Ganeti 2.2 design¶
This document describes the major changes in Ganeti 2.2 compared to the 2.1 version.
The 2.2 version will be a relatively small release. Its main aim is to avoid changing too much of the core code, while addressing issues and adding new features and improvements over 2.1, in a timely fashion.
As for 2.1 we divide the 2.2 design into three areas:
core changes, which affect the master daemon/job queue/locking or all/most logical units
logical unit/feature changes
external interface changes (e.g. command line, OS API, hooks, …)
Core changes¶
Master Daemon Scaling improvements¶
Current state and shortcomings¶
Currently the Ganeti master daemon is based on four sets of threads:
The main thread (1 thread) just accepts connections on the master socket
The client worker pool (16 threads) handles those connections, one thread per connected socket, parses luxi requests, and sends data back to the clients
The job queue worker pool (25 threads) executes the actual jobs submitted by the clients
The rpc worker pool (10 threads) interacts with the nodes via http-based-rpc
This means that every masterd currently runs 52 threads to do its job. Being able to reduce the number of thread sets would make the master’s architecture a lot simpler. Moreover having less threads can help decrease lock contention, log pollution and memory usage. Also, with the current architecture, masterd suffers from quite a few scalability issues:
Core daemon connection handling¶
Since the 16 client worker threads handle one connection each, it’s very easy to exhaust them, by just connecting to masterd 16 times and not sending any data. While we could perhaps make those pools resizable, increasing the number of threads won’t help with lock contention nor with better handling long running operations making sure the client is informed that everything is proceeding, and doesn’t need to time out.
Wait for job change¶
The REQ_WAIT_FOR_JOB_CHANGE luxi operation makes the relevant client thread block on its job for a relatively long time. This is another easy way to exhaust the 16 client threads, and a place where clients often time out, moreover this operation is negative for the job queue lock contention (see below).
Job Queue lock¶
The job queue lock is quite heavily contended, and certain easily reproducible workloads show that’s it’s very easy to put masterd in trouble: for example running ~15 background instance reinstall jobs, results in a master daemon that, even without having finished the client worker threads, can’t answer simple job list requests, or submit more jobs.
Currently the job queue lock is an exclusive non-fair lock insulating the following job queue methods (called by the client workers).
AddNode
RemoveNode
SubmitJob
SubmitManyJobs
WaitForJobChanges
CancelJob
ArchiveJob
AutoArchiveJobs
QueryJobs
Shutdown
Moreover the job queue lock is acquired outside of the job queue in two other classes:
jqueue._JobQueueWorker (in RunTask) before executing the opcode, after finishing its executing and when handling an exception.
jqueue._OpExecCallbacks (in NotifyStart and Feedback) when the processor (mcpu.Processor) is about to start working on the opcode (after acquiring the necessary locks) and when any data is sent back via the feedback function.
Of those the major critical points are:
Submit[Many]Job, QueryJobs, WaitForJobChanges, which can easily slow down and block client threads up to making the respective clients time out.
The code paths in NotifyStart, Feedback, and RunTask, which slow down job processing between clients and otherwise non-related jobs.
To increase the pain:
WaitForJobChanges is a bad offender because it’s implemented with a notified condition which awakes waiting threads, who then try to acquire the global lock again
Many should-be-fast code paths are slowed down by replicating the change to remote nodes, and thus waiting, with the lock held, on remote rpcs to complete (starting, finishing, and submitting jobs)
Proposed changes¶
In order to be able to interact with the master daemon even when it’s under heavy load, and to make it simpler to add core functionality (such as an asynchronous rpc client) we propose three subsequent levels of changes to the master core architecture.
After making this change we’ll be able to re-evaluate the size of our thread pool, if we see that we can make most threads in the client worker pool always idle. In the future we should also investigate making the rpc client asynchronous as well, so that we can make masterd a lot smaller in number of threads, and memory size, and thus also easier to understand, debug, and scale.
Connection handling¶
We’ll move the main thread of ganeti-masterd to asyncore, so that it can share the mainloop code with all other Ganeti daemons. Then all luxi clients will be asyncore clients, and I/O to/from them will be handled by the master thread asynchronously. Data will be read from the client sockets as it becomes available, and kept in a buffer, then when a complete message is found, it’s passed to a client worker thread for parsing and processing. The client worker thread is responsible for serializing the reply, which can then be sent asynchronously by the main thread on the socket.
