blob: 41235635c6a6c0953b2ac65e1665f7ee77c80364 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
|
# Copyright (c) 2015 Intel Research and Development Ireland Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import abc
class BenchmarkBaseClass(object):
'''
This class represents a Benchmark that we want to run on the platform.
One of them will be the calculation of the throughput changing the
configuration parameters
'''
def __init__(self, name, params):
if not params:
params = dict()
if not isinstance(params, dict):
raise ValueError("Parameters need to be provided in a dict")
for param in self.get_features()['parameters']:
if param not in params.keys():
params[param] = self.get_features()['default_values'][param]
for param in self.get_features()['parameters']:
if params[param] not in \
(self.get_features())['allowed_values'][param]:
raise ValueError('Value of parameter "' + param +
'" is not allowed')
self.name = name
self.params = params
def get_name(self):
return self.name
def get_params(self):
return self.params
def get_features(self):
features = dict()
features['description'] = 'Please implement the method ' \
'"get_features" for your benchmark'
features['parameters'] = list()
features['allowed_values'] = dict()
features['default_values'] = dict()
return features
@abc.abstractmethod
def init(self):
"""
Initializes the benchmark
:return:
"""
raise NotImplementedError("Subclass must implement abstract method")
@abc.abstractmethod
def finalize(self):
"""
Finalizes the benchmark
:return:
"""
raise NotImplementedError("Subclass must implement abstract method")
@abc.abstractmethod
def run(self):
"""
This method executes the specific benchmark on the VNF already
instantiated
:return: list of dictionaries (every dictionary contains the results
of a data point
"""
raise NotImplementedError("Subclass must implement abstract method")
|
