The End of Cloud Computing | Summary and Q&A

TL;DR

Cloud computing is being replaced by edge computing, where data processing occurs at the edge devices, such as cars, drones, and robots.

Key Insights

• 🦔 Cloud computing is being disrupted by the rise of edge computing, where processing occurs at the edge devices.
• 😶‍🌫️ Edge devices, such as self-driving cars and drones, collect and process vast amounts of real-time data that cannot be efficiently handled by the cloud.
• 💁 Machine learning is essential for extracting relevant information from the unstructured data collected by edge devices.
• 👶 Edge computing will require new programming languages and a focus on data-centric programming.
• ✊ The increased processing power and reduced cost of sensors will drive the adoption of edge devices in various industries.
• 🦔 The networking and security challenges posed by edge computing will need to be addressed to effectively manage and secure the massive amount of data generated.
• 🦔 The shift towards edge computing will transform industries, combining consumer-oriented applications with enterprise manageability.

Transcript

I'd like to show you a little bit of what I think the future is going to look like I'm going to take you out to the edge no pun intended I know what you're all thinking like how can I actually say that cloud computing is coming to an end when it hasn't really started yet well let me show you and I here's what I I think you probably all think that I... Read More

Questions & Answers

Q: What is edge computing, and why is it replacing cloud computing?

Edge computing is the processing of data at the edge devices, such as cars and drones, instead of sending it to a centralized cloud. It is replacing cloud computing because edge devices can collect and process massive amounts of real-time data, which is not feasible with the latency of sending data back and forth to the cloud.

Q: How is machine learning contributing to edge computing?

Machine learning algorithms are necessary to extract relevant information from the unstructured and highly variable data collected by edge devices. These algorithms run on the edge devices themselves, enabling real-time decision-making without the need for the cloud.

Q: What are the implications of the shift towards edge computing?

The shift towards edge computing will lead to a data explosion that will overload the existing cloud infrastructure. It will also require new programming languages and a focus on data-centric programming. Additionally, the increased processing power at the edge and the commoditization of sensors will drive the proliferation of edge devices in various industries.

Q: How will edge computing impact networking and security?

Edge computing will present challenges in managing and securing the massive amount of data generated by edge devices. The networking infrastructure will also need to support the communication and coordination of trillions of peer-to-peer devices.

Summary

This video explores the idea that cloud computing may be coming to an end and discusses the shift towards edge computing. The speaker suggests that as mobile devices become more sophisticated, they may take over the role of the central cloud, and the edge devices such as self-driving cars, drones, and robots will become the new data centers. The speaker also emphasizes the importance of real-time data processing at the edge and the role of machine learning in catalyzing the adoption of edge computing. Additionally, the video discusses the concept of sense, infer, and act at the edge, and how data-centric programming and new programming languages will play a significant role in this shift. The video concludes by highlighting the challenges and opportunities that the rise of edge computing will bring, and the need for organizations to prepare for this transformation.

Questions & Answers

Q: How does the speaker predict the future of cloud computing?

The speaker suggests that as mobile devices become more sophisticated, they may replace the central cloud, and the edge devices such as self-driving cars, drones, and robots will become the new data centers.

Q: What is the concept of sense, infer, and act at the edge?

Sense, infer, and act at the edge refers to the three elements of processing that occur at the edge in edge computing. First, the edge devices sense the environment using various sensors like cameras, depth sensors, and accelerometers. Then, they use machine learning algorithms to infer relevant information from the collected data. Finally, the edge devices take action based on the insights gained from the inference step.

Q: How does real-time data processing at the edge differ from processing in the centralized cloud?

Real-time data processing at the edge is necessary because there is latency in sending data back to the centralized cloud for processing. For example, in the case of a self-driving car, if the data had to be sent to the cloud for decision-making, it would take too long for the car to respond to immediate situations like a stop sign or a person crossing the road. Therefore, real-time processing at the edge is crucial for critical safety and responsiveness.

Q: What is the role of machine learning in edge computing?