Wait for job change¶
The REQ_WAIT_FOR_JOB_CHANGE luxi request is changed to be subscription-based, so that the executing thread doesn’t have to be waiting for the changes to arrive. Threads producing messages (job queue executors) will make sure that when there is a change another thread is awakened and delivers it to the waiting clients. This can be either a dedicated “wait for job changes” thread or pool, or one of the client workers, depending on what’s easier to implement. In either case the main asyncore thread will only be involved in pushing of the actual data, and not in fetching/serializing it.
Other features to look at, when implementing this code are:
Possibility not to need the job lock to know which updates to push: if the thread producing the data pushed a copy of the update for the waiting clients, the thread sending it won’t need to acquire the lock again to fetch the actual data.
Possibility to signal clients about to time out, when no update has been received, not to despair and to keep waiting (luxi level keepalive).
Possibility to defer updates if they are too frequent, providing them at a maximum rate (lower priority).
Job Queue lock¶
In order to decrease the job queue lock contention, we will change the code paths in the following ways, initially:
A per-job lock will be introduced. All operations affecting only one job (for example feedback, starting/finishing notifications, subscribing to or watching a job) will only require the job lock. This should be a leaf lock, but if a situation arises in which it must be acquired together with the global job queue lock the global one must always be acquired last (for the global section).
The locks will be converted to a sharedlock. Any read-only operation will be able to proceed in parallel.
During remote update (which happens already per-job) we’ll drop the job lock level to shared mode, so that activities reading the lock (for example job change notifications or QueryJobs calls) will be able to proceed in parallel.
The wait for job changes improvements proposed above will be implemented.
In the future other improvements may include splitting off some of the work (eg replication of a job to remote nodes) to a separate thread pool or asynchronous thread, not tied with the code path for answering client requests or the one executing the “real” work. This can be discussed again after we used the more granular job queue in production and tested its benefits.
Inter-cluster instance moves¶
Current state and shortcomings¶
With the current design of Ganeti, moving whole instances between different clusters involves a lot of manual work. There are several ways to move instances, one of them being to export the instance, manually copying all data to the new cluster before importing it again. Manual changes to the instances configuration, such as the IP address, may be necessary in the new environment. The goal is to improve and automate this process in Ganeti 2.2.
Proposed changes¶
Copying data¶
To simplify the implementation, we decided to operate at a block-device level only, allowing us to easily support non-DRBD instance moves.
Intra-cluster instance moves will re-use the existing export and import scripts supplied by instance OS definitions. Unlike simply copying the raw data, this allows one to use filesystem-specific utilities to dump only used parts of the disk and to exclude certain disks from the move. Compression should be used to further reduce the amount of data transferred.
The export scripts writes all data to stdout and the import script reads it from stdin again. To avoid copying data and reduce disk space consumption, everything is read from the disk and sent over the network directly, where it’ll be written to the new block device directly again.
Workflow¶
Third party tells source cluster to shut down instance, asks for the instance specification and for the public part of an encryption key
Instance information can already be retrieved using an existing API (
OpInstanceQueryData).An RSA encryption key and a corresponding self-signed X509 certificate is generated using the “openssl” command. This key will be used to encrypt the data sent to the destination cluster.
Private keys never leave the cluster.
The public part (the X509 certificate) is signed using HMAC with salting and a secret shared between Ganeti clusters.
Third party tells destination cluster to create an instance with the same specifications as on source cluster and to prepare for an instance move with the key received from the source cluster and receives the public part of the destination’s encryption key
The current API to create instances (
OpInstanceCreate) will be extended to support an import from a remote cluster.A valid, unexpired X509 certificate signed with the destination cluster’s secret will be required. By verifying the signature, we know the third party didn’t modify the certificate.
The private keys never leave their cluster, hence the third party can not decrypt or intercept the instance’s data by modifying the IP address or port sent by the destination cluster.
The destination cluster generates another key and certificate, signs and sends it to the third party, who will have to pass it to the API for exporting an instance (
OpBackupExport). This certificate is used to ensure we’re sending the disk data to the correct destination cluster.Once a disk can be imported, the API sends the destination information (IP address and TCP port) together with an HMAC signature to the third party.
