Office of the CTO

Enterprises are naturally siloed with information shared on a need-to-know basis. As enterprises expand, so do the various business units, product teams, and operational teams. In essence, the business sprawls, limiting overall effectiveness and efficiency. Because teams and business units today rely significantly on APIs, a similar effect is nearly inevitable: API sprawl.

Digital transformation and adoption of Cloud technologies is causing changes in application architectures. These changes introduce new attack surfaces, which are being targeted by advanced, highly organized, and financially driven attack methods. Utilizing NVIDIA's Morpheus framework, F5 is researching techniques to detect these advanced threats with real-time telemetry and AI-powered analytics.

Multi-cloud strategies are taking hold in the enterprise. But those strategies come with a formidable challenge—how to securely manage workloads across multiple cloud providers.

Observability, aka Monitoring 2.0, is a significant step forward in this technology journey for operators and digital business as they strive to understand and stabilize the relationship between user experience and business outcomes. But it’s only half the battle, and the other half involves analytics and automation.

Savvy CIOs know that the interface is just that: a means to a much more comprehensive digital experience that crosses functional and organizational boundaries. With most organizations firmly entrenched in the second phase of digital transformation—digital expansion—the need to digitize the entire enterprise architecture should be clearly rising as an inevitability.

Most organizations employ automation with infrastructure as code but still retain control over deployments. Looking forward, we’ll see more of an event-driven approach, where the trigger itself is automated based on an event. This is a significant part of what will make applications adaptive in the future—the ability to react to automatically to events and adapt location, security, and capacity to meet service-level objectives.

It should be no surprise that just as the emergence of cloud gave us cloud-native applications, edge is driving a set of edge-native applications. These applications, however, will not reside solely at the edge. Simultaneously, new architectural patterns are emerging that take advantage of applications residing in the cloud and data center.

The path that application delivery has taken over the last 20 years is a story of convergent evolution. More specifically, this era has seen the development of multiple delivery technologies that—when looked upon in hindsight—have revealed themselves to be the initial, nascent evolutionary steps towards what is becoming the application edge.

Digital transformation and the associated data generation are threatening to overwhelm the systems and companies that rely on data to create value. Accordingly, organizations are looking for more efficient, modern computing architectures that can support multi-tenancy and deliver applications at data center scale with all the necessary levels of performance and security.

Like Greek and Roman gods, multiple manifestations of the same persona are evident throughout many mythologies. At the core, each incarnation is a new face to an existing persona. A similar reality is true in the digital world; what we call “applications” can often be considered as incarnations of existing functionality.

We are living in an unprecedented time. Technology innovations routinely disrupt existing business models, and in some cases, completely replace existing industries—continuously and fundamentally changing the way we live and interact with each other. This post identifies three areas where AI is currently having a major impact on the enterprise.

The first in a series of blogs that looks at application-related technologies whose tide will be coming in during the next few years, especially as we evolve toward a more fully dispersed application delivery fabric and the emergent role of the edge. We’ll begin by examining where we are today (and the paths taken so far).

F5’s completion of its acquisition of Volterra marks the beginning of the next phase of edge computing, ushering in the Edge 2.0 era. We envision that in the Edge 2.0 era, digital business in every industry sector will adopt edge computing platforms to deliver applications and to process and analyze data. The edge platform will be an essential component of the user experience for all digital services.

It's no surprise that we all have at least a few devices in our homes that can and do connect to the Internet. What's surprising is that most of us—and most businesses, too—don't include the "IoT" as part of digital transformation. They should because digitizing products is absolutely a part of the business journey.

The payoffs of a well-constructed data architecture go beyond operational process. The benefits extend into strategic operational efficiencies, deeper business insights, and the ability to reach adjacent business opportunities, all executed in a more agile manner.

Whether we’re talking digital transformation, application architectures, or the importance of telemetry in maintaining a digital business, the soon-to-be-released results of our annual survey help shape F5's understanding of the market and strategic decisions. This year, we gave respondents a bit more latitude in providing freeform answers. They did not disappoint.

A thoughtful and deliberate data strategy is fundamental to enabling the quality and cost-effectiveness of the most important business workflows. Further, when the workflows are instrumented to transmit their observed data exhaust to a collection and analysis infrastructure, the workflows themselves can be continuously analyzed and improved, resulting in constantly adaptive and optimized business workflows.

While it's true that 2020 has seen different DDoS attack patterns emerge, what is also true is that DDoS attacks at the infrastructure layer are still DDoS attacks. They are what we might call "traditional" attacks. What is changing are targets and opportunities that come with a distributed workforce, along with considerations around 5G and Edge computing.

There's a big difference between knowing something's wrong and knowing what to do about it. Only after monitoring the right elements can we discern the health of a user experience, deriving from the analysis of those measurements the relationships and patterns that can be inferred. Ultimately, the automation that will give rise to truly adaptive applications is based on measurements and our understanding of them.

The industry saw nearly 250,000 attempts of credit card fraud in 2019. That breaks down to about one attempt every two minutes. Detecting fraud today requires a correlation of data, collected from as many points in the digital workflow as possible, and the ability to analyze it at speeds far greater than manual data processing can offer.

