OpenSIPS在多站点IP-PBX环境中的应用

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"Damien_Sandras在2015年OpenSIPS峰会上的演讲,主要讨论了OpenSIPS在多站点IP电话系统中的应用,以及BeIP公司如何利用OpenSIPS构建商业IP PBX产品。演讲者是OpenSIPS和FOSDEM的创始人Damien Sandras,以及NOVACOM的成员Steve Frécinaux。" 在本次演讲中,Damien Sandras和Steve Frécinaux分享了他们如何将OpenSIPS用作IP-PBX(IP私人分支交换机)的替代方案,特别是在一个多站点环境中。BeIP公司成立于2008年,源自NOVACOM,它销售基于OpenSIPS和Asterisk的IPPBX产品,大约有15,000名用户在比利时和卢森堡地区使用他们的产品。 一个具体的案例是他们为一个拥有700用户的政府机构设计的系统,该机构有一个主站点和7个远程办公室。这个案例强调了高度可用性、随时随地的可达性、以及与 Presence、即时消息和Exchange集成的重要性。这些需求对系统的架构设计提出了挑战。 在架构设计方面,BeIP注意到他们的服务器主要还是部署在本地,这意味着他们面临着与云服务提供商或ITSP(Internet Telephony Service Provider)不同的约束。他们的用户规模通常在10到100人之间,但硬件成本相对较高,因为服务器数量有限。此外,带宽资源稀缺,常见的是1Mbps的站点间链接,而且服务质量(QoS)保障通常不足。这使得维护工作变得昂贵且复杂。 在这种环境下,OpenSIPS的优势在于其灵活性和可扩展性,能够适应小到中型企业的需求,同时提供必要的高可用性和集成功能,以满足客户对于稳定通信和高效协作的要求。通过优化QoS策略和利用OpenSIPS的路由功能,BeIP能够克服带宽限制,确保语音通话质量。而其HA(高可用性)解决方案则保证了即使在单个站点故障的情况下,整个通信系统也能持续运行。 Damien Sandras的演讲突出了OpenSIPS在解决多站点企业通信问题上的价值,以及BeIP如何利用开源技术来创建经济高效且适应各种网络条件的IP通信解决方案。

'' Basic Operations example using TensorFlow library. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' from __future__ import print_function import tensorflow as tf # Basic constant operations # The value returned by the constructor represents the output # of the Constant op. a = tf.constant(2) b = tf.constant(3) # Launch the default graph. with tf.compat.v1.Session() as sess: print("a=2, b=3") print("Addition with constants: %i" % sess.run(a+b)) print("Multiplication with constants: %i" % sess.run(a*b)) # Basic Operations with variable as graph input # The value returned by the constructor represents the output # of the Variable op. (define as input when running session) # tf Graph input a = tf.placeholder(tf.int16) b = tf.placeholder(tf.int16) # Define some operations add = tf.add(a, b) mul = tf.multiply(a, b) # Launch the default graph. with tf.compat.v1.Session() as sess: # Run every operation with variable input print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})) print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})) # ---------------- # More in details: # Matrix Multiplication from TensorFlow official tutorial # Create a Constant op that produces a 1x2 matrix. The op is # added as a node to the default graph. # # The value returned by the constructor represents the output # of the Constant op. matrix1 = tf.constant([[3., 3.]]) # Create another Constant that produces a 2x1 matrix. matrix2 = tf.constant([[2.],[2.]]) # Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs. # The returned value, 'product', represents the result of the matrix # multiplication. product = tf.matmul(matrix1, matrix2) # To run the matmul op we call the session 'run()' method, passing 'product' # which represents the output of the matmul op. This indicates to the call # that we want to get the output of the matmul op back. # # All inputs needed by the op are run automatically by the session. They # typically are run in parallel. # # The call 'run(product)' thus causes the execution of threes ops in the # graph: the two constants and matmul. # # The output of the op is returned in 'result' as a numpy `ndarray` object. with tf.compat.v1.ession() as sess: result = sess.run(product) print(result) # ==> [[ 12.]]

2023-06-11 上传

代码怎么样'' Basic Operations example using TensorFlow library. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' from __future__ import print_function import tensorflow as tf # Basic constant operations # The value returned by the constructor represents the output # of the Constant op. a = tf.constant(2) b = tf.constant(3) # Launch the default graph. with tf.compat.v1.Session() as sess: print("a=2, b=3") print("Addition with constants: %i" % sess.run(a+b)) print("Multiplication with constants: %i" % sess.run(a*b)) # Basic Operations with variable as graph input # The value returned by the constructor represents the output # of the Variable op. (define as input when running session) # tf Graph input a = tf.placeholder(tf.int16) b = tf.placeholder(tf.int16) # Define some operations add = tf.add(a, b) mul = tf.multiply(a, b) # Launch the default graph. with tf.compat.v1.Session() as sess: # Run every operation with variable input print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})) print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})) # ---------------- # More in details: # Matrix Multiplication from TensorFlow official tutorial # Create a Constant op that produces a 1x2 matrix. The op is # added as a node to the default graph. # # The value returned by the constructor represents the output # of the Constant op. matrix1 = tf.constant([[3., 3.]]) # Create another Constant that produces a 2x1 matrix. matrix2 = tf.constant([[2.],[2.]]) # Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs. # The returned value, 'product', represents the result of the matrix # multiplication. product = tf.matmul(matrix1, matrix2) # To run the matmul op we call the session 'run()' method, passing 'product' # which represents the output of the matmul op. This indicates to the call # that we want to get the output of the matmul op back. # # All inputs needed by the op are run automatically by the session. They # typically are run in parallel. # # The call 'run(product)' thus causes the execution of threes ops in the # graph: the two constants and matmul. # # The output of the op is returned in 'result' as a numpy `ndarray` object. with tf.compat.v1.ession() as sess: result = sess.run(product) print(result) # ==> [[ 12.]]

2023-06-11 上传