Edge computing moves towards full autonomy

Edge computing is rapidly losing its reputation as a fringe concept, and adopters and vendors alike are setting their sights on the technology’s next goal: fully autonomous deployment and operation.

The edge deployment experience is approaching the simplicity of unboxing a new mobile phone, says Teresa Tung, first chief cloud technologist at IT advisory and consulting firm Accenture. “We’re seeing automated technology that simplifies handling the unique complexity of the edge for application, network and security deployments.”

The ability to build and manage containerized applications enables seamless development and deployment in the cloud, with the edge simply becoming a specialized location with tighter resource constraints, says Tung. “Self-organizing and self-healing wireless mesh communication protocols such as Zigbee, Z-Wave, ISA100.11a, or WirelessHART can create networks in which devices can be deployed ad hoc and self-configured.”

Decentralizing IT environments to encompass edge systems presents unique challenges, says Matteo Gallina, principal consultant at global technology research and advisory firm ISG. “Device and service management must be done outside of the traditional management sphere, including managing physically inaccessible devices, a wide variety of solutions and operating systems, different security requirements, and more,” he says. “The larger and more dispersed systems become, the more important role automation plays in ensuring efficiency and reliability.”

Innovation in automation technology led by open source communities

The trend toward automating edge deployments is not unlike the journey toward AI, where innovations are being led by open source groups, infrastructure manufacturers, and cloud service providers, says Tung. He points out that open source communities, like LF edge— are leading innovations and creating critical standard definitions in areas such as communication, security, and resource management.

“Infrastructure providers are creating solutions that allow computing to run anywhere and be integrated into anything,” says Tung. “It includes ultra-low-power, ultra-fast, connected-anywhere, ultra-secure and private new hardware capabilities.” She adds: “5G opens up new opportunities for network equipment providers and telecom operators to innovate with public and private networks with embedded edge computing capabilities.”

At the same time, innovations from cloud providers are making it easier to extend centralized cloud DevOps and management practices to the edge. “Like [the] central cloud makes it easy for any developer to access services, now we’re seeing the same thing happen with technologies like 5Grobotics, digital twinY internet of thingsTung says.

Software-defined integration of multiple network services has become the most important technology approach for automating edge deployments, says Ron Howell, managing network architect at Capgemini Americas. Safety net, equipped with zero trust deployment methods that incorporate SASE edge features, can significantly improve automation and simplify what it takes to implement and monitor an edge computing solution. Additionally, when implemented, full-stack observability tools and methods that incorporate AIOps will help proactively keep compute and edge resources available and reliable.

AI applied to the network edge is now widely seen as the way forward in network edge availability. “AIOps, when used in the form of full-stack observability, is a key improvement,” says Howell.

A variety of options are already available to help organizations looking to move toward edge autonomy. “These start with onboarding and managing physical and functional assets, and include automated software and security updates, and automated device testing,” explains Gallina. If a device is running any kind of ML or AI functionality, AIOps will be needed, both at the device level to keep the local ML model up to date and ensure the right decisions are made in any situation, and within any ML/AI backbone. that may be located on premises or in centralized perimeter systems.

Physical and digital experiences come together at the edge

Tung uses the term “phygital” to describe the outcome when digital practices are applied to physical experiences, as in the case of autonomous management of edge data centers. “We see creating highly personalized and adaptive phygital experiences as the ultimate goal,” she says. “In a phygital world, anyone can imagine an experience, build it, and scale it.”

In an edge computing environment that integrates digital processes and physical devices, hands-on network management is significantly reduced or eliminated to the point where network faults and downtime are automatically detected and resolved, and configurations they are applied consistently across the infrastructure, making scaling simpler and more effective. faster.

Automatic data quality control is another potential benefit. “This involves a combination of sensor data, edge analytics, or natural language processing (NLP) to control the system and deliver data to the site,” Gallina says. Yet another way a self-driving edge environment can benefit businesses is with “touchless” remote hardware provisioning at scale, with the operating system and system software automatically downloaded from the cloud.

