GAMS Engine comes with a highly flexible model- and user management system that allows you to restrict the activities of your users according to your organizational hierarchy. To understand this in its entirety, we need to address a few concepts in more detail: models, namespaces, permissions and user types.

When submitting a GAMS job, it usually consists of model files and data files. While the latter usually differ with each job, the actual GAMS model remains mostly unchanged. In order to avoid submitting the same files to Engine with each job, models can be registered with Engine. Files of registered models are stored by Engine and used for subsequent jobs. You only need to provide the name under which the model is registered and send the dynamic data.

Models are organized in namespaces, which you can think of as file directories. Models are always registered in a namespace and submitted GAMS jobs are also executed there. Each model name can only occur once within a namespace. If two models with the same name are to be registered, this must be done in different namespaces. Apart from the ability to manage multiple models with the same name, namespaces are particularly useful for representing hierarchical (user) structures. This is due to an important property of namespaces, namely the concept of permissions.

Namespaces are similar to directories in the UNIX file system: you can specify which users have access to which namespaces. Just like the UNIX file system, GAMS Engine has three types of permissions on namespaces:

• Read Permission
  Users can download GAMS models registered in this namespace
• Write Permission
  Users can register new models in this namespace
• Execute Permission
  Users can execute models in this namespace

The table below shows the mapping of these permissions to the permission codes used by GAMS Engine.

In the following example, two namespaces are used by three people with different permissions to represent the structure and workflow of a company with a customer.

• Developer:
  The Model Developer is responsible for maintaining existing models and developing future models. He only accesses the namespace R&D provided for this purpose, in which he has all permissions.
• Manager:
  The manager has read and execute permissions on the namespace R&D. There she checks the models for customer suitability. If a model (update) is ready, then the manager registers it in the namespace production.
• Customer:
  The customer has read and execute permissions on the namespace production. With these permissions she can submit jobs with her data and receive the results.

After first installing the system, there is a single default user available with the name: admin and password: admin.

If you are administrator, you can invite other users. If you are using the Engine UI, you will find an invite user button in the upper right corner of the Users section.

GAMS Engine distinguishes three types of users:

• Users
• Inviters
  Inviters are users with the additional privilege to invite new users to use GAMS Engine. However, inviters can only invite users who have the same or fewer permissions than themselves. For example, if an inviter has read and execute permissions on the namespace global, she is not allowed to invite a user with write permission on this namespace. Inviters can invite other inviters, but not admins.
• Administrators
  Administrators are the most privileged users. They have full permissions for each namespace and are allowed to invite new users, even new administrators. Administrators can also add new namespaces or remove existing ones.

In order to add a new user to GAMS Engine, you need to generate an invitation code. Users can then register themselves by providing this invitation code, a username and a password. Invitation codes can be generated by administrators and inviters. When creating an invitation code, permissions to namespaces can be assigned so that the new user can start interacting with the system directly. Note that the invitee's permissions may be lower than the inviter's, but not higher. Furthermore, inviters are able to manage - i.e. modify permissions and delete - their children (invitees) as well as any grandchildren. In this way, you can set up several hierarchy levels.

User hierarchy example:

Users can be assigned to user groups. A user group is a collection of users with any role (user, iviter, or administrator). A user group is always assigned to a namespace and has a unique label/name within that namespace. To create or remove a user group, you must be an inviter with write permission in that namespace (or an administrator).

Any member of a group can see all other members within that group. In addition, inviters can see the members of all their invitees' groups. Only inviters and admins can add and remove members from a group. While admins can remove any member from any group, inviters can only remove their invitees. Also, inviters can only remove members from groups that they are either a member of themselves or that belong to an invitee.

Future plans are to extend the functionality of groups to allow a more fine-grained access control system not only by namespace, but by model.

User groups can be used to restrict the visibility of models in a namespace. When registering a new model (or updating an existing model), you can specify which user groups can see that model. All users who can see at least one of the user groups to which the model is assigned can see the model. If a model is not assigned to a user group, the model is visible to anyone who has any permission (read, write, and/or execute) in that namespace.

