Smart Edge: The Future of Cloud Computing?

Smart Edge: The Future of Cloud Computing?

The intelligent edge refers to data collection and analysis where the data is generated. It could be the point where a user, such as a mobile worker, is doing their job, or an IoT device connected to industrial equipment is generating data. Data is collected, processed and analyzed at this remote point, which means that the intelligence is at the “edge” of the organization’s IT architecture. This ‘decentralization’ of data storage, processing, and intelligence capabilities removes much of the workload from the central processing center, data center, or cloud.

Edge computing is a powerful enabler when intelligence is injected into it to enhance its potential for disruption and enrich the value of data captured at its proverbial source, says Scott Cowling, director of network solutions at BT. “The sheer volume of data created through the IoT, where information is streamed from sensors to optimize operations, makes this intelligence a business imperative.”

For Hanno Brink, machine learning engineer at Synthesis, the technical challenge that the intelligent edge tries to solve is the efficient use of resources, increasing the robustness and reducing the cost of implementing and managing this technology.

“There are many benefits that could be unlocked if this technology is applied correctly. One is reducing latency between when data is collected and acted upon, which has applications like predictive maintenance, fleet maintenance, and many others where real-time visibility and responsiveness would help reduce operating costs or improve service delivery.

“Another would be for smart applications to be delivered to customers where limited bandwidth or connectivity has previously prevented this, or where customer data has not been easily collected and put to work. This technology could also be used to collect data that was previously inaccessible, due to the cost of implementation, and to deliver new customer experiences or improve current service delivery through the use of intelligence where the customer interacts with it.” .

Forrester’s 2021 ‘Building Integrated Technology Platforms to Accelerate Growth and Agility’ report tells IT leaders: “At this stage of technology maturity, innovate with technology to serve customers across the entire product lifecycle. customer, mastering commitment and knowledge systems”.

This is precisely what the intelligent edge allows us to do, says Varsha Ramesar, executive director of data and analytics at iOCO. “From a technology perspective, the intelligent edge has several benefits, including reducing reliance on network performance, and enabling the business to increase its bottom line by reducing overhead. What organizations need to keep in mind, however, is that the real value of the intelligent edge lies in how it can expand and amplify an enterprise’s ability to detect and respond with greater speed and agility, whether in the context of predictive maintenance or the client’s. Service.”

Navinder Singh, general manager of In2IT Technologies, says that edge computing emerged in recent years driven by some technology companies that developed the technology. When the intelligent cloud arrived, it was a great innovation, helping to overcome some of the fundamental problems of cloud computing, such as latency, bandwidth requirements, and cost containment. In the past year, cloud providers have been accelerating intelligent cloud offerings, where data sitting at the edge with built-in intelligence can still interact with and stream to and from the cloud.

In addition, he says that the investment in the edge has changed its potential and scope. “If we look at the intelligence built into edge devices, we see advancements that align with cloud strategies and one of the most challenging elements: cloud integration. Intelligence is creating seamless integration of cloud applications through AI. For example, companies are often forced to integrate their CRM and ERP solutions with cloud service providers, but it takes a lot of resource effort for these applications to achieve a seamless integration. The AI ​​collects information from the edge applications, and since the integration requirements are predefined, this significantly simplifies the integration process and, as a result, reduces costs. It also removes bottlenecks where processing and analysis used to be done centrally with data alignment across multiple applications. Processing becomes faster and more efficient.”

Moving data and compute closer to the edge also means that this infrastructure is moving further out of your control and much more exposed.

Hanno Brink, Synthesis

However, like any technology that is relatively in its infancy, the smart edge is not without its challenges. Brink says these include creating more efficient hardware, creating more compute-efficient AI, finding more efficient ways to manage limited bandwidth and storage resources, securing the edge, delivering machine learning to the edge to preserve privacy and overcome a myriad of challenges presented by implementing systems that interface with the real world.

Several proof-of-concept and proof-of-value projects have demonstrated the ROI and business benefits of intelligent edge implementations that bring AI and ML to edge environments, adds Ramesar. However, the challenge now is determining how to scale these deployments to hundreds or thousands of sites so that organizations can take full advantage of the business-critical data they are generating at the edge. Before companies can realize the benefits of the edge and embark on their industrial digital transformation, they must consider their data. The volume and speed of data is growing astronomically, and availability is key. To support these applications and use cases, sensors and related contextual data must be ingested, processed, and analyzed in the right place, at the right time, and provided to the right people.

For Cowling, one of the main concerns at the edge and one of the biggest barriers to deployment is cybersecurity. Increasingly, the edge is becoming a point of convergence between two worlds: operational technology (OT), including the industrial systems that run equipment in factories, refineries, and mines, and IT. Industry 4.0 solutions like predictive maintenance need data from both worlds like SCADA from OT and ERP from IT. air gaps “Once you connect the dots, critical processes become vulnerable because they run on old proprietary software, with poor password protection, limited patching, and no authentication. Therefore, identifying and mitigating vulnerabilities becomes a major focus area.”

When it comes to securing the smart edge, Brink says the most crucial component of any edge app is making sure your app is protected from the device, to the cloud, and back. “Moving data and computing closer to the edge also means that this infrastructure is moving further out of your control and becoming more exposed. Any such application must be designed with security in mind.

“Beyond data security, physical security and reliability also become factors that need to be considered and properly balanced against functionality. In addition, secure physical devices must be used, and these devices must be robust against the extreme environments in which they can operate.”

Organizations need to ensure systems can scale effectively while ensuring consistent security implementation, and they need to define an organizational root of trust. This is a way for edge devices to authenticate themselves to the enterprise and prevent privileged systems from being spoofed and their access abused.

Security and privacy risks can be reduced by limiting data flows between the point of collection and the core infrastructure, particularly when those flows occur over the public Internet, Cowling adds. Using the smart edge helps companies comply with country data protection laws. It keeps sensitive data within the device, anonymizing and analyzing it at the source instead of sending identifiable information to the cloud.

Secure Access Service Edge, or SASE, is also key to uniting network connectivity and security into a single policy-based service that provides consistent, centrally managed access and security from edge to edge. SASE also supports a zero-trust approach to the cloud and underlying infrastructure, which means sessions are protected no matter where the edge device connects.

As we move into cloud and hybrid cloud environments, the benefits that the intelligent edge brings make it a compelling technology acquisition decision.

Navinder Singh, In2IT

Securing the edge is not only daunting, it can seem downright impossible, considering the unprecedented number of devices on the network, generating data every second of the day, all of which must be ingested, transformed, and analyzed by computing platforms in the wild. , and everything that needs to be blocked.

“Surprisingly, many IT professionals think of security as protecting perimeters and implementing strong access control. However, security in today’s edge and cloud era is much more complicated. With the explosion of IoT, and Industrial IoT in particular, the attack surface has increased, as has the number of attack vectors. Since edge computing is a distributed model, its security concerns are very different from a centralized model. To save costs and speed deployment, many edge devices do not natively encrypt data, and IT administrators need a security framework before deploying large-scale edge projects,” says Ramesar.

Ultimately, as we move closer to cloud and hybrid cloud environments, the benefits that the intelligent edge brings make it a compelling technology acquisition decision, says Singh. It helps streamline business processes, data alignment across multiple applications, easy integrations, and more. It’s also about the business optimizing its operations and driving efficiency, and in an age of ‘always on’ and ‘instant gratification’, the intelligent edge is the way forward.

* This feature was first published in ITWeb’s May issue Great idea magazine.

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