In today’s fast-paced digital landscape, the need for efficient data processing and reduced latency has become paramount.

As a response to this demand, a new computing paradigm known as edge computing has emerged. Edge computing brings computation and data storage closer to the source of data generation, enabling real-time processing and reduced reliance on centralized cloud infrastructure. This article will delve into the rise of edge computing, its implications for various industries, and how it is transforming the future of technology and connectivity.

What is Edge Computing?

Edge computing can be defined as a decentralized computing model that places computational resources and data storage closer to the edge of the network, near the source of data generation.

Unlike traditional centralized cloud computing, where data is processed in remote data centers, edge computing performs computation and analysis at or near the edge devices themselves. This decentralized architecture aims to bring processing power and storage capabilities closer to the data source, enabling faster and more efficient data processing.

Advantages and Benefits of Edge Computing

One of the key advantages of edge computing is reduced latency. By processing data closer to the edge devices, the time it takes for data to travel to and from remote cloud servers is minimized. This reduction in latency enables faster response times, making it ideal for real-time applications where immediate processing and decision-making are crucial.

Edge computing also enhances reliability. By distributing computing resources across multiple edge devices, the system becomes less reliant on a single central server. This redundancy improves system reliability, as failures in individual edge devices do not lead to complete service disruptions. It also mitigates the risks associated with network connectivity issues or latency problems that can occur when relying solely on centralized cloud infrastructure.

Furthermore, edge computing enables real-time decision making. By analyzing and processing data at the edge, immediate insights and actions can be taken without the need for constant communication with remote cloud servers. This capability is particularly valuable in time-sensitive applications such as autonomous vehicles, where split-second decisions can significantly impact safety and efficiency.

Applications and Use Cases of Edge Computing

Edge computing has a wide range of applications across various industries. In the Internet of Things (IoT) domain, edge computing plays a crucial role. By performing local data processing and analysis at the edge devices, it reduces the need for constant communication with the cloud, improving efficiency, reducing bandwidth requirements, and enhancing security.

Autonomous vehicles also heavily rely on edge computing. The ability to process data in real-time at the edge enables object recognition, collision detection, and immediate response times, making autonomous driving safer and more efficient.

In the healthcare industry, edge computing has the potential to revolutionize patient care. Remote patient monitoring, real-time diagnostics, and personalized treatment plans can all benefit from the fast and localized processing capabilities of edge computing. By analyzing health data at the edge, healthcare professionals can provide timely interventions and improve patient outcomes.

Challenges and Considerations of Edge Computing

While edge computing offers numerous advantages, it also comes with challenges that need to be addressed. Security and privacy are major concerns, as sensitive data is processed and stored closer to the edge devices. Proper encryption, data protection measures, and secure communication protocols are essential to mitigate these risks.

Scalability and management of distributed edge infrastructure can also be challenging. As the number of edge devices increases, ensuring seamless integration, network connectivity, and device management becomes crucial. Standardization and interoperability among different edge computing solutions are necessary to create a cohesive and scalable ecosystem.

What’s Next for Edge Computing?

Organizations using edge computing to power their IoT systems can minimize the latency of their network, i.e., they can minimize the time for response between client and server devices. The rise of edge computing represents a significant shift in the way data is processed, analyzed, and utilized. By bringing computation closer to the edge of the network, edge computing offers reduced latency, improved reliability, and real-time decision-making capabilities. With applications spanning across IoT, autonomous vehicles, healthcare, and beyond, edge computing is reshaping the future of technology and connectivity.

Cisco has unveiled its vision for Cisco Networking Cloud, an integrated management platform experience for both on-prem and cloud operating models.

Building a Better Future for Cisco Customers and Partners
Managing networks in today’s era of connecting everyone, everywhere is hard. According to Cisco’s State of Global Innovation report, 85% of IT professionals indicate they value simplicity in their IT systems. Simplicity becomes increasingly important with the advancement of cloud, IoT, Wi-Fi + 5G, AI/ML, and security. With so many technologies and applications coming together, it can be difficult for IT staff to deliver a consistent, unified experience whether in the office, at home, or on the go.

A simplified IT experience influences customer satisfaction, employee retention, and competitive differentiation. Cisco recognizes the struggles with fragmentation, lack of visibility, security threats, and time-consuming integration that get in the way of delivering better experiences. It understands that the journey to simplification is defined by each operator’s business objectives, functional needs, and preferred consumption model. Whether the use cases require on-premises delivery, cloud-enabled delivery or anything in between, Cisco is meeting IT where they are.

The Vision for Cisco Networking Cloud

As part of its journey to simplification, Cisco has been working to create a simpler network management platform experience to help customers easily access and navigate its platforms to manage all Cisco networking products from one place. Featuring cloud-driven automation, rich network insights, and innovation through its partner ecosystem, Cisco Networking Cloud will accelerate the delivery of unified experiences and drive measurable business outcomes.

