The upward trajectory of edge computing uptake in modern software development is driving a much-needed convergence of various disciplines including DevOps, data engineering, security, networking, operational technology (OT), and machine learning operations (MLOps) best practices. This convergence is expected to empower IT teams to become more responsive than ever to the evolving needs of the business.
The Necessity Of Teams Convergence
Historically, each of these IT disciplines has operated in silos, with distinct handoffs occurring between the teams responsible for provisioning and maintaining various services. However, as more applications are deployed at the network edge, it becomes imperative for IT teams to collaborate closely. With applications deployed across a highly distributed computing environment, the seamless coordination of tasks such as updates and patches becomes essential for ensuring optimal performance and security.
Likewise, there’s a growing trend of processing and analyzing data at the point of collection and consumption. With more stateful applications operating at the network edge, there’s a rise in aggregated analytics data being transmitted back to applications running in the cloud or on-premises IT environments. This shift necessitates a fundamentally different approach to managing storage across multiple edge computing environments and the diverse federated backend systems spanning the enterprise.
Organizations often require storing files locally on edge computing platforms while also maintaining copies of those files in the cloud using an S3-compatible storage service. This approach facilitates access to cost-effective object-based storage, supporting various functions ranging from data protection to powering artificial intelligence (AI) applications. The multi-protocol capability inherent in this setup presents an opportunity not only to develop new stateful distributed applications but also to modernize existing applications, irrespective of the protocols used for data storage.
Collaboration & Centralized Management
Many edge computing platforms are maintained by operational technology (OT) teams, which typically report directly to the heads of different departments. However, with the increasing connectivity of these platforms to networks, there’s a growing need to centrally manage them as part of broader efforts to reduce the total cost of IT. After all, labor costs associated with managing IT infrastructure remain one of the most significant expenses. Centralized management can streamline operations, improve efficiency, and ultimately drive cost savings across the organization.
Additionally, the increasing demand for near real-time data sharing across distributed computing environments places additional strain on network operations (NetOps) teams. Unlike traditional batch updates performed once a day, the continuous stream of data from numerous edge computing platforms requires synchronization with multiple applications to deliver the latest and most accurate insights promptly.
Indeed, deploying AI models at the edge presents unique challenges, especially considering that much of the data used to train these models is utilized for inference engines deployed at the edge. To process terabytes of data efficiently, the underlying platform supporting these inference engines must also be scalable, often measuring in terabytes itself. Additionally, AI models can experience drift over time, or in the case of generative AI, produce inaccurate outputs. In response, data scientists must collaborate closely with DevOps teams to replace these models as needed. Unlike traditional applications that can be updated via patches, AI models require complete replacement with newer, more reliable, and safer versions.
Absolutely, securing edge computing platforms is important. Cybercriminals often target these platforms as potential gateways into the wider enterprise network. With each new edge computing platform deployed, the attack surface expands, necessitating comprehensive defense measures. It’s essential for cybersecurity teams to be closely involved in the development and deployment of edge computing applications and platforms to mitigate security risks effectively. Without this proactive approach, the likelihood of a breach occurring increases significantly, making it a matter of when, rather than if, a security incident will occur.
Integrating Edge Computing With DevOps
It may take some time for the convergence of these disparate IT domains to occur fully, but it seems all but inevitable. In fact, the distinction between edge computing and the broader IT environment is rapidly blurring as event-driven applications spanning multiple platforms become increasingly common. As this trend continues, the integration of edge computing into the overall IT landscape will become seamless, ushering in a new era of interconnected and agile computing environments.
DevOps teams, with their steadfast dedication to automation, should indeed lead the charge in these endeavors. Rather than limiting DevOps principles to application development and deployment, it’s evident that the time has come to automate IT workflows comprehensively. Scaling up the application of DevOps principles to encompass end-to-end IT workflows is crucial for effectively managing edge computing at scale. This approach, also known as platform engineering, empowers organizations to oversee distributed IT environments efficiently, without the need to amass large teams of IT specialists. While specialized expertise will always be necessary, breaking down silos and streamlining processes is essential for accelerating innovation and adapting to the evolving landscape of IT.
While not every member of an IT organization needs to be an expert in every task, it’s crucial for them to understand the dependencies between various components such as applications, networking, storage, and cybersecurity. This understanding enables the team to collaborate effectively and advance the goals of businesses that rely heavily on IT infrastructure. In today’s digital age, where businesses are increasingly dependent on IT, having a holistic understanding of how different components interact is essential for driving innovation, mitigating risks, and ensuring operational efficiency.
In conclusion, each organization must find the pace of convergence that aligns with its specific needs and circumstances. However, accelerating this convergence can lead to significant returns on investment in edge computing. The challenge and opportunity lie in effectively managing yet another major IT transition. This transition involves addressing not only technical challenges but also cultural shifts within the organization. Successfully navigating these cultural and technical hurdles will be critical in unlocking the full potential of edge computing and maximizing its benefits for the business.
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