We'll simulate traffic ramp-up gradually reaching about 3x of the server capacity and observe how response time, error rate, and throughput are affected for both FIFO and LIFO queueing.īy running this simulation, we'll be able to compare the two queueing strategies and identify their strengths and weaknesses under heavy load.The server can handle a certain amount of requests before queueing them.The client generates queries of random complexity using the lognormal distribution.
The essential characteristics of the model are: To compare the performance of FIFO (which processes requests in the order they are received) and LIFO (which prioritizes the most recent requests) queueing strategies, we'll build a simple model using a Client that generates requests and a Server that handles them. After all, as the saying goes: " I hear and I forget, I see and I remember, I do and I understand." Model Comparing the pros and cons of each approach helps to build an understanding of the trade-offs and make better calls about queueing strategies, improving your engineering skills in the process. But what happens when traffic bursts overwhelm your system? Queueing requests is a common solution, but what's the best approach: FIFO or LIFO? In this post, we'll explore both strategies through a simple simulation in Colab, allowing you to see the impact of changing parameters on system performance. As an engineer, you probably know that server performance under heavy load is crucial for maintaining the availability and responsiveness of your services.
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