To interact with GAMS Engine, you must authenticate yourself. Engine currently supports two ways to authenticate: Basic authentication as well as authentication using JSON Web Tokens (JWT).

Whereas with basic authentication you send your username and password with each request, with JWT a token is used for authentication. JWT are only valid for a certain amount of time that you can specify when generating a new token (via the POST /auth/login endpoint).

Whenever your username or password is changed (either by yourself or by an administrator), all your JWT are automatically invalidated and you have to generate new ones. You can also manually invalidate all your JWT via the POST /auth/logout endpoint. It is not possible to invalidate individual tokens.

One advantage of JWT authentication over basic authentication is that instead of storing a user's password (which is often reused for multiple services), a random-looking token is stored if you want to implement a "stay logged in" feature on your client (in fact, this is what Engine UI, GAMS Studio and GAMS MIRO do). In addition, you can restrict access of a certain token by making use of "access scopes":

In GAMS Engine, you can specify what you can do with a particular token: You can make a token accessible only to GET endpoints with the READONLY access scope, or you can specify which API endpoints (for example, JOBS or NAMESPACES) can be accessed with that particular token. This allows you to ensure, for example, that a particular CI job can only add/update/delete models but cannot submit new jobs, etc.

GAMS Engine allows you to limit the solve time as well as the disk usage of individual users or groups of users.

Let's take the user hierarchy from the previous section on user management to give you an example of how quotas work:

Only admins and inviters can update quotas of users. While admins can update the quotas of all users, inviters can only update the quotas of direct/indirect invitees (we also call this the "subtree" of an inviter). An inviter cannot assign a quota larger than his own

There are three types of quotas that can be assigned: volume_quota, disk_quota and parallel_quota (only used in the Engine Kubernetes version).

If "Admin" sets the volume_quota of "Student 1" to 60 seconds, the student can only run jobs for one minute. If the quota is exceeded, new jobs submitted by "Student 1" are rejected by Engine. In addition, currently active jobs are canceled (using SIGKILL) when the volume quota of the user who submitted the job is exhausted.

If "Admin" sets the volume_quota of "Teacher" to 1800 seconds, it means that the "Teacher", "Student 1", "Student 2", "Assistant" and anyone invited by the "Assistant" can run GAMS for a total of 1800 seconds. This allows administrators to invite new "entities" by creating a new inviter that has quotas assigned to it. Both the inviter and anyone invited by that inviter are now limited by those quotas.

Quotas allow further restrictions. Imagine the case where "Admin" has assigned 1800 seconds of volume_quota to "Teacher". As an inviter, "Teacher" can give students any volume_quota that is less than or equal to 1800 seconds. "Teacher" could give "Student 1" and "Student 2" each 600 seconds and "Assistant" 800 seconds. In this case, "Student 1" can use up to 600 seconds, "Student 2" can use up to 600 seconds, and "Assistant" can use up to 800 seconds.

Quotas allow for overbooking. An attentive reader may have noticed that in the last example, the teacher had 1800 seconds, but she provided a total of 2000 seconds for her invitees. This does not mean that the teacher's subtree had a total of more than 1800 seconds. When "Student 1" and "Student 2" have used up their entire allotment, the "Assistant" still has 600 seconds left. Therefore, he can no longer use up the full 800 seconds allocated to him by the teacher.

disk_quota is used to limit the disk usage of a user as well as the user's invitees (if any). The following files contribute to disk usage:

• Temporary/unregistered models
• Additional data provided with the (Hypercube) job
• Inex files provided with the (Hypercube) job
• (Hypercube) job results

If the disk_quota is exceeded, GAMS Engine rejects all new jobs submitted by users who have exceeded their quotas. When a worker receives a job from a user whose disk_quota is exceeded, it is cancelled.

The disk space used during solving does not contribute to the disk_quota. A job could use more disk space during solving, but only upload a small portion (perhaps a result GDX file) and would not violate the quota.