In the three phases of digital transformation, the first phase is all about automation. The focus on digitizing of workflows in phase two will ultimately offer business a path forward to the third phase, where data-driven services will generate actionable insights that improve efficiency, reduce process friction, and increase both productivity and profits.

First confined to the data center, Application Performance Monitoring (APM) has become increasingly context-driven around web-based user experiences. Today, it isn't enough to identify what went wrong after the fact. Businesses need to identify where trouble might occur before it happens.

There is an ebb and flow to technology cycles. Its inevitability is extant in many theories, such as the S-curve of innovation and a certain well-known analyst firm's hype cycle. Cloud is subject to these same waves. We've seen two very strong cycles over the past ten years, and it appears that a third wave is beginning to crest.

Data comes from a variety of sources across the code-to-customer path. Applications. Platforms. Application services. Infrastructure. Devices. All are capable of emitting data that can be turned into business value by the right solution. The elephant in the room during any discussion on the use of data is, of course, privacy.

Amid this pandemic, the systems processing unemployment claims in many U.S. states found themselves in peril. Developed using COBOL, the systems faced overwhelming demand, prompting urgent calls for those proficient in a programming language dating back to 1959. In parallel, many applications driving contemporary digital transformation efforts are microservices-based. These apps, like their COBOL forebears, are likely to be so critical that they, too, may still be running in 50 or 60 years.

An insertion point is an architecturally distinct location in the code to customer data path at which it makes sense to add functionality that is often outside the purview of development or operationally more efficient. Insertion points include the client, infrastructure, and the app itself. So, what we're looking for are app services that are both operationally and cost efficient at the point of insertion; in this case, we're focused on the app server (platform) itself.

The dominance of video and SaaS traffic today is, in part, why remote access services are being overwhelmed. In parallel, the rise of telemedicine during this pandemic is increasing and along with it, live video traffic. One way to increase capacity and improve performance for remote users is to update your remote access configuration to reflect the modern makeup of application traffic.

"Data is the new oil" or "Data is the grease of the digital economy." If you're like me, you've probably heard these phrases, or perhaps even the more business school-esque phrase "data exhaust monetization," to the point of being clichés. But like all good clichés, they are grounded in a fundamental truth, or in this case, in a complementary pair of truths.

Are we just using telemetry because it sounds sexier than data? Ultimately both data and telemetry are organized bits of information. To use them interchangeably is not a crime. But the reality is that, if you want to be accurate, there is a difference. And that difference will become increasingly important as organizations march into the data economy.

Put simply, one can define artificial intelligence as “teaching a computer how to mimic aspects of human intelligence.” To understand how AI and application services will work together in the future, it’s first necessary to examine three distinct types of AI: Strong, Weak, and Assistive.

The inability to verify the integrity or correctness of data should be of significant concern to those organizations pursuing digital transformation efforts (which rely heavily on data). That data will be used not only to conduct business but it also forms the basis for pattern and behavior recognition. Accordingly, it will power advanced analytics that automatically make operational and business decisions without human intervention.

What sets Shape and F5 apart is F5’s ability to capture high fidelity data from our position in front of millions of mission-critical customer applications combined with the sophisticated AI-assisted analytics platform from Shape. By integrating Shape and F5, we are executing on our vision to create an advanced set of security capabilities that can handle today’s most sophisticated attacks.

API stands for Application Programming Interface. Over the years, it has evolved from a tightly coupled imperative specification to a loosely coupled declarative model. Regardless of implementation and the mode of invocation, APIs tend to be associated with app development. But another API economy has been steadily expanding. It lies within operations. And in that domain, the "A" in API stands for automation.

Application architectures have evolved several times since the early days of computing, and it is no longer optimal to rely solely on a single, known data path to insert application services. Furthermore, because many of the emerging data paths are not as suitable for a proxy-based platform, we must look to the other potential points of insertion possible to scale and secure modern applications.

Digital transformation is about moving technology from business interactions to processes to new models. At first, it's about apps. But as app portfolios expand, it turns to focus on automation and orchestration. With the increase in data generation, transformation becomes the pivot point for new business opportunities.

The future of security rests on telemetry that is more than technical data points picked out of packets. It requires a holistic view of interactions from client to application to behavior. Machine learning requires enormous amounts of data to establish and recognize patterns. This is why programmable proxies are such a critical part of an advanced security approach.

Organizations are taking advantage of the proliferation of digital technologies to define new business models or to improve business productivity with existing models. While the pace of digital transformation varies based on the business and the sector it is in, overall, the journey of digital transformation has three stages.

Visibility remains key for organizations to secure, scale, and speed up applications. And as apps are increasingly distributed—across containers, clouds, etc.—the more widely we need to distribute the application services that provide heightened visibility to enhance performance.

Some attacks are merely a nuisance, degrading network performance or disrupting availability. Some others may be relatively rare but have far more serious impacts in the form of data breaches. Like the plagues spread by mice that wiped out cities in a bygone era, today’s attacks wipe out brand reputations and business value.