The supermarket model typically refers to a system with a large number of queues, where arriving customers choose d queues at random and join the queue with  fewest customers. The supermarket model demonstrates the power of even small amounts of choice, as compared to simply joining a queue chosen uniformly at random, for load balancing systems. In this work we perform simulation-based studies to consider variations where service times for a customer are predicted, as might be done in modern settings using machine learning techniques or related mechanisms. To begin, we start by considering the baseline where service times are known. We find that this allows for significant improvements. In particular, not only can the queue being joined be chosen based on the total work at the queue instead of the number of jobs, but also the jobs in the queue can be served using strategies that take advantage of the service times such as shortest job first or shortest remaining processing time. Such strategies greatly improve performance under high load. We then examine the impact of using predictions in place of true service times. Our main takeaway is that using even seemingly weak predictions of service times can yield significant benefits over blind First In First Out queueing in this context. However, some care must be taken when using predicted service time information to both choose a queue and order elements for service within a queue; while in many cases using the information for both choosing and ordering is beneficial, in many of our simulation settings we find that simply using the number of jobs to choose a queue is better when using predicted service times to order jobs in a queue. Our study leaves many natural  open questions for further work.
The supermarket model refers to a system with a large number of queues, where arriving customers choose d queues at random and join the queue with the fewest customers. The supermarket model demonstrates the power of even small amounts of choice, as compared to simply joining a queue chosen uniformly at random, for load balancing systems. In this work we perform simulation-based studies to consider variations where service times for a customer are predicted, as might be done in modern settings using machine learning techniques or related mechanisms. Our primary takeaway is that using even seemingly weak predictions of service times can yield significant benefits over blind First In First Out queueing in this context. However, some care must be taken when using predicted service time information to both choose a queue and order elements for service within a queue; while in many cases using the information for both choosing and ordering is beneficial, in many of our simulation settings we find that simply using the number of jobs to choose a queue is better when using predicted service times to order jobs in a queue. Although this study is simulation based, our study leaves many natural theoretical open questions for future work.
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the
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To begin, we start by considering the baseline where service times are known. We find that this allows for significant improvements. In particular, not only can the queue being joined be chosen based on the total work at the queue instead of the number of jobs, but also the jobs in the queue can be served using strategies that take advantage of the service times such as shortest job first or shortest remaining processing time. Such strategies greatly improve performance under high load. We then examine the impact of using predictions in place of true service times. Our main
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Although this study is simulation based, our study
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future