Unlike volume_quota, disk_quota has a grace space. When a job is solved, it might be undesirable to refuse to upload the result because the disk_quota is exceeded. In this scenario, the result will be uploaded if it does not exceed disk_quota + 1GB. If the grace space is also exceeded, the job is marked as "Corrupted" and the results are not available.

disk_quota allows you to limit unregistered files. To limit registered files, such as registered models, inex files, etc. admins can assign a disk_quota to namespaces (available via the /namespaces/{namespace}/disk-quota API).

Similar to the volume_quota, the disk_quota allows further restrictions for invited users as well as overbooking.

Over time, the amount of disk space required on the server can grow accordingly. It is the responsibility of the admin to ensure that the result data is deleted when required. The Engine API provides a cleanup endpoint for this purpose.

When working with the Engine UI, you can reduce the amount of disk space used by GAMS Engine using the Cleanup view. You can either remove result files of individual jobs one by one or clean up multiple files at once using the Run housekeeping dialog. This allows you to remove all files created more than x days ago and/or files created by users who have been removed from the system.

For production servers, we recommend tracking logs of GAMS Engine to ensure system stability. You can use solutions such as EFK or ELK stack. Regardless of which solution you use, you need to familiarize yourself with GAMS Engine's core parts and their logging formats.

In GAMS Engine, we chose RabbitMQ as the message broker solution. When the cluster is first spun up, RabbitMQ creates the necessary queues, exchanges, and users. This part of the process creates non-standard protocols. However, it is usually okay to skip this part as it is only done once. After that, the logging format is standard. An example of a RabbitMQ log entry: 2021-10-20 11:46:16.006 [info] <0.941.0> accepting AMQP connection <0.941.0> (172.19.0.5:46598 -> 172.19.0.2:5672)

We chose PostgreSQL to store most of the structured data. Similar to RabbitMQ, PostgreSQL also creates the required tables and users on the first run, so it creates non-default logs. Skipping these logs should not be a problem either, as they are only issued once. An example of a PostgreSQL log entry: 2021-10-20 11:46:02.076 UTC [1] LOG: database system is ready to accept connection

MongoDB stores some of the structured data and all of the unstructured data. Its bootstrap process is also similar to RabbitMQ and PostgreSQL. A sample log of MongoDB looks like this: 2021-10-20T11:46:07.054+0000 I NETWORK [initandlisten] waiting for connections on port 27017

The logging format of the Worker,Cleaner,Hypercube Unpacker,Hypercube Appender, Dependency Checker,Job Watcher,Job Spawner, Job Cleaner,Job Canceler containers is standardized. An example log a worker is [20/10/2021 11:46:16] INFO #: Waiting for job.. The hash sign (#) after the severity (INFO) indicates which job is associated with this log. In this case, no job is associated with this log. Another log of a worker: [20/10/2021 12:11:54] INFO 1: Job Received indicates that the worker got the job with id 1. For Hypercubes, the log format is different. For example [20/10/2021 12:13:18] DEBUG 4@617007d8917cb774979dab76: Working directory cleaned this log refers to the the 4th job of the Hypercube job 617007d8917cb774979dab76. This log format facilitates the retrieval of logs that refer to a single job from multiple containers.

REST API backend log format is different from the other log formats. We should address that the backend is reverse proxied by nginx. nginx log format is different from the backend log format but it is widely-known. An example log is 172.22.0.1 - - [20/Oct/2021:12:12:58 +0000] "GET /api/jobs/status-codes HTTP/1.1" 200 385 "http://localhost:30001/" "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:93.0) Gecko/20100101 Firefox/93.0" "-" Backend is a WSGI app and here is an example log [pid: 13] 172.22.0.1 (admin 1) [20/Oct/2021:12:13:01 +0000] apis.ns_users INFO User 'admin' listed all the users. We also log the user ID after the user name because it is possible to change the user name. However, the logs that come from uWSGI do not log the user ID. [pid: 13] 172.22.0.1 (-) [20/Oct/2021:12:11:49 +0000] uwsgi INFO {36 vars in 401 bytes} [Wed Oct 20 12:11:49 2021] GET /api/version => generated 72 bytes in 6 msecs (HTTP/1.1 200) 3 headers in 108 bytes (1 switches on core 0)