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.

Rule Discovery in Telecommunication AlarmData

Abstract: Fault management is an important but difficult area of telecommunication network management: networks produce large amounts of alarm information which must be analyzed and interpreted before faults can be located. So called alarm correlation is a central technique in fault identification. While the use of alarm correlation systems is quite popular and methods for expressing the correlations are maturing, acquiring all the knowledge necessary for constructing an alarm correlation system for a network and its elements is difficult. We describe a novel partial solution to the task of knowledge acquisition for correlation systems. We present a method and a tool for the discovery of recurrent patterns of alarms in databases; these patterns, episode rules, can be used in the construction of real-time alarm correlation systems. We also present tools with which network management experts can browse the large amounts of rules produced. The construction of correlation systems becomes easier with these tools, as the episode rules provide a wealth of statistical information about recurrent phenomena in the alarm stream. This methodology has been implemented in a research system called TASA, which is used by several telecommunication operators. We briefly discuss experiences in the use of TASA.

Automatic management of network security policy

Abstract: The paper describes work in our project funded by the DARPA Dynamic Coalitions program to design, develop, and demonstrate a system for automatically managing security policies in dynamic networks. Specifically, we aim to reduce human involvement in network management by building a practical network reconfiguration system so that simple security policies stated as positive and negative invariants are upheld as the network changes. The focus of this project is a practical tool to help systems administrators verifiably enforce simple multi-layer network security policies. Our key design considerations are computational cost of policy validation and the power of the enforcement primitives. The central component is a policy engine populated by models of network elements and services that validates policies and computes new configuration settings for network elements when they are violated. We instantiate our policy enforcement tool using a monitoring and instrumentation layer that reports network changes as they occur and implements configuration changes computed by the policy engine.

A measurement study on co-residence threat inside the cloud

Abstract: As the most basic cloud service model, Infrastructure as a Service (IaaS) has been widely used for serving the ever-growing computing demand due to the prevalence of the cloud. Using pools of hypervisors within the cloud, IaaS can support a large number of Virtual Machines (VMs) and scale services in a highly dynamic manner. However, it is well-known that the VMs in IaaS are vulnerable to co-residence threat, which can be easily exploited to launch different malicious attacks. In this measurement study, we investigate how IaaS evolves in VM placement, network management, and Virtual Private Cloud (VPC), as well as the impact upon co-residence. Specifically, through intensive measurement probing, we first profile the dynamic environment of cloud instances inside the cloud. Then using real experiments, we quantify the impacts of VM placement and network management upon co-residence. Moreover, we explore VPC, which is a defensive network-based service of Amazon EC2 for security enhancement, from the routing perspective. On one hand, our measurement shows that VPC is widely used and can indeed suppress co-residence threat. On the other hand, we demonstrate a new approach to achieving co-residence in VPC, indicating that co-residence threat still exists in the cloud.

Statistical analysis of network traffic for adaptive faults detection

Abstract: This paper addresses the problem of normal operation baselining for automatic detection of network anomalies. A model of network traffic is presented in which studied variables are viewed as sampled from a finite mixture model. Based on the stochastic approximation of the maximum likelihood function, we propose baselining network normal operation, using the asymptotic distribution of the difference between successive estimates of model parameters. The baseline random variable is shown to be stationary, with mean zero under normal operation. Anomalous events are shown to induce an abrupt jump in the mean. Detection is formulated as an online change point problem, where the task is to process the baseline random variable realizations, sequentially, and raise alarms as soon as anomalies occur. An analytical expression of false alarm rate allows us to choose the design threshold, automatically. Extensive experimental results on a real network showed that our monitoring agent is able to detect unusual changes in the characteristics of network traffic, adapt to diurnal traffic patterns, while maintaining a low alarm rate. Despite large fluctuations in network traffic, this work proves that tailoring traffic modeling to specific goals can be efficiently achieved.

Self-Healing in Emerging Cellular Networks: Review, Challenges, and Research Directions

Abstract: Mobile cellular network operators spend nearly a quarter of their revenue on network management and maintenance. Incidentally, a significant proportion of that budget is spent on resolving outages that degrade or disrupt cellular services. Historically, operators mainly rely on human expertise to identify, diagnose, and resolve such outages. However, with growing cell density and diversifying cell types, this approach is becoming less and less viable, both technically and financially. To cope with this problem, research on self-healing solutions has gained significant momentum in recent years. Self-healing solutions either assist in resolving these outages or carry out the task autonomously without human intervention, thus reducing costs while improving mobile cellular network reliability. However, despite their growing popularity, to this date no survey has been undertaken for self-healing solutions in mobile cellular networks. This paper aims to bridge this gap by providing a comprehensive survey of self-healing solutions proposed in the domain of mobile cellular networks, along with an analysis of the techniques and methodologies employed in those solutions. This paper begins by providing a quantitative analysis to highlight why in emerging mobile cellular network self-healing will become a necessity instead of a luxury. Building on this motivation, this paper provides a review and taxonomy of existing literature on self-healing. Challenges and prospective research directions for developing self-healing solutions for emerging and future mobile cellular networks are also discussed in detail. Particularly, we identify that the most demanding challenges from self-healing perspective are the difficulty of meeting 5G low latency and the high quality of experience requirement.