By 2025, 41.6 billion IoT devices are expected to generate 79.4 zettabytes of data and the collective sum of the world’s data will be 175 ZB, according to IDC. Essentially, close to 50% of world data will be generated by IoT. By 2025, 70% of data generated by IoT applications will be processed outside the conventional data center, according to Gartner.
Edge computing in IoT
Considering the amount of data generated by IoT, it is a no brainer that the data needs to be processed closer to the data generation point. This new model of computing is known as edge computing, which provides significant advantages compared to the conventional cloud computing model.
Edge computing is well-positioned to take the challenges of IoT head-on. Latency issues found in cloud-computing is mitigated by edge computing local data processing. Dependency on edge computing becomes pronounced when there is an unreliable communication channel to the cloud for data processing. Edge computing brings long-term efficiency to data processing in IoT applications, which is inevitable.
Elements of edge computing
A few elements of edge computing include:
- Computing devices. Machine learning algorithms running on computing devices process data generated by IoT devices. Computing device can be a small form factor server or an embedded system-on-chip board.
- Data storage devices. Data can be stored locally for analysis at a later time, or to understand real-time data behavior. Data can also be designated to a central data center.
- Communication infrastructure. IoT devices exchange data with computing and storage devices over a reliable communication infrastructure.
Edge computing also requires other technology, such as regulated power supplies, optional battery backup and optional cooling systems.
Some edge computing sites are remotely located, and it’s possible that each site might not have qualified IT staff. If this is the case, it becomes essential to have the ease and reliability of connecting to devices on these sites. Connectivity to these devices will provide the IT staff the ability to manage and control devices remotely.
Devices might malfunction, and as a result edge computing applications running on those devices will likely malfunction as well. IT staff might want to look at logs, statistics, alerts and resource consumption patterns on the devices. On many occasions, IT staff might want to upgrade the device system software and applications, apply security patches to the devices or update the learning model of machine learning applications. IT staff might also need to change the configurations, restart devices, restart applications, or delete and modify logs and statistics to bring failed systems back to a normal operating state.
There are four important considerations IT pros should take into account while creating a reliable remote connectivity solution for an edge computing.
Security is one of the most critical aspects in any design. Security must never be an afterthought; it must be part of the solution. The remote connection channel must be secured using strong encryption and authentication algorithms. A public key infrastructure technology is one of the most adapted solutions. SSH/SSL based tunnels are also popular solutions for remote connectivity.
Edge computing devices by themselves must not open up additional endpoints — or ports — that are exposed to the public internet. This can cause serious security vulnerabilities and can also increase the attack surface significantly.
Organizations must follow strong security policies for usernames and passwords on each edge computing device. Note whether the devices are remotely located outside the enterprise IT network or data centers. While the edge computing devices can be physically secured, the interfaces on them — wired or wireless — may be exposed to attackers. Systems are only as secure as the weakest secured device in the edge network.
Identifying the devices in edge computing sites can be tricky, especially if the devices are connected over a Global System for Mobile Communications network, or are behind a network address translator or firewall. The edge computing devices will not have a globally addressable IP address, and the remote connectivity solution must address this. The solution must also provide an easy way of mapping a device’s ID to the endpoint for connectivity.
Connect at scale
Most of the edge computing solutions involve large scale deployment of devices across multiple edge computing site locations. These sites might be geographically dispersed, and the remote connectivity solution must consider this requirement. IT staff must be able to connect to a large set of devices and perform operations. This is because remote connectivity solutions with persistent connectivity within each of the devices may not scale. A persistent connection needs connection states to be maintained and refreshed at regular intervals. These models do not work efficiently at scale. A solution that involves on-demand connectivity has a better prospect of scaling.
Managing remote edge computing sites with no IT staff locally available can be a significant overhead. Automation is the key enabler for efficient operation. It is desirable to create rules, clear logs and bring the system back to a normal operating state. Remote connect solutions must support programmable interfaces such as APIs, which IT staff can use to create If This Then That rules. Programmable APIs can also be used to pull statuses and statistics at regular intervals and feed data to the operational management systems.
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