What are the best ways to measure the effectiveness of edge computing technology in information systems?

2 Choose your metrics

When assessing the effectiveness of edge computing, it's important to consider metrics such as latency, bandwidth, security, reliability, and scalability. Edge computing can reduce latency by processing and storing data near the source, allowing for improved responsiveness and quality of service. Additionally, edge computing can reduce bandwidth consumption by filtering and compressing data before sending it to the central servers; this saves network resources and costs. Security is also improved with edge computing as it encrypts and anonymizes data at the edge, reducing the exposure and risk of breaches. Reliability is increased through distributing the workload and data across multiple edge nodes, reducing dependency and vulnerability of the central servers. Finally, scalability is improved with edge computing by adding or removing edge nodes as needed to adapt to changes in the environment or user behavior.

3 Collect your data

To measure the metrics you have chosen, you need to collect data from your information systems. You can use monitoring to track and record the performance and status of your information systems, testing to measure and compare the performance under different scenarios, and surveying to gather feedback and opinions from users or stakeholders. For example, monitoring can be used to monitor CPU usage, memory consumption, network traffic, and errors; testing can be used to simulate or experiment with performance under normal, peak, or failure conditions; and surveying can be used to collect satisfaction ratings, preferences, and expectations.

4 Analyze your data

Once you have collected your data, you need to analyze it to draw conclusions and insights about the effectiveness of edge computing in your information systems. Various techniques and tools can be used, such as statistical methods to summarize, visualize, and interpret your data, machine learning algorithms to learn from your data and make predictions or recommendations, and business intelligence software or platforms to create dashboards and reports that present your data in an understandable and actionable way. Examples of these include mean, median, standard deviation, correlation, regression, clustering, classification, charts, graphs, tables, etc.

5 Improve your systems

The final step is to use the results and insights from your data analysis to improve your information systems and the edge computing technology. You can do this by optimizing parameters and settings, innovating new features and functions, and evaluating performance periodically or continuously. Optimizing can involve adjusting the number and location of edge nodes, data processing and storage methods, or network configuration. Innovating may include developing new applications, services, or protocols that leverage the advantages of edge computing. Evaluating should be done to monitor performance and impact, as well as identify new opportunities and challenges.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?