This project is a product of the first practical assignement of one of our classes: Distributed Computing and the main idea behind it is to implement a drive-through restaurant using a peer-to-peer distributed system architecture.
This restaurant simulation is composed by five different entities:
- Restaurant: entity responsible for managing all kitchen equipment (fryer, barbecue griller and bar).
- Clerk: receives the clients and records their requests.
- Chef: receives the requests and cooks them.
- Waiter: delivers the order to the respective client and receives payment.
- Client: makes a food request.
This entities are organized in a token-ring.
The kitchen is composed by 3 equipments:
- Barbecue griller: where the Chef can prepare one hamburger (average preparing time: 3 seconds)
- Bar: where the Chef can prepare one drink (average preparing time: 1 second)
- Fryer: where the Chef can prepare one package of fries (average preparing time: 5 seconds)
Each action takes a random time to be completed. This random time follows a gaussian distribution with an average time of 2 seconds and a standar deviation of 0.5 seconds. One client request is composed by at least one item (hamburger, drink or package of fries), but can be one possible combination of items with a maximum of 5 items. This request is generated randomly.
- Clone this repository
Open two terminals:
simulation:
$ python simulation.pyclient:
$ python client.pyWe created a run script in bash that executes 20 clients, so that is another way to execute and test the program:
$ ./run.shKeep your fork sync with the upstream
$ git remote add upstream git@github.com:mariolpantunes/load-balancer.git
$ git fetch upstream
$ git checkout master
$ git merge upstream/master- Vasco Ramos: vascoalramos
- Diogo Silva: HerouFenix
This project is licensed under the MIT License - see the LICENSE file for details