service capacity, long-term high use endurance conditions, and demand spikes. It is great for identifying problems with latency as well — something usage data does not provide any insight into. An example load characteristic is the latency between users when a typical number is simultaneously using the service.

Combining Both for Hosting Capacity and Programming Efficiency Analysis

Looking at your web service’s usage and load characteristics helps answer the question of whether your platform needs to make programming efficiency improvements and adjust hosting resources.

If your service passes the test with little headroom, it is an indication that future growth will disrupt service quality. The performance data helps businesses avoid being victims of their own success. Unpredictable load and rapid use expansion can cause the service to falter if the hosting services are not prepared.

For example, when Pinterest first launched, they used a gated account approval method at first for gradually allowing new users to access the service. This prevented them from overloading the application and creating a poor user experience.

Take advantage of the information that usage and load characteristics provide, adjusting service capabilities and your auto scaling settings to address problems with real-world service use. Do not become a victim of your own online success!

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 ...