Latency refers to the interruption in data transmission, and cloud computing is a model in which computing services are delivered over the Internet from a central data center. Edge computing, on the other hand, is a data distribution and computing system that processes data near the edge of the source without depending on data transmission to a centralized data center. Edge computing cuts latency by processing data locally on devices at the network edge. For cloud computing, this is not true. Edge computing is recognized for producing faster responses, real-time responses with zero latency. Cloud computing enables broader capabilities.
Key Points to Remember
There are some key points to remember while reading and understanding the three concepts. They are:
- Latency- Latency is the time required for data transmission from a source to its destination. It is measured in milliseconds and is a vibrant feature in applications necessitating prompt response or actions like online gaming or autonomous vehicles.
- Cloud Computing- This computing framework allows users to access services like servers, storage, and software over the network from a centralized data center, offering flexibility and scalability. However, it can experience higher latency due to the long distance traveled by the data to be transferred.
- Edge Computing- This model brings computing nearer to the data source wherein sensors or other devices permit quicker collection and processing of data, thus dipping latency. Data is processed locally in this system, thereby reducing latency, and sometimes data can also be sent to a centralized system for further analysis.
Edge computing and cloud computing are not competing technologies, but they are complementing technologies. Edge instead of replacing cloud computing, extends its functionality. Big data analytics is always done through cloud computing because it supports heavy lifting. Long-term storage and complex computations are done by cloud computing which do not require immediate results and are prepared for latency. Edge computing provides time-constrictive data processing done locally with less bandwidth usage and reduced latency. The data is big, and the response is thus quick.
Latency depends on several factors like network connections, hardware, geographical location of the equipment, and even physical distance sometimes. Latency in IoT impacts the productivity and performance of the entire system as it relies on the network and interconnectivity of components.
Latency can impact IoT in various ways like:
- Data Transmission- Network latency could result in delayed data transmission and decreasing operational efficiency. This could result in delays in real-time applications requiring immediate data communication.
- Power Consumption- IoT devices often work on limited battery power. Higher network latency requires more battery backup to transmit data, leading to increased power consumption and reduced battery life.
- Device Lifespan- The lifespan of IoT devices may suffer from high network latency due to constant strain on the device. Appliances might be heating and have a shorter lifespan under edge computing.
- User Experience- Network latency can hardly contribute to providing a perfect user experience. There will be real-time processing delays that will confuse users and question the functionality of the devices being utilized.