DevOps Must Expand Edge Visibility for Improved App Performance

In the ever-evolving world of software development, the edge of the network is no longer a nebulous frontier—it’s a critical front in the battle for seamless app performance. As applications stretch across global infrastructures, the quality and continuity of services they depend on—ranging from locally provided internet connections to expansive content delivery networks—become foundational to the user experience. DevOps teams have long been the custodians of code, ensuring its smooth passage from development to deployment. Yet, their watch tends to halt where the realm of network operations begins. This division has led to blind spots when it comes to managing the increasingly common hiccups that occur beyond their traditional scope—hiccups that can cripple an application’s effectiveness as surely as any bug.

Collaboration: Bridging DevOps and NetOps

The merging of NetOps and DevOps is not merely a matter of convenience; it’s one of necessity. The former brings to the table expertise in managing and maintaining the integrity of network operations—insights that are invaluable when applications are subject to the volatile nature of the internet. The vastness of the network edge amplifies the complexity of diagnosing issues, where the problem might be rooted in external services like DNS resolution, CDN caching strategies, or even transient ISP outages. For DevOps, the expertise of NetOps is the compass by which they can navigate these murky waters. Together, they can enact strategies that not only detect and mitigate issues more rapidly but can also incorporate network considerations into the development lifecycle itself, leading to more resilient code.

DevOps teams must rethink their perimeter. It is no longer enough to monitor and optimize within the confines of their own infrastructure. The full composition of application delivery—including the unpredictable performance of third-party internet services—must fall under their purview. By integrating network visibility tools, they can identify bottlenecks and failures in real-time, often before users are impacted. But beyond monitoring, they must also interpret and act on this data, engaging with service providers to refine routes, improve caching, and enforce quality service agreements. Performance at the edge is not solely the domain of network specialists; it is a critical component of modern DevOps best practices.

Toward Comprehensive Observability

Understanding your application’s performance at the network edge goes beyond basic monitoring; it’s about in-depth analysis to pinpoint the reasons behind performance dips. For DevOps, this translates to leveraging detailed telemetry offered by observability platforms or Internet Performance Monitoring tools. Such tools dissect network stats like latency and packet loss, helping to differentiate between code-level issues and network inefficiencies.

This visibility is crucial for quick problem-solving and gives developers concrete data for refining their work. It involves recognizing that every element in the delivery chain is critical and warrants attention. By adopting such inspection tools and philosophies, developers don’t just react faster to problems—they also proactively design applications that are resilient and adaptable to the unpredictable network landscape, ensuring a seamless experience for users worldwide.

Explore more

Agentic AI Redefines the Software Development Lifecycle

The quiet hum of servers executing tasks once performed by entire teams of developers now underpins the modern software engineering landscape, signaling a fundamental and irreversible shift in how digital products are conceived and built. The emergence of Agentic AI Workflows represents a significant advancement in the software development sector, moving far beyond the simple code-completion tools of the past.

Is AI Creating a Hidden DevOps Crisis?

The sophisticated artificial intelligence that powers real-time recommendations and autonomous systems is placing an unprecedented strain on the very DevOps foundations built to support it, revealing a silent but escalating crisis. As organizations race to deploy increasingly complex AI and machine learning models, they are discovering that the conventional, component-focused practices that served them well in the past are fundamentally

Agentic AI in Banking – Review

The vast majority of a bank’s operational costs are hidden within complex, multi-step workflows that have long resisted traditional automation efforts, a challenge now being met by a new generation of intelligent systems. Agentic and multiagent Artificial Intelligence represent a significant advancement in the banking sector, poised to fundamentally reshape operations. This review will explore the evolution of this technology,

Cooling Job Market Requires a New Talent Strategy

The once-frenzied rhythm of the American job market has slowed to a quiet, steady hum, signaling a profound and lasting transformation that demands an entirely new approach to organizational leadership and talent management. For human resources leaders accustomed to the high-stakes war for talent, the current landscape presents a different, more subtle challenge. The cooldown is not a momentary pause

What If You Hired for Potential, Not Pedigree?

In an increasingly dynamic business landscape, the long-standing practice of using traditional credentials like university degrees and linear career histories as primary hiring benchmarks is proving to be a fundamentally flawed predictor of job success. A more powerful and predictive model is rapidly gaining momentum, one that shifts the focus from a candidate’s past pedigree to their present capabilities and