Network Performance Management Using Application-centric Key Performance Indicators

The Internet and intranets are viewed as capable of supplying Anything, Anywhere, Anytime and e-commerce, e-government, e-community, and military C4I are now deploying many and varied applications to serve their needs. Network management is currently centralized in operations centers. To assure customer satisfaction with the network performance they typically plan, configure and monitor the network devices to insure an excess of bandwidth, that is overprovision. If this proves uneconomical or if complex and poorly understood interactions of equipment, protocols and application traffic degrade performance creating customer dissatisfaction, another more application-centric, way of managing the network will be needed. This research investigates a new qualitative class of network performance measures derived from the current quantitative metrics known as quality of service (QOS) parameters. The proposed class of qualitative indicators focuses on utilizing current network performance measures (QOS values) to derive abstract quality of experience (QOE) indicators by application class. These measures may provide a more user or application-centric means of assessing network performance even when some individual QOS parameters approach or exceed specified levels. The mathematics of functional analysis suggests treating QOS performance values as a vector, and, by mapping the degradation of the application performance to a characteristic lp-norm curve, a qualitative QOE value (good/poor) can be calculated for each application class. A similar procedure could calculate a QOE node value (satisfactory/unsatisfactory) to represent the service level of the switch or router for the current mix of application traffic. To demonstrate the utility of this approach a discrete event simulation (DES) test-bed, in the OPNET telecommunications simulation environment, was created modeling the topology and traffic of three semi-autonomous networks connected by a backbone. Scenarios, designed to degrade performance by under-provisioning links or nodes, are run to evaluate QOE for an access network. The application classes and traffic load are held constant. Future research would include refinement of the mathematics, many additional simulations and scenarios varying other independent variables. Finally collaboration with researchers in areas as diverse as human computer interaction (HCI), software engineering, teletraffic engineering, and network management will enhance the concepts modeled