Third party hands public part of the destination’s encryption key together with all necessary information to source cluster and tells it to start the move
The existing API for exporting instances (
OpBackupExport) will be extended to export instances to remote clusters.
Source cluster connects to destination cluster for each disk and transfers its data using the instance OS definition’s export and import scripts
Before starting, the source cluster must verify the HMAC signature of the certificate and destination information (IP address and TCP port).
When connecting to the remote machine, strong certificate checks must be employed.
Due to the asynchronous nature of the whole process, the destination cluster checks whether all disks have been transferred every time after transferring a single disk; if so, it destroys the encryption key
After sending all disks, the source cluster destroys its key
Destination cluster runs OS definition’s rename script to adjust instance settings if needed (e.g. IP address)
Destination cluster starts the instance if requested at the beginning by the third party
Source cluster removes the instance if requested
Instance move in pseudo code¶
The following pseudo code describes a script moving instances between clusters and what happens on both clusters.
Script is started, gets the instance name and destination cluster:
(instance_name, dest_cluster_name) = sys.argv[1:] # Get destination cluster object dest_cluster = db.FindCluster(dest_cluster_name) # Use database to find source cluster src_cluster = db.FindClusterByInstance(instance_name)
Script tells source cluster to stop instance:
# Stop instance src_cluster.StopInstance(instance_name) # Get instance specification (memory, disk, etc.) inst_spec = src_cluster.GetInstanceInfo(instance_name) (src_key_name, src_cert) = src_cluster.CreateX509Certificate()
CreateX509Certificateon source cluster:key_file = mkstemp() cert_file = "%s.cert" % key_file RunCmd(["/usr/bin/openssl", "req", "-new", "-newkey", "rsa:1024", "-days", "1", "-nodes", "-x509", "-batch", "-keyout", key_file, "-out", cert_file]) plain_cert = utils.ReadFile(cert_file) # HMAC sign using secret key, this adds a "X-Ganeti-Signature" # header to the beginning of the certificate signed_cert = utils.SignX509Certificate(plain_cert, utils.ReadFile(constants.X509_SIGNKEY_FILE)) # The certificate now looks like the following: # # X-Ganeti-Signature: $1234$28676f0516c6ab68062b[…] # -----BEGIN CERTIFICATE----- # MIICsDCCAhmgAwIBAgI[…] # -----END CERTIFICATE----- # Return name of key file and signed certificate in PEM format return (os.path.basename(key_file), signed_cert)
Script creates instance on destination cluster and waits for move to finish:
dest_cluster.CreateInstance(mode=constants.REMOTE_IMPORT, spec=inst_spec, source_cert=src_cert) # Wait until destination cluster gives us its certificate dest_cert = None disk_info = [] while not (dest_cert and len(disk_info) < len(inst_spec.disks)): tmp = dest_cluster.WaitOutput() if tmp is Certificate: dest_cert = tmp elif tmp is DiskInfo: # DiskInfo contains destination address and port disk_info[tmp.index] = tmp # Tell source cluster to export disks for disk in disk_info: src_cluster.ExportDisk(instance_name, disk=disk, key_name=src_key_name, dest_cert=dest_cert) print ("Instance %s sucessfully moved to %s" % (instance_name, dest_cluster.name))
CreateInstanceon destination cluster:# … if mode == constants.REMOTE_IMPORT: # Make sure certificate was not modified since it was generated by # source cluster (which must use the same secret) if (not utils.VerifySignedX509Cert(source_cert, utils.ReadFile(constants.X509_SIGNKEY_FILE))): raise Error("Certificate not signed with this cluster's secret") if utils.CheckExpiredX509Cert(source_cert): raise Error("X509 certificate is expired") source_cert_file = utils.WriteTempFile(source_cert) # See above for X509 certificate generation and signing (key_name, signed_cert) = CreateSignedX509Certificate() SendToClient("x509-cert", signed_cert) for disk in instance.disks: # Start socat RunCmd(("socat" " OPENSSL-LISTEN:%s,…,key=%s,cert=%s,cafile=%s,verify=1" " stdout > /dev/disk…") % port, GetRsaKeyPath(key_name, private=True), GetRsaKeyPath(key_name, private=False), src_cert_file) SendToClient("send-disk-to", disk, ip_address, port) DestroyX509Cert(key_name) RunRenameScript(instance_name)
ExportDiskon source cluster:# Make sure certificate was not modified since it was generated by # destination cluster (which must use the same secret) if (not utils.VerifySignedX509Cert(cert_pem, utils.ReadFile(constants.X509_SIGNKEY_FILE))): raise Error("Certificate not signed with this cluster's secret") if utils.CheckExpiredX509Cert(cert_pem): raise Error("X509 certificate is expired") dest_cert_file = utils.WriteTempFile(cert_pem) # Start socat RunCmd(("socat stdin" " OPENSSL:%s:%s,…,key=%s,cert=%s,cafile=%s,verify=1" " < /dev/disk…") % disk.host, disk.port, GetRsaKeyPath(key_name, private=True), GetRsaKeyPath(key_name, private=False), dest_cert_file) if instance.all_disks_done: DestroyX509Cert(key_name)
Miscellaneous notes¶
A very similar system could also be used for instance exports within the same cluster. Currently OpenSSH is being used, but could be replaced by socat and SSL/TLS.
