With the growing number of connected devices, alongside the large amount of data that they collect, consequences are bound to result within distributed systems. It is because of this that we must be very mindful of how we design such systems, as we discussed in the earlier subsection. The consequences can be summarized into three main laws: the laws of physics, economics, and the land.
Law of physics
The law of physics is about understanding the physical limitations of data transfers that are made to the cloud. This has become more prevalent as a challenge given that more use cases have required increasingly more real-time responses to certain events, such as within healthcare or autonomous devices. In scenarios within those settings, every millisecond delay can cost lives.
Law of economics
Data that grows exponentially often causes bottlenecks within performance and overarching costs. This is what leads to edge computing in the first place, as it often is not economical for companies to transmit all IoT data to the cloud, given the costs that are associated with them, especially when it comes to transferring petabytes of data, as some large enterprises do with their line of work.
Law of the land
There are geographical and legal restrictions that often constrain the extent of how data can be gathered and transferred. Often, there are sovereignty laws that mandate data must only be kept and transmitted within the country’s borders. For example, GDPR in the European Union imposes strict rules on how data is handled, including the transfer of personal data outside the EU. Similarly, China’s Cybersecurity Law mandates that critical data must be stored within mainland China. Additionally, some parts of the world do not have the proper infrastructure required to support regular IoT network operations due to poor connectivity to the internet, which limits the capabilities and reliability of the cloud for that region.
Understanding the requirements, we can now look at how we can optimize our workloads.
Optimizing edge computing on networks
Optimizing edge computing on networks is a crucial step in ensuring that edge devices are utilized effectively and efficiently. In this section, we will explore various strategies for optimizing edge computing workloads, including workload distribution, resource allocation, data processing techniques, and security considerations. By understanding and applying these strategies, developers and system architects can harness the full potential of edge computing, delivering efficient, responsive, and secure IoT solutions. Additionally, we will briefly touch on optimizing the network infrastructure and communication protocols to be able to achieve this while also learning how to evaluate edge networks and will finally use all these tools to look at a smart city case study.
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