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What “the cloud is dead” means
The phrase comes from Ruediger Stroh’s 2017 article, “The Cloud is Dead: How Businesses Will Benefit from the Shift from Center to Edge”. Stroh, then executive vice president and general manager of Security & Connectivity at NXP Semiconductors, was arguing for a redistribution of computing—not the disappearance of cloud services.
In a centralized model, connected devices send data to remote infrastructure, which processes it and returns a result. In an edge model, some of that processing happens closer to where data is created: on a device, in a vehicle, or in a nearby gateway. A hybrid design uses both.
Why move some computing to the edge?
Connected sensors, vehicles, robots and other devices can generate large amounts of data. Sending every reading to a distant data center and waiting for a response can add delay and use network capacity. That is a poor fit when a system needs to act immediately. The 2017 article used autonomous vehicles as an example, noting that real-time decisions cannot reliably depend on repeated centralized round trips.
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- Response time: Local processing can reduce the time between detecting an event and responding to it.
- Bandwidth: Filtering or analyzing data nearby can reduce how much raw information travels over the network.
- Privacy: Keeping some data local can limit what is uploaded, though it does not by itself guarantee privacy.
- Connectivity resilience: A device that can make local decisions may continue some operations during a network interruption. The degree of resilience depends on the system design.
Cloud, edge and hybrid computing compared
| Consideration | Centralized cloud | Edge or hybrid design |
|---|---|---|
| Response latency | Processing requires a network round trip, which can be unsuitable for urgent decisions. | Local processing can shorten the response path for time-sensitive tasks. |
| Bandwidth and congestion | Continuous uploads of device data can consume network capacity. | Local filtering can reduce raw-data transfers; selected results can still go to the cloud. |
| Privacy and data handling | More data may need to be sent to centralized services. | Some analysis can happen locally, reducing the amount of raw data transmitted. |
| Operation during connectivity loss | Cloud-dependent functions may be unavailable when the connection fails. | Local functions may continue if designed to operate offline; this is not automatic. |
| Security and management | Centralized services still require secure access and administration. | Many distributed devices create additional points to secure and manage, including physically exposed equipment. |
| Typical division of work | Storage, large-scale analysis, training and less time-critical processing. | Immediate control or inference near the device, with cloud support for aggregation, storage and training. |
What remains valuable about the cloud?
Edge systems do not eliminate the need for centralized computing. The 2017 article describes the cloud as a place to store information for future reference and to develop and train IoT pattern-recognition and machine-learning systems. It can also handle workloads that do not need an immediate response.
Stroh put the idea this way: “The cloud will become the teaching and training center of the IoT.” In practice, a device might use a locally available model to make a quick decision, while cloud infrastructure stores data, helps refine models and coordinates activity across many devices.
What the shift could mean for businesses
Businesses may find edge processing useful wherever a fast local response or reduced data transfer matters. The 2017 article points to possibilities such as autonomous-vehicle services, retail analytics, industrial robotics, smart homes and secure IoT infrastructure. These are examples of potential applications, not proof of a particular market size or guaranteed commercial return.
The business case depends on the workload. A company should weigh the cost and complexity of deploying and managing hardware in many locations against the value of faster decisions, lower data transmission and continued operation during interruptions. Edge computing is not automatically cheaper: it can shift spending and operational responsibility from centralized infrastructure to devices, gateways and their management.
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Moving computation closer to devices also distributes the security problem. Edge equipment may be physically accessible, deployed across many sites, and connected to systems that affect real-world operations. A compromised or poorly maintained device can therefore create risks beyond data loss.
Stroh’s article calls for dedicated edge processing capacity and security-by-design across hardware and software. For a business, that means treating device security, updates, access controls and fleet management as core requirements—not as add-ons after deployment. Systems that control safety-sensitive equipment need safeguards appropriate to the consequences of failure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide what belongs at the edge
- Identify decisions that cannot wait. Put functions that need a rapid response near the equipment or people they affect.
- Separate immediate action from long-term analysis. Keep local control or inference at the edge; send useful summaries or selected data to the cloud for storage, training and broader analysis.
- Test connectivity failures. Decide which functions must continue offline, and verify that they do so safely.
- Plan device security and operations. Account for physical exposure, software maintenance, access management and monitoring across the whole device fleet.
- Prototype against the real workload. A Raspberry Pi 5 can serve as an editorial example of hardware for physical edge prototyping; it is not a product recommendation from Stroh’s article. A prototype can help assess whether local processing meets the application’s latency and reliability needs before a larger deployment.
What the 2017 forecast does—and does not—show
The article attributed a forecast to IDC that 43 percent of IoT computing would occur at the edge by 2021. That date has passed, and the forecast is not a current measured share. The figure should be read as a historical projection made in a 2017 article, not evidence of today’s adoption level.
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