During the design of intra-cluster instance moves we also discussed encrypting instance exports using GnuPG.
While most instances should have exactly the same configuration as on the source cluster, setting them up with a different disk layout might be helpful in some use-cases.
A cleanup operation, similar to the one available for failed instance migrations, should be provided.
ganeti-watchershould remove instances pending a move from another cluster after a certain amount of time. This takes care of failures somewhere in the process.RSA keys can be generated using the existing
bootstrap.GenerateSelfSignedSslCertfunction, though it might be useful to not write both parts into a single file, requiring small changes to the function. The public part always starts with-----BEGIN CERTIFICATE-----and ends with-----END CERTIFICATE-----.The source and destination cluster might be different when it comes to available hypervisors, kernels, etc. The destination cluster should refuse to accept an instance move if it can’t fulfill an instance’s requirements.
Privilege separation¶
Current state and shortcomings¶
All Ganeti daemons are run under the user root. This is not ideal from a security perspective as for possible exploitation of any daemon the user has full access to the system.
In order to overcome this situation we’ll allow Ganeti to run its daemon
under different users and a dedicated group. This also will allow some
side effects, like letting the user run some gnt-* commands if one
is in the same group.
Implementation¶
For Ganeti 2.2 the implementation will be focused on a the RAPI daemon
only. This involves changes to daemons.py so it’s possible to drop
privileges on daemonize the process. Though, this will be a short term
solution which will be replaced by a privilege drop already on daemon
startup in Ganeti 2.3.
It also needs changes in the master daemon to create the socket with new permissions/owners to allow RAPI access. There will be no other permission/owner changes in the file structure as the RAPI daemon is started with root permission. In that time it will read all needed files and then drop privileges before contacting the master daemon.
Feature changes¶
KVM Security¶
Current state and shortcomings¶
Currently all kvm processes run as root. Taking ownership of the hypervisor process, from inside a virtual machine, would mean a full compromise of the whole Ganeti cluster, knowledge of all Ganeti authentication secrets, full access to all running instances, and the option of subverting other basic services on the cluster (eg: ssh).
Proposed changes¶
We would like to decrease the surface of attack available if an hypervisor is compromised. We can do so adding different features to Ganeti, which will allow restricting the broken hypervisor possibilities, in the absence of a local privilege escalation attack, to subvert the node.
Dropping privileges in kvm to a single user (easy)¶
By passing the -runas option to kvm, we can make it drop privileges.
The user can be chosen by an hypervisor parameter, so that each instance
can have its own user, but by default they will all run under the same
one. It should be very easy to implement, and can easily be backported
to 2.1.X.
This mode protects the Ganeti cluster from a subverted hypervisor, but doesn’t protect the instances between each other, unless care is taken to specify a different user for each. This would prevent the worst attacks, including:
logging in to other nodes
administering the Ganeti cluster
subverting other services
But the following would remain an option:
terminate other VMs (but not start them again, as that requires root privileges to set up networking) (unless different users are used)
trace other VMs, and probably subvert them and access their data (unless different users are used)
send network traffic from the node
read unprotected data on the node filesystem
Running kvm in a chroot (slightly harder)¶
By passing the -chroot option to kvm, we can restrict the kvm
process in its own (possibly empty) root directory. We need to set this
area up so that the instance disks and control sockets are accessible,
so it would require slightly more work at the Ganeti level.