Machine learning plays a significant role in edge computing as it helps extract relevant information from the vast amount of unstructured and variable data collected at the edge. Machine learning algorithms infer patterns and insights from the data, allowing the edge devices to make informed decisions and take appropriate actions. The speaker emphasizes that machine learning algorithms will run at the endpoint devices themselves, rather than in the centralized cloud.

Q: How does the concept of distributed computing tie into the shift towards edge computing?

The shift towards edge computing is described as a return to distributed computing. Just as computing has evolved from mainframes to client-server models to mobile cloud, edge computing represents a shift back to a distributed computing model. The edge devices will connect together, creating a network of endpoint devices that process and share data among themselves, similar to the original distributed computing model. This shift has implications for networking, security, and the management of the vast number of devices that will be part of the edge computing ecosystem.

Q: What are some predictions made by the speaker about the future of edge computing?

The speaker predicts that the explosion of sensor data will surpass the capabilities of the centralized cloud, leading to a move towards edge computing. There will be a shift towards a data-centric programming model and the development of new programming languages specific to data processing and analytics. The processing power at the edge will increase while the price decreases, enabling the placement of sensors in various everyday objects. The speaker foresees trillions of devices being connected, leading to new applications and industries being transformed by edge computing.

Q: What is the role of the cloud in the future of computing?

While the speaker suggests that cloud computing as we know it may come to an end, the cloud will still have a purpose. It will become a centralized repository for curated information collected at the edge. The cloud will serve as a place for learning, where information from billions of devices will be processed and used to make devices at the edge smarter. However, the most important decisions and real-time processing will no longer rely solely on the cloud, as edge devices become more sophisticated in terms of processing power and intelligence.

Q: How will the rise of edge computing impact organizations and IT management?

The rise of edge computing will bring significant challenges and opportunities for organizations and IT management. With the increasing number of devices and the need for their coordination and management, IT managers will face the task of managing trillions of connected devices. Network and security issues will become more complex. There will also be a need for talent with expertise in data analytics and the ability to program for data-centric solutions. The impact will be felt across various industries, combining consumer applications with enterprise manageability.

Q: What is the importance of real-world data in the context of edge computing?

Real-world data collected by edge devices is a significant aspect of edge computing. Unlike traditional computing that mainly involved human-generated information, edge devices are now collecting real-world data about the environment through sophisticated sensors. This data, which can include information about vision, location, acceleration, temperature, and gravity, is massive and needs to be processed in real-time at the edge. This is because the latency involved in sending the data to the centralized cloud and waiting for a response is too slow for critical decision-making.

Q: How does the speaker compare the processing power and prices of edge devices over time?

The processing power of edge devices is expected to increase over time, while the prices decrease. The speaker gives an example of the iPhone 7, which has significantly more transistors than the original Pentium processor. This increase in processing power and decrease in costs can be attributed to the economies of scale resulting from the large number of devices being produced. As edge devices become more powerful and affordable, the ability to place sensors in various objects will become more accessible, further driving the growth of edge computing.

Takeaways

The shift towards edge computing is predicted to lead to the end of cloud computing as we know it. Edge devices, such as self-driving cars, drones, and robots, will become the new data centers, processing and analyzing vast amounts of real-world data in real-time at the edge. Machine learning will play a crucial role in extracting meaningful insights from the data, and programming languages will evolve to support data-centric programming. The rise of edge computing will bring challenges in terms of networking, security, and device management, but it also presents vast opportunities for innovation and transformation in various industries. Organizations and IT managers need to be prepared for this significant shift in computing paradigm and the exponential growth of connected devices.

Summary & Key Takeaways

• Cloud computing, which is currently centralized, is being challenged by the rise of edge computing, where processing takes place at the edge devices.

• Edge devices, such as self-driving cars and drones, collect vast amounts of information that needs to be processed in real-time.

• The combination of machine learning, sensor data, and edge computing is driving a new paradigm in computing that will transform various industries.