“Today we are sharing our vision and first steps to eliminate networking complexity and securely connect the world,” said Jonathan Davidson, executive VP and GM, Cisco Networking. “Only Cisco has the portfolio, experience, and partner ecosystem to bring together campus and branch, data center, compute, IoT, and SD-WAN to optimize outcomes using one networking management platform to deliver unified experiences.”

From Vision to Value: Innovation Launching Today

• As a first step, Cisco is delivering the following components across its existing networking products portfolio, which will increase operational simplicity, efficiency, and reliability:
  • Single sign-on (SSO) simplifying access across Cisco networking platforms.
  • API key exchange/repository, when linked with SSO, making it easier for Cisco networking platforms to connect and exchange data through automation to reduce friction and opportunities for error.
  • Cross-platform navigation, delivering more seamless navigation between Cisco networking platforms.
  • Common user interface across Cisco networking platforms, bringing greater consistency and ease of use across a customer’s operational functions.

• Elevating the power of the network with end-to-end assurance and expanded cloud monitoring capabilities:
  • Cisco ThousandEyes for end-to-end network assurance over any network: ThousandEyes delivers expanded visibility, automated insights, and seamless workflows to assure digital experiences across any network—whether on premises, the internet, or in the cloud. New innovations include:
    • Expanded visibility into internet and cloud networks with new vantage points on Meraki MX and Webex RoomOS devices.
    • Faster insights into incidents impacting digital experiences with new automated Event Detection plus unmatched insight into your AWS connections for enhanced troubleshooting.
    • Seamless workflows with simplified ThousandEyes endpoint deployment with Cisco Secure Client, adding to ThousandEyes’ already rich set of ecosystem integrations, including data export via OpenTelemetry.
  • Cloud Monitoring for Catalyst to view, troubleshoot and manage Catalyst devices: Enhancements to the Meraki dashboard will now support new capabilities for Cisco Catalyst switches including a CLI view, image management, and advanced troubleshooting. 

• Simplifying operations with an easier, more predictable, and more scalable Cisco Catalyst stack, improved visibility into data center power consumption insights and energy footprints, and new AI data center blueprints:
  • Simplified branding for the Cisco Catalyst Stack: Cisco is now connecting the power and flexibility of the Catalyst brand across the entire enterprise networking stack with Catalyst Center (formerly DNA Center), Catalyst Software and Licensing (formerly DNA Software and Licensing), Catalyst Wireless, Catalyst Switching, Catalyst Routing, and Catalyst SD-WAN (formerly Cisco SD-WAN or Viptela SD-WAN). 
  • New Cisco Catalyst SD-WAN Consumption Model: With cloud-delivered Cisco Catalyst SD-WAN, customers can now consume SD-WAN as a utility with a flexible subscription model. Customers can simply purchase and have the SD-WAN software and services spun up in minutes. Cisco will be responsible for management of the underlying delivery of the SD-WAN fabric automating solution with zero-touch lifecycle deployment and management.
  • Simplified Licensing Options: Starting with Cisco Catalyst switches, new licensing combines hardware and software support into a single subscription—simplifying buying and renewing.
  • Sustainable Data Center Networking Bolstered by new integrations for Cisco data center networking and Nexus Dashboard, customers will gain real-time and historical insights for power consumption of all IT equipment in their data center and estimate the energy footprint of their data center operations. 
  • AI Data Center Blueprint for Networking: Leveraging Cisco experience with customer deployments, the Cisco AI/ML data center network blueprint will give customers a new and proven solution for high performance compute, InfiniBand to Ethernet network migrations, and large-scale ML fabrics. With visibility into AI workloads via Cisco Nexus Dashboard and automation templates, customers can meet the demand for specific network performance characteristics such as deterministic load-balancing, line-rate transmission, congestion management and no drop characteristics with their Cisco Nexus 9000 and NX-OS implementations.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: Cisco, networking

Like a science fiction novel, artificial intelligence (AI) is ready to disrupt how many industries operate in the 21st century.

For some of these industries, the advantages of AI are a boon. For others, the changes might be a hindrance. Whether a company can adapt to this new technology might make or break them. Here are ten industries disrupted by AI.

1. Healthcare

One of the most critical industries that AI disrupts is healthcare. The applications of AI and machine learning programs are what allowed the healthcare industry to meet the rapidly increasing needs of people during the COVID-19 pandemic. 

While most people think of healthcare as doctors and medical science, many administrative duties go on behind the scenes. Scheduling, filing insurance, and medical reports are the backbone of hospitals and healthcare facilities. That’s where AI comes in. Automating processes such as filing insurance, taking patient calls, and helping scheduling tasks can free nurses and healthcare staff to use their time on more pressing matters, such as taking care of patients. 