Breaking out in a chroot would mean:
a lot less options to find a local privilege escalation vector
the impossibility to write local data, if the chroot is set up correctly
the impossibility to read filesystem data on the host
It would still be possible though to:
terminate other VMs
trace other VMs, and possibly subvert them (if a tracer can be installed in the chroot)
send network traffic from the node
Running kvm with a pool of users (slightly harder)¶
If rather than passing a single user as an hypervisor parameter, we have a pool of useable ones, we can dynamically choose a free one to use and thus guarantee that each machine will be separate from the others, without putting the burden of this on the cluster administrator.
This would mean interfering between machines would be impossible, and can still be combined with the chroot benefits.
Running iptables rules to limit network interaction (easy)¶
These don’t need to be handled by Ganeti, but we can ship examples. If the users used to run VMs would be blocked from sending some or all network traffic, it would become impossible for a broken into hypervisor to send arbitrary data on the node network, which is especially useful when the instance and the node network are separated (using ganeti-nbma or a separate set of network interfaces), or when a separate replication network is maintained. We need to experiment to see how much restriction we can properly apply, without limiting the instance legitimate traffic.
Running kvm inside a container (even harder)¶
Recent linux kernels support different process namespaces through control groups. PIDs, users, filesystems and even network interfaces can be separated. If we can set up ganeti to run kvm in a separate container we could insulate all the host process from being even visible if the hypervisor gets broken into. Most probably separating the network namespace would require one extra hop in the host, through a veth interface, thus reducing performance, so we may want to avoid that, and just rely on iptables.
Implementation plan¶
We will first implement dropping privileges for kvm processes as a single user, and most probably backport it to 2.1. Then we’ll ship example iptables rules to show how the user can be limited in its network activities. After that we’ll implement chroot restriction for kvm processes, and extend the user limitation to use a user pool.
Finally we’ll look into namespaces and containers, although that might slip after the 2.2 release.
New OS states¶
Separate from the OS external changes, described below, we’ll add some internal changes to the OS.
Current state and shortcomings¶
There are two issues related to the handling of the OSes.
First, it’s impossible to disable an OS for new instances, since that will also break reinstallations and renames of existing instances. To phase out an OS definition, without actually having to modify the OS scripts, it would be ideal to be able to restrict new installations but keep the rest of the functionality available.
Second, gnt-instance reinstall --select-os shows all the OSes
available on the clusters. Some OSes might exist only for debugging and
diagnose, and not for end-user availability. For this, it would be
useful to “hide” a set of OSes, but keep it otherwise functional.
Proposed changes¶
Two new cluster-level attributes will be added, holding the list of OSes hidden from the user and respectively the list of OSes which are blacklisted from new installations.
These lists will be modifiable via gnt-os modify (implemented via
OpClusterSetParams), such that even not-yet-existing OSes can be
preseeded into a given state.
For the hidden OSes, they are fully functional except that they are not
returned in the default OS list (as computed via OpOsDiagnose),
unless the hidden state is requested.
For the blacklisted OSes, they are also not shown (unless the
blacklisted state is requested), and they are also prevented from
installation via OpInstanceCreate (in create mode).
Both these attributes are per-OS, not per-variant. Thus they apply to all of an OS’ variants, and it’s impossible to blacklist or hide just one variant. Further improvements might allow a given OS variant to be blacklisted, as opposed to whole OSes.
External interface changes¶
OS API¶
The OS variants implementation in Ganeti 2.1 didn’t prove to be useful enough to alleviate the need to hack around the Ganeti API in order to provide flexible OS parameters.
As such, for Ganeti 2.2 we will provide support for arbitrary OS parameters. However, since OSes are not registered in Ganeti, but instead discovered at runtime, the interface is not entirely straightforward.
Furthermore, to support the system administrator in keeping OSes
properly in sync across the nodes of a cluster, Ganeti will also verify
(if existing) the consistence of a new os_version file.
These changes to the OS API will bump the API version to 20.
OS version¶
A new os_version file will be supported by Ganeti. This file is not
required, but if existing, its contents will be checked for consistency
across nodes. The file should hold only one line of text (any extra data
will be discarded), and its contents will be shown in the OS information
and diagnose commands.
It is recommended that OS authors increase the contents of this file for any changes; at a minimum, modifications that change the behaviour of import/export scripts must increase the version, since they break intra-cluster migration.
