Network management

About: Network management is a research topic. Over the lifetime, 17859 publications have been published within this topic receiving 234520 citations. The topic is also known as: computer network management & NM.

Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks

Abstract: The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks. This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data. This architecture leverages and combines existing frequent itemset discovery over data streams, association rule deduction, frequent sequential pattern mining, and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

Identifying network management policies

Abstract: A policy based network management (PBNM) system may identify one or more policies associated with a network component (e.g., a network device, a device group, a device subgroup, a user, an application, an end-host, etc.) by identifying one or more policies directly associated with the network component, generating a list of one or more groups to which the network component belongs, and identifying one or more policies associated with each of the groups in the generated list. An aggregated data set (e.g., a hash table or a balanced tree) may be used to store network component identity elements, one or more pointers to a deployed policy tree, and one or more pointers to a network configuration tree. Each identity element in the data set identifies a network component and has an associated network configuration tree pointer and one or more associated deployed policy tree pointers.

Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps

Abstract: The rapid adoption of mobile messaging Apps has enabled us to collect massive amount of encrypted Internet traffic of mobile messaging. The classification of this traffic into different types of in-App service usages can help for intelligent network management, such as managing network bandwidth budget and providing quality of services. Traditional approaches for classification of Internet traffic rely on packet inspection, such as parsing HTTP headers. However, messaging Apps are increasingly using secure protocols, such as HTTPS and SSL, to transmit data. This imposes significant challenges on the performances of service usage classification by packet inspection. To this end, in this paper, we investigate how to exploit encrypted Internet traffic for classifying in-App usages. Specifically, we develop a system, named CUMMA, for classifying service usages of mobile messaging Apps by jointly modeling user behavioral patterns, network traffic characteristics, and temporal dependencies. Along this line, we first segment Internet traffic from traffic-flows into sessions with a number of dialogs in a hierarchical way. Also, we extract the discriminative features of traffic data from two perspectives: (i) packet length and (ii) time delay. Next, we learn a service usage predictor to classify these segmented dialogs into single-type usages or outliers. In addition, we design a clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers and decompose mixed dialogs into sub- dialogs of single-type usage. Indeed, CUMMA enables mobile analysts to identify service usages and analyze end-user in-App behaviors even for encrypted Internet traffic. Finally, the extensive experiments on real-world messaging data demonstrate the effectiveness and efficiency of the proposed method for service usage classification.

Apparatus and method for measuring and using response to SNMP requests to provide real-time network parameter estimates in a network management zone

Abstract: Method creates Real-Time Estimates (RTE) of network parameters for responsive resources of a network management zone (NMZ) by sending requests in a management protocol and uses those real-time estimates to present a resource map of the NMZ, possibly altering a responsive resource, possibly posting a service schedule request. The invention includes implementation mechanisms and installation packages. The RTE of network parameter is a product of the process. Constructing a quality of service measure from RTE of at least two network parameters. Quality of service measure as a product of the process. The quality of service measure may include or be the Mean Opinion Score.

Peer-to-Peer Cloud Provisioning: Service Discovery and Load-Balancing

Abstract: This chapter presents: (i) a layered peer-to-peer Cloud provisioning architecture; (ii) a summary of the current state-of-the-art in Cloud provisioning with particular emphasis on service discovery and load-balancing; (iii) a classification of the existing peer-to-peer network management model with focus on extending the DHTs for indexing and managing complex provisioning information; and (iv) the design and implementation of novel, extensible software fabric (Cloud peer) that combines public/private clouds, overlay networking and structured peer-to-peer indexing techniques for supporting scalable and self-managing service discovery and load-balancing in Cloud computing environments. Finally, an experimental evaluation is presented that demonstrates the feasibility of building next generation Cloud provisioning systems based on peer-to-peer network management and information dissemination models. The experimental test-bed has been deployed on a public cloud computing platform, Amazon EC2, which demonstrates the effectiveness of the proposed peer-to-peer Cloud provisioning software fabric.