Industry executives and experts share their predictions for 2024.  Read them in this 16th annual VMblog.com series exclusive.

2024 Edge Computing, Hyperconverged Infrastructure, and Virtualization predictions

By Jeff Ready, CEO and co-founder, Scale Computing

As 2023 draws to a close, it's time to take stock of the year that was and look ahead to what the new year might look like. To aid in our prognostications, we've brought together some of our resident product experts and technical evangelists here at Scale Computing to share their insights and offer their forecasts on what trends might shape the edge computing, hyperconverged infrastructure, and virtualization market in the year ahead.

While LLMs Steal the Limelight, Computer Vision Will Reinvent Retail and Many Other Industry Verticals

The emergence of Large Language Models (LLMs) like ChatGPT this past year has been nothing short of astonishing, with hundreds of millions of users now using these intelligent chatbots as an essential part of their daily routine. However, while applications like ChatGPT have dominated the headlines over the past year, we believe that computer vision will be one of the most consequential applications of artificial intelligence (AI) in 2024 and beyond.

Using digital imagery and telemetry from cameras, video streams, infrared, thermal, depth sensors and LiDAR, AI-enabled computer vision enables machines to interpret and understand their surroundings with high precision, facilitating tasks such as object detection, pattern recognition, and environmental mapping - and then respond to what it "sees." While computer vision capabilities will benefit a wide range of industries, a few sectors have already begun to demonstrate the full potential of their capabilities. At the top of this list is the retail industry, which, in the face of continued labor shortages, has begun to invest heavily in AI-based computer vision to both improve the efficiency of their existing workforce as well as enhance the customer experience in novel ways. 

For instance, by integrating computer vision with connected IoT sensors - a combination of data from various sources like cameras and barcode scanners processed through AI-based analytics - retailers will be able to gain a more comprehensive and nuanced understanding of their store environments, transforming both their internal operations and how they interact with customers.

Use cases such as loss prevention, automated payments, inventory management, and bounding box technology are just the tip of the iceberg. We also expect to see retailers leveraging computer vision in 2024 as they begin piloting real-time demographic marketing capabilities to in-store customers, such as dynamic digital signage that can engage customers at the point of purchase and aims to deliver a truly individualized shopping experience to consumers.

AI Supercharges The Intelligent Edge

Edge computing is all about bringing computing power closer to the source of data, whether it's being generated by industrial equipment, IoT devices, or sensors scattered across a factory floor. The promise of the Intelligent Edge lies in its ability to provide faster, more reliable, and more efficient processing, leading to quicker decision-making and reduced reliance on centralized cloud systems, which can be plagued by latency and bandwidth issues.

However, realizing the promise of the Intelligent Edge has its challenges. One significant barrier is the technological complexity involved in deploying and managing edge computing infrastructures. The sheer volume and variety of data generated at the edge can overwhelm existing processing capabilities. There's also the issue of interoperability, as various devices and systems need to communicate seamlessly for optimal functionality. 

AI already plays a crucial role in overcoming these challenges and unlocking the potential of the Intelligent Edge. According to Gartner, "In 2022, perhaps 5% of edge computing deployments involve some level of Machine Learning - but by 2026, at least 50% of edge computing deployments will involve it." AI algorithms, designed to analyze vast amounts of data quickly and accurately, are ideal for the data-rich environments of edge computing. 

By integrating AI, edge devices can autonomously process and act upon the data they collect without needing to be in constant communication with a central server. This integration enhances decision-making speed and efficiency, critical in applications like real-time inventory management or calibrating industrial equipment on the factory floor. Moreover, AI will be instrumental in enhancing the security and reliability of edge computing systems by enabling them to detect and respond to anomalies in real-time.

Integrating Legacy and Cloud-Native Application Design with the Convergence of VMs & Containers

As enterprises seek to streamline their development and deployment processes, the ability to run legacy virtual machine workloads alongside containerized workloads is emerging as a key competitive advantage for today's enterprise, offering a cost-effective means to integrate their legacy applications with modern cloud-native environments. This shift also recognizes a fundamental desire among application developers: the need for portability without the burden of worrying about the underlying hardware or operating system or deployment model (centralized cloud vs. distributed edge). By erasing the distinction between VMs and containers, developers can instead focus on the efficiency and scalability that such a converged environment can offer.

This convergence will be further augmented by the emerging concept of ‘pick your own control plane,' enabling developers and IT admins to consolidate their resources across disparate systems. Instead of maintaining separate ‘islands' for running VMs and containerized applications, the goal is to empower application teams to select their container management toolset of choice. This simplifies management, maximizes resource utilization, and helps streamline operations across the board.

The adoption of such an integrated approach will likely only accelerate as organizations recognize the benefits of a unified system. The convergence is more than a mere convenience; it's a strategic transformation that enables faster deployment, better resource management, and a more coherent approach to cloud-native development. As the barriers between shared common infrastructure disappear, developers and IT teams will be able to focus more on innovation and spend less time managing disparate systems.

Kubernetes at the Edge Goes Mainstream

A powerful orchestration tool for containerized applications, Kubernetes (K8S) has quickly become an essential component in modern IT infrastructure, streamlining the deployment, scaling, and management of applications - regardless of where those applications might be hosted. As enterprises look to improve their real-time decision-making capabilities, we expect that K8S at the edge will become the norm, not the exception, in 2024.

However, it's important to note that the scaling capabilities of K8S, often essential in cloud environments, are not typically required at the edge. The key advantage of K8S in edge computing lies in its ability to provide a standardized environment for application deployment, creating a more uniform process for developers to define, build, test, and deploy their edge-based applications.

And because K8S supports a wide range of workloads - from lightweight IoT applications to more compute-intensive AI and machine learning models - this versatility makes it well-suited for the wide-ranging requirements of edge computing. Of course, one of the primary reasons why companies move to the edge in the first place is to ensure system reliability and availability. 

However, while K8S is often heralded for its self-healing capabilities, it can be complex to set up and deploy, requiring specialized knowledge and resources to effectively manage these  capabilities in an edge computing environment.

##

ABOUT THE AUTHOR

Jeff is the CEO and co-founder of Scale Computing. Prior to founding Scale Computing, Jeff was co-founder and CEO of Corvigo, a Linux-based anti-spam appliance, where he oversaw the company from startup through funding to acquisition. After the acquisition, Jeff served as VP of Marketing at Tumbleweed Communications. Prior to Corvigo, Jeff was co-founder, COO and VP of Marketing at Radiate. Jeff holds a degree in Computer Science from Rose-Hulman Institute of Technology.