N093-219 TITLE: Network Operations (NetOps) Data Transport Optimization Engine

TECHNOLOGY AREAS: Information Systems

ACQUISITION PROGRAM: JPEO JTRS, Network Enterprise Domain (NED) ACAT I

OBJECTIVE: To provide optimization of NetOps data transfer and a greatly simplified interface.

DESCRIPTION: The goal of this effort is to develop an understanding on how to dynamically determine the data that has high informational content. The goal also is to investigate and define a simplified interface that will allow all network management tools to communicate with each other. .

Today�s networks are typically infrastructure backed networks. These networks are richly connected, reliable, and have a high bandwidth backbone with a stable topology. A tactical mobile ad hoc network, on the other hand, has very different characteristics. It is intermittently connected, very low bandwidth, and has a highly dynamic network topology. This network is much more complex than an infrastructure backed network and bandwidth resource is scarce with many applications and services competing for it constantly.

A number of schemes have been adopted to regulate network traffic when bandwidth is become scarce. One such scheme is Multi-Level Precedence and Preemption (MLPP). In a nutshell, MLPP requires that applications with higher precedence (or priority) be given preference over those with lower precedence when network resources are scarce. While schemes like MLPP are efficient in regulating network traffic, they do very little to reduce the bandwidth requirement of an application or service.

Currently, existing network management tools collect large volume of data and transmit 100% of those data over the network. This adds a network management overhead on the bandwidth. Nobody has defined the minimum set of NetOps data needed to effective manage the network. It is not necessary to analyze 100% of the data collected to infer the status of any network. This is mainly due to the fact that not all data are high in carrying information content. Algorithms must be developed to dynamically determine the set of data that has high informational content.

The other issue is currently there does not exist any interface by which all Network Management tools communicate with each other. While a number of interface definitions are available, they are very complex. Due to their complex nature, vendors of the commercial-off-the-shelf (COTS) tools are typically unwilling to adhere to those standards. In order to mitigate this problem, a simple interface that can handle NetOps data transfer must be defined and developed.

Once the algorithms for determining high informational content data has been developed and the simplest interface for transferring NetOps data has been defined, an engine must be created to perform the optimization and transport of the data. By having such a tool, the interactions between different tools can be made easy and the network management overhead on the bandwidth can be significantly reduced. The payoff from this technology will be a cost effective way to integrate all, current and future, network management tools.

PHASE I: Develop approaches for dynamically determining the set of data that has the most relevant information for the task being performed by the user. Determine how to dynamically control data gathering scope at the source and cross-network. Provide a paper documenting several approaches for determining relevant data dynamically. As part of the interface study, research and define the simplest interface and related processes, which can be used in order to transfer all NetOps information. Provide a paper defining the simplest interface.

PHASE II: Implement the best approach resulting from the documented reports in Phase I. Deliverable includes interface control document and prototype software that will be tested at CERDEC. Deliverable final report will include the final design, as well as test results, and any results of modeling and simulation. The prototype must demonstrate how it interfaces with all network management tools. It must also demonstrate how it dynamically determines data with high entropy.

PHASE III: Complete the development of the prototype described in Phase II and refine to the degree necessary to transition into a program. Some potential programs this capability could transition to include the JTRS NMS, Future Combat System NMS, and WIN-T NMS.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Future commercial wireless networks will be ad hoc networks and therefore will be limited in bandwidth resource. Hence, they will also be required optimize data streams based on entropy. By researching them now and building a prototype software solution the commercial world would have tools to best utilize bandwidth.

REFERENCES:
1. Lakshminath Bhuvanagiri, Sumit Ganguly, Estimating Entropy over Data Streams, http://icdcit.org/users/sganguly/ent.pdf

2. M A.Wagner and B Plattner. "Entropy based worm and anomaly detection in fast IP networks. In 14th IEEE WET ICE, STCA Security Workshop, 2005

KEYWORDS: Entropy; Interface; Bandwidth; Optimization; Transport; JTRS

** TOPIC AUTHOR (TPOC) **

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