Artificial intelligence has also made contributions to medical labs. Automating much of the testing process has allowed laboratories to keep up with the need for testing despite a staff shortage. Ensuring that tests are done on time ensures patients get the care they need as quickly as possible.

2. Manufacturing

Another industry disrupted by AI is manufacturing. Over the past several years, manufacturing has shifted towards automation using computer and robotics technology. These machines perform mundane but essential tasks, such as assembly and management.

The use of robotics technology makes the manufacturing process more efficient. One application of AI is learning how to perform repetitive tasks as quickly as possible. AI uses formulas and templates to perform necessary functions. Some tasks, such as gage calibration, are essential yet mundane — something that artificial intelligence can perform effortlessly, compared to humans who may become restless or bored. 

Combined with robotics technology, AI can perform repetitive tasks better and with fewer mistakes than humans. This is a game changer in the manufacturing industry, where repetitive processes are common. In addition to bearing some of the workload, AI machines can also keep workers safe by taking on more dangerous tasks.

3. Weather Prediction

AI is also making strides in meteorology, specifically the ability to make weather forecasts. Predicting the weather involves a lot of computational power from many scanning devices worldwide. Even with the power of supercomputers, the ability to predict the weather is limited as forecasts degrade the further into the future you go.

Artificial intelligence and machine learning can make weather prediction more accurate and more efficient. Satellites are constantly sending new data to supercomputers, making it arduous to sift through it all. AI’s ability to handle large data sets makes it perfect for this task — compiling reports of all the data collected for users to read.

Machine learning takes this capability a step further by making predictions based on the data collected without human input. This feature allows for accurate predictions further into the future than ever before.

4. Customer Service

As AI chat programs like ChatGPT become more sophisticated, people-facing industries like customer service are adopting them to help fulfill more tasks. Automated calls and answering robots can remove much of the burden from human representatives by completing mundane tasks and answering frequently asked questions. 

AI programs can even guide customers through several processes, such as making payments and changes to their accounts. This capability allows representatives to use their time helping customers with genuinely pressing issues and personalized assistance.

5. Advertising and Marketing

AI and machine learning applications are even making their way into the advertising and marketing industries. The key to any successful marketing strategy is to have the correct data to fulfill the company’s purposes. Artificial intelligence can make gathering that data faster and more efficient than ever. 

AI tools can be used to launch surveys and observe a target market’s interests so companies can properly plan their advertising campaigns. AI can even help design advertising materials, such as signs and containers, based on trends the target audience is interested in.

6. Finance

The financial sector is another industry disrupted by AI. Many banks and other financial institutions were deeply affected by the COVID-19 pandemic. When people could no longer visit these places, they turned to digital banking to manage their finances. 

Digital banking continues to be the preferred method for people to work on their finances. AI applications make managing money from anywhere a much simpler affair. Chatbot programs and voice assistants can guide consumers to the solution to any problem they might face. 

In addition to proving convenience, AI has made digital banking more secure by tightening cybersecurity — ensuring that hackers and viruses stay out of people’s bank accounts.

7. Cybersecurity

Artificial intelligence isn’t just raising cybersecurity for banks. As the world becomes more reliant on digital technology, the rate of cyber attacks on businesses and individuals has also skyrocketed. New cybersecurity tools powered by machine learning have emerged to combat these ever-growing threats.

These tools can perform analysis of the newest and most common cybersecurity threats and scan internet traffic for them — destroying malicious programs before they can enter the system. Machine learning allows for more accurate threat detection and can even automate tasks such as alert response and reports.

8. Logistics

As worldwide events such as the conflict in Ukraine create difficulties in the global supply chain, the applications of AI have become important to keeping logistics on track on a local and international scale. Artificial intelligence can allow greater interaction between companies, ensuring that all parties involved in logistics are kept informed of each other’s activities. 

Being aware of any disruptions in the supply chain allows companies to adjust their expectations and make plans to minimize the impact of said disruptions. It can also streamline the travel process and give greater insight into the status of goods traveling from long distances.

9. Retail

New applications of AI are also disrupting the retail sector. Digital tools are becoming the standard for retail management. Tasks such as inventory management and payment processing can be automated using artificial intelligence — making it easier for companies to retain employees and make business processes more efficient.

10. Lifestyle

Finally, artificial intelligence technology has become a facet of everyday life. AI operates on every level of the modern home, from personal computers to voice assistants like Alexa and Siri. As digital technology advances, machine learning will play an even bigger part in everyday life.

Self-driving cars and buses, more accurate GPS and virtual hobbies are all poised to become big industries in the near future. Meanwhile, voice assistants are growing more sophisticated as machine learning continues to evolve.

AI Is the Future of Every Industry

Artificial intelligence was once thought of as something you could only find in a sci-fi novel. Now, it has become a fact of life. AI and machine learning have made places for themselves in every industry — and they’re here to stay.