Abstract-Managing networks and services in large virtual enterprises often requires significant expertise to decipher and reconcile information coming from several heterogeneous sources. This presents a significant challenge as the person with the network management expertise is not necessarily the person who has the task or vision of how to maximize the value of these services. Conversely it would be valuable for dedicated network managers to have insight into the meaning of the services running over the network. This paper presents an approach to empowering users to monitor and eventually manage network devices and services that they are not necessarily expert in. This paper details this challenge, presents two enabling technological frameworks and offers two compelling case studies currently being addressed in the Federated, Autonomic Management of End-to-End Communications Services (FAME) project. Due to the continuously growing amount of monitoring data the question of what to show is increasingly becoming as important as how to show it [1] . When considering data from heterogeneous sources, relating the information so that it can be compared and prioritized becomes a significant issue. A semantic layer on top of these streams of data is required to provide a common framework and vocabulary so that comparisons can be drawn. However, a mechanism to dynamically structure the information is also needed to semantically prioritize it, so only the information that is most pertinent to the end user's needs is presented. By introducing appropriate semantics and prioritizations a non-expert user can be offered insights into the underlying information to enable them to monitor and manage underlying networks and services from which the information originates. The work presented in this paper presents an approach to capture and use domain expertise to support visualizations of such monitoring and management information. By applying domain knowledge to the structured exploration of information [2] , users are empowered to delve beyond a set of static results to explore a set of meaningful responses. The Semantic Attribute Reconciliation Architecture (SARA) [3] describes a system that utilizes the subjective expertise of experts to determine what information, from several sources, may be reconciled to highlight significance for the end user. This paper provides an overview of SARA and introduces the Insight Framework and Authoring Tool. Insight facilitates the creation of several visually prioritized information overviews with a sophisticated information exploration paradigm, to sequence and prioritize information for visualization. This paper presents an overview of SARA and Insight in the context of two case studies highlighting the challenges from the Federated, Autonomic Management of End-to-End Communications Services (FAME) [4] Irish Strategic Research Cluster. The paper begins by presenting the visual monitoring and management challenges faced in FAME. Harmonizing monitoring data requires mechanisms for mapping and federating the large volumes of low-level data produced by monitoring systems into semantically meaningful information and knowledge that may be discretely consumed by all parties involved in producing, delivering or consuming a communications service. Any complex system will have domain experts who are very familiar with how the constituent parts of the system can be managed, and are particularly aware of the end-to-end operating constraints of those constituent parts. Encoding this expertise in a manner that can be utilized by other stakeholders presents a significant challenge [5] , and arguably requires significant advances in knowledge representation and engineering. The benefit of enabling such expertise to be encoded, aggregated and interpreted across diverse domains enables other stakeholders of other systems to monitor other parts of the system and relate their operation to their own system. It also enables common knowledge to be shared and reused across multiple domains. From a practical point of view, there are three major problems with current monitoring and management techniques that this work attempts to solve. The first is the inherent heterogeneity of management information. For example, many systems use proprietary tools or command-line interfaces to configure devices, networks and services, but monitor their performance using the other monitoring tools like SNMP. This requires a skilled human operator that can understand both languages to ensure that an issued command is performed correctly, where this process can require days or weeks of tedious iterative fine-tuning. The second is the potentially large amount of data produced, as seen for example with "alarm storms" that are common in networks under stress. This leads to "information overload", which complicates an already difficult task of managing networks, which is exacerbated when end-to-end concerns must be understood by different systems using different management philosophies. The third is the presence of stove-pipe based partitioning between end-toend user quality of experience-focused monitoring and typically technologically-or geographically-bounded network or network element monitoring and configuration functions.
Our research is also based on Conlan et al.'s work REF to express a semantically enriched visualization approch for non-expert users to monitor networks and services.
Towards non-expert users monitoring networks and services through semantically enhanced visualizations
2010 International Conference on Network and Service Management
2010 International Conference on Network and Service Management
Computer Science