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In this paper, we present a new traffic engineering (TE) software frameworkto analyze, configure, and optimize (with the aid of a linear programmingsolver) a network for service provisioning. The developed software tool isbased on our new data-driven traffic engineering approach that analyzes a largevolume of network configuration data generated given the user input. Byanalyzing the data, one can then make efficient decisions later when designinga traffic engineering solution. We focus on three well-known trafficengineering objective functions: minimum cost routing (MCR), load balancing(LB), and average delay (AD). With this new tool, one can answer numeroustraffic engineering questions. For example, what are the differences among thethree objective functions? What is the impact of an objective function on linkutilization? How many candidate paths are enough to achieve optimality ornear-optimality with respect to a specific objective. This new software toolallows us to conveniently perform various experiments and visualize the resultsfor performance analysis. As case studies, this paper presents examples thatanswer the questions for two traffic engineering problems: (1) how many pathsare required to obtain a solution that is within a few percent from the optimalsolution and whether that number is fixed for any network size? (2) how thechoice of single-path/multi-path routing affects the load in the network? Forthe first problem, it turns out that the number of paths needed to achieveoptimality increases as the number of links in the network increases.

A New Software Framework for Traffic Engineering: Path Cardinality and the Effect of Multipath on Residual Capacity