Every technologist is all too familiar with the intricacies of increasing system complexity. Myriad tech issues lead to sleepless nights: rapidly evolving applications, cloud migrations, hybrid environments, security concerns, incomplete documentation, and inadequate tools for developing, modeling and measuring high-performance software. Methodical performance testing can help mitigate the risks of implementing new application deployments and features, infrastructure upgrades and more.
Performance engineering (PE) is “a systematic approach for continuous application performance improvement,” Gartner notes. It involves practices, techniques and activities in each DevOps phase to achieve your performance quality goals. It focuses on architecture, design and implementation choices to enhance application performance, and adopts practices to help mitigate performance risks before development efforts progress to additional phases.
Why performance engineering matters
In today’s digital business environment, the ability to consistently deliver high-value applications and services that satisfy customer expectations is more critical than ever. The connection between application and business success is essential, particularly as end users demand an always-on, secure and exceptional customer experience — an expectation that grew significantly during the pandemic. Digital transformation is impacting all organizations, especially those that interact with consumers via web and mobile applications. In addition, the transition to hybrid work is increasing interaction with SaaS tools and web interfaces.
Performance requirements, a critical component of a continuous quality strategy, help drive product design, development and delivery. But increasingly complex development needs — scalability, stability, quality of service (QoS), availability, performance, heightened security and more — are taxing organizations already dealing with labyrinthine software environments. As summed up by the International Conference on Performance Engineering (ICPE), modern web-based systems, data centers, cloud infrastructures, social networks, mobile and wireless systems, Internet of Things and real-time and embedded systems have greatly increased distributed and dynamic system architectures that — while providing increased flexibility — have also heightened the complexity of end-to-end application performance management.
Given these challenges, it’s unsurprising that one in four respondents in AppDynamics’ Agents of Transformation 2022 survey say their organization remains stuck in reactive, “fire-fighting mode” instead of innovating.
Business benefits of performance engineering
Performance engineering can include both shift-left and shift-right testing practices: the former involves moving software checks and controls to earlier in the code development pipeline; the latter refers to continuous software testing in a post-production environment. By providing a framework for application excellence, performance engineering can significantly improve the ability of developers to consistently deliver solutions that exceed performance expectations, drive value and boost customer satisfaction.
In addition to rising end user demand for application quality, many additional factors drive performance engineering in organizations, including the ongoing effort to ensure business continuity under changing usage patterns, network topologies, data volumes and more. The need to reduce or eliminate common problems caused by performance issues — including software bottlenecks, increased maintenance costs, late system deployment, tuning, rework and deployment, and unnecessary hardware spend — is a major factor as well.
Furthermore, organizations must optimize their use of multi-cloud deployments, microservices-based architectures, and automation and integration capabilities of modern application platforms. They also may want to manage performance and scalability across multiple cloud providers, including Amazon Web Services (AWS), Google Cloud and Microsoft Azure, ensuring their ability to shift between cloud providers without impacting the user experience.
Major performance engineering roadblocks
When it comes to achieving their performance engineering goals, organizations face numerous obstacles. They must avoid focusing solely on modern tools and technology, which can enable higher-quality output but not resolve issues on their own. They also must fight enterprise-wide inertia, and implement much-needed changes in organizational culture, notes Gartner. Common problems include:
- Departmental silos: teams don’t collaborate or share insights across workgroups.
- Autocratic, top-down leadership: lower-level managers have little to no authority to change policies without approval from the highest level of management.
- Lack of organizational experience with continuous quality management.
- Lack of well-defined goals aligned with the business’ priorities.
- Internal resistance to PE efforts, such as encouraging stakeholders throughout the org to seek out new opportunities for PE improvements.
- Failure to establish clear performance objectives and facilitate the discovery of issues early in development.
Performance engineering tips
Performance testing and tuning can produce a deluge of data, which may seem overwhelming to analyze for insights. Fortunately, there are many steps an organization can take to manage this process effectively. (For some great PE tips, check out TechBeacon’s pragmatic advice.)
One good approach is to develop a proactive, product-quality strategy that makes performance a well-defined requirement. It’s essential to verify that end user and customer satisfaction meets or exceeds expectations, and that performance goals are being met. For example, to create awareness for nonfunctional characteristics such as scalability, usability, security, maintainability and efficiency, Gartner suggests using the ISO/IEC 25010 product quality evaluation system to help teams better understand quality characteristics, including the eight key categories outlined in the diagram below.
By establishing relevant, performance-quality metrics based on common goals shared by the business and IT, organizations will gain a stronger framework to ensure that application performance expectations are met. It’s equally important to collaborate with infrastructure and operations (I&O) leaders, thus creating a feedback loop by leveraging performance data from production and real users alike.
Avoid analyzing too many business transactions, though, as this all-too-common approach is inefficient and can lead to analysis paralysis. It’s also important to wait for a test to complete before analyzing the results, advises Tech Beacon, which may seem obvious but is a common mistake among business stakeholders during load tests. Rather than observing each data point as it arrives, sit back, breathe and wait. The results and trends will be much easier to analyze once testing is done.
Observability in performance engineering
An observability or monitoring solution can play a critical role in performance engineering, providing invaluable feedback to validate system and performance metrics specified by the non-functional requirements (NFRs). It can enable many benefits including the ability to track trends over time, such as the performance impact of increasing user loads and data sets.
A properly implemented observability solution allows organizations to predict when an application will exceed the NFRs for a specific use case, enabling IT teams to deploy resources to keep the system operating within the established NFR parameters.
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