from nepi.execution.trace import TraceAttr
from nepi.util.serializer import ECSerializer, SFormats
from nepi.util.plotter import ECPlotter, PFormats
+from nepi.util.netgraph import NetGraph, TopologyType
# TODO: use multiprocessing instead of threading
# TODO: Allow to reconnect to a running experiment instance! (reconnect mode vs deploy mode)
.. note::
An experiment, or scenario, is defined by a concrete set of resources,
- behavior, configuration and interconnection of those resources.
+ and the behavior, configuration and interconnection of those resources.
The Experiment Description (ED) is a detailed representation of a
single experiment. It contains all the necessary information to
allow repeating the experiment. NEPI allows to describe
recreated (and re-run) by instantiating an EC and recreating
the same experiment description.
- In NEPI, an experiment is represented as a graph of interconnected
+ An experiment is represented as a graph of interconnected
resources. A resource is a generic concept in the sense that any
component taking part of an experiment, whether physical of
virtual, is considered a resource. A resources could be a host,
single resource. ResourceManagers are specific to a resource
type (i.e. An RM to control a Linux application will not be
the same as the RM used to control a ns-3 simulation).
- To support a new type of resource in NEPI, a new RM must be
- implemented. NEPI already provides a variety of
- RMs to control basic resources, and new can be extended from
- the existing ones.
+ To support a new type of resource, a new RM must be implemented.
+ NEPI already provides a variety of RMs to control basic resources,
+ and new can be extended from the existing ones.
Through the EC interface the user can create ResourceManagers (RMs),
configure them and interconnect them, to describe an experiment.
ec = serializer.load(filepath)
return ec
- def __init__(self, exp_id = None, local_dir = None, persist = False):
- """ ExperimentController entity to model an execute a network experiment.
+ def __init__(self, exp_id = None, local_dir = None, persist = False,
+ add_node_callback = None, add_edge_callback = None, **kwargs):
+ """ ExperimentController entity to model an execute a network
+ experiment.
:param exp_id: Human readable name to identify the experiment
- :type name: str
+ :type exp_id: str
:param local_dir: Path to local directory where to store experiment
related files
- :type name: str
+ :type local_dir: str
:param persist: Save an XML description of the experiment after
completion at local_dir
- :type name: bool
+ :type persist: bool
- """
+ :param add_node_callback: Callback to invoke for node instantiation
+ when automatic topology creation mode is used
+ :type add_node_callback: function
+
+ :param add_edge_callback: Callback to invoke for edge instantiation
+ when automatic topology creation mode is used
+ :type add_edge_callback: function
+ """
super(ExperimentController, self).__init__()
# Logging
# EC state
self._state = ECState.RUNNING
+ # Automatically construct experiment description
+ self._netgraph = None
+ if add_node_callback and add_edge_callback:
+ self._build_from_netgraph(add_node_callback, add_edge_callback,
+ **kwargs)
+
# The runner is a pool of threads used to parallelize
# execution of tasks
self._nthreads = 20
@property
def persist(self):
- """ If Trie persist the ExperimentController to XML format upon completion
+ """ If True, persists the ExperimentController to XML format upon
+ experiment completion
"""
return self._persist
+ @property
+ def netgraph(self):
+ """ Return NetGraph instance if experiment description was automatically
+ generated
+
+ """
+ return self._netgraph
+
@property
def abort(self):
""" Returns True if the experiment has failed and should be interrupted,
return rm
def get_resources_by_type(self, rtype):
- """ Returns a registered ResourceManager by its guid
+ """ Returns the ResourceManager objects of type rtype
:param rtype: Resource type
:type rtype: string
"""
rms = []
for guid, rm in self._resources.iteritems():
- if rm.get_rtype() == type:
+ if rm.get_rtype() == rtype:
rms.append(rm)
return rms
@property
def resources(self):
- """ Returns the set() of guids of all the ResourceManager
+ """ Returns the guids of all ResourceManagers
:return: Set of all RM guids
- :rtype: set
+ :rtype: list
"""
keys = self._resources.keys()
return keys
+ def filter_resources(self, rtype):
+ """ Returns the guids of all ResourceManagers of type rtype
+
+ :param rtype: Resource type
+ :type rtype: string
+
+ :rtype: list of guids
+
+ """
+ rms = []
+ for guid, rm in self._resources.iteritems():
+ if rm.get_rtype() == rtype:
+ rms.append(rm.guid)
+ return rms
+
def register_resource(self, rtype, guid = None):
""" Registers a new ResourceManager of type 'rtype' in the experiment
self._cond.notify()
self._cond.release()
+ def _build_from_netgraph(self, add_node_callback, add_edge_callback,
+ **kwargs):
+ """ Automates experiment description using a NetGraph instance.
+ """
+ self._netgraph = NetGraph(**kwargs)
+
+ ### Add resources to the EC
+ for nid in self.netgraph.graph.nodes():
+ add_node_callback(self, nid)
+
+ #### Add connections between resources
+ for nid1, nid2 in self.netgraph.graph.edges():
+ add_edge_callback(self, nid1, nid2)
+
+
git://git.onelab.eu / nepi.git / blobdiff