Parameters¶
The interface between Ganeti and the OS scripts will be based on environment variables, and as such the parameters and their values will need to be valid in this context.
Names¶
The parameter names will be declared in a new file, parameters.list,
together with a one-line documentation (whitespace-separated). Example:
$ cat parameters.list
ns1 Specifies the first name server to add to /etc/resolv.conf
extra_packages Specifies additional packages to install
rootfs_size Specifies the root filesystem size (the rest will be left unallocated)
track Specifies the distribution track, one of 'stable', 'testing' or 'unstable'
As seen above, the documentation can be separate via multiple spaces/tabs from the names.
The parameter names as read from the file will be used for the command line interface in lowercased form; as such, there shouldn’t be any two parameters which differ in case only.
Values¶
The values of the parameters are, from Ganeti’s point of view, completely freeform. If a given parameter has, from the OS’ point of view, a fixed set of valid values, these should be documented as such and verified by the OS, but Ganeti will not handle such parameters specially.
An empty value must be handled identically as a missing parameter. In other words, the validation script should only test for non-empty values, and not for declared versus undeclared parameters.
Furthermore, each parameter should have an (internal to the OS) default value, that will be used if not passed from Ganeti. More precisely, it should be possible for any parameter to specify a value that will have the same effect as not passing the parameter, and no in no case should the absence of a parameter be treated as an exceptional case (outside the value space).
Environment variables¶
The parameters will be exposed in the environment upper-case and
prefixed with the string OSP_. For example, a parameter declared in
the ‘parameters’ file as ns1 will appear in the environment as the
variable OSP_NS1.
Validation¶
For the purpose of parameter name/value validation, the OS scripts
must provide an additional script, named verify. This script will
be called with the argument parameters, and all the parameters will
be passed in via environment variables, as described above.
The script should signify result/failure based on its exit code, and show explanatory messages either on its standard output or standard error. These messages will be passed on to the master, and stored as in the OpCode result/error message.
The parameters must be constructed to be independent of the instance specifications. In general, the validation script will only be called with the parameter variables set, but not with the normal per-instance variables, in order for Ganeti to be able to validate default parameters too, when they change. Validation will only be performed on one cluster node, and it will be up to the ganeti administrator to keep the OS scripts in sync between all nodes.
Instance operations¶
The parameters will be passed, as described above, to all the other
instance operations (creation, import, export). Ideally, these scripts
will not abort with parameter validation errors, if the verify
script has verified them correctly.
Note: when changing an instance’s OS type, any OS parameters defined at instance level will be kept as-is. If the parameters differ between the new and the old OS, the user should manually remove/update them as needed.
Declaration and modification¶
Since the OSes are not registered in Ganeti, we will only make a ‘weak’ link between the parameters as declared in Ganeti and the actual OSes existing on the cluster.
It will be possible to declare parameters either globally, per cluster (where they are indexed per OS/variant), or individually, per instance. The declaration of parameters will not be tied to current existing OSes. When specifying a parameter, if the OS exists, it will be validated; if not, then it will simply be stored as-is.
A special note is that it will not be possible to ‘unset’ at instance level a parameter that is declared globally. Instead, at instance level the parameter should be given an explicit value, or the default value as explained above.
CLI interface¶
The modification of global (default) parameters will be done via the
gnt-os command, and the per-instance parameters via the
gnt-instance command. Both these commands will take an addition
--os-parameters or -O flag that specifies the parameters in the
familiar comma-separated, key=value format. For removing a parameter, a
-key syntax will be used, e.g.:
# initial modification
$ gnt-instance modify -O use_dchp=true instance1
# later revert (to the cluster default, or the OS default if not
# defined at cluster level)
$ gnt-instance modify -O -use_dhcp instance1
Internal storage¶
Internally, the OS parameters will be stored in a new osparams
attribute. The global parameters will be stored on the cluster object,
and the value of this attribute will be a dictionary indexed by OS name
(this also accepts an OS+variant name, which will override a simple OS
name, see below), and for values the key/name dictionary. For the
instances, the value will be directly the key/name dictionary.
Overriding rules¶
Any instance-specific parameters will override any variant-specific parameters, which in turn will override any global parameters. The global parameters, in turn, override the built-in defaults (of the OS scripts).