Optimizing the startup speed of a modular system using machine learning

A computer program product for generating an efficient startup sequence for a plurality of components of modular system, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:

• receive, from a requesting process, an indication of the plurality of components of the modular system and an indication of a mechanism for launching the modular system;
• invoke a machine-learning ordering tool to generate at least one startup sequence, the at least one startup sequence being returned with corresponding launch metrics;
• determine, based on launch metrics for the at least one startup sequence, whether an efficient startup sequence with a respective launch metric that satisfies an optimization criterion has been identified;
• responsive to the efficient sequence being identified, return the efficient startup sequence, wherein the modular system is launched using the efficient startup sequence;
• and
• responsive to failing to identify the efficient sequence, repeat the invoking and determining.

A method comprising:

• receiving a plurality of components of a modular system, an initial startup sequence, and a mechanism for launching the modular system;
• launching the modular system using the initial startup sequence to generate launch metrics;
• generating a search tree based on domain knowledge, the initial startup sequence, and the launch metrics, each leaf of the search tree representing an individual startup sequence;
• performing a heuristic search of the search tree to obtain a potential startup sequence;
• determining whether the launch metric for the initial sequence is within a threshold of the launch metric for the potential startup sequence;
• responsive to the launch metric being within the threshold, returning the potential startup sequence;
• and
• responsive to the launch metric being outside the threshold, repeating the generating, performing, and determining using the potential startup sequence as the initial startup sequence.