I need to optimise the end-to-end latency for a multi-service application while distributing it on multiple devices. The application is a series of services interconnected to each other. The goal is to use some online optimisation algorithm to find the optimal cut point in a series of services connected of multi-service application, after the cut point the half part of the collection of services will run on one device and the intermediate result will be sent to another device considering bandwidth and resource capacity of the device, taking into account the modification of the initial assignment to deal with events such as load changes. The input to the algorithm is the latency to execute the services on two different devices, the bandwidth to send the intermediate the result, and application latency to optimise against. The output is the optimal point to divide the application into two different parts.
From reading different research papers and the majority of the papers have used deep Reinforcement learning (DRL), I could understand but I am unable to take a start point. What is the best way to solve the above problem other than using a linear search algorithm that would return the lowest end-to-end latency from the search space? Using linear search only returns the lowest end-to-end latency from the search space while not considering resource constraint or bandwidth constraint. Any help is highly appreciated.