Gallina points out that an increasing number of edge devices now come with dedicated operating systems and various other types of support tools. “Next-generation apps and marketplaces are starting to become available, as well as an increasing number of open source projects,” she says.

Vendors are working on solutions to seamlessly manage edge assets of almost any type and with any underlying technology. Edge-oriented open source software projects, for example, like those hosted by the Linux Foundation, can further drive adoption at scale, Gallina says.

AI-optimized hardware is a promising edge computing technology, Gallina says, with many products offering interoperability and resiliency. “Solutions and services for edge data collection (QA, management, and analytics) are likely to expand tremendously in the coming years—just as cloud-native applications have,” she adds.

AI on Edge automation leaders include IBM, ClearBlade, Verizon, hyperscalers

Numerous technologies are already available to businesses considering edge automation, including offerings from hyperscaler developers and other specialty vendors. An example is KubeEdge, which offers Kubernetesan open source system for automating the deployment, scaling, and management of containerized applications.

Gallina notes that in 2021 ISG ranked systems integrators Atos, Capgemini, Cognizant, Harman, IBM and Siemens as world leaders in AI technology at the edge. Leading edge computing vendors include hyperscalers (AWS, Azure, Google) as well as edge platform providers ClearBlade and IBM. In the telecommunications market, Verizon stands out.

Edge-specific features provide autonomy and reliability

Providers are incorporating physical and digital availability features into their offerings in an effort to make edge technology more autonomous and reliable. Providers typically use two methods to provide autonomy and reliability: internal sensors and redundant hardware components, Gallina says.

Embedded sensors, for example, can use on-site monitoring to monitor the environment, detect and report anomalies, and can be combined with failover components for the required level of redundancy.

Tung lists several other approaches:

  • Tamper-resistant physical features designed to protect devices from unauthorized access.
  • Secure identifiers built into chipsets that enable easy and reliable device authentication.
  • Self-configuring network protocols, based on ad hoc and mesh networks, to guarantee connectivity whenever possible.
  • Partitioned boot configurations so updates can be applied without the risk of bricking devices if the installation goes wrong.
  • Hardware watchdog capabilities to ensure devices will automatically reboot if they stop responding.
  • Boot-time integrity checking from a secure root of trust, which protects devices from installing malicious hardware.
  • Trusted computing and secure execution environments to ensure approved computing runs on protected and private data.
  • Firewalls with anomaly detection that capture unusual behavior indicative of emerging flaws or unauthorized access.

Self-optimization and AI

Networks require an almost infinite number of configuration settings and fine-tuning to function efficiently. “Wi-Fi networks need to adjust based on signal strength, firewalls need to be constantly updated with support for new threat vectors, and edge routers need constantly changing configurations to enforce service level agreements (SLAs)” says Juniper member Patrick MeLampy. at Juniper Networks. “Almost all of this can be automated, saving human labor and human error.”

Self-optimization and AI are necessary to operate at the edge and determine how to handle change, says Tung. What should happen, for example, if the network fails, the power goes out, or a camera is misaligned? And what should happen when the problem is fixed? “The edge will not scale if these situations require manual intervention every time,” she warns. Troubleshooting can be addressed by simply implementing a rule to detect conditions and prioritizing application deployment accordingly.

key takeaways

The edge is not a single technology, but a collection of technologies that work together to support an entirely new topology that can effortlessly connect data, AI and actions, says Tung. “The biggest innovations are yet to come,” she adds.

Meanwhile, the pendulum is swinging toward more numerous but smaller edge hubs located closer to customer needs, supplemented by larger cloud services that can handle additional workloads that are less time-sensitive, less critical and less sensitive to latency, says Howell. She points out that the one factor that remains unchanged is that the information must be highly available at all times. “This first rule of data centers has not changed: high-quality services that are always available.”

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Copyright © 2022 IDG Communications, Inc.

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