Efficient execution plan for egress traffic engineering

The main objective of Egress Peer Engineering (EPE) is to steer traffic exiting one Autonomous System (AS) to another in the most cost-effective way by assigning network traffic flows of different destinations to internal routes specific to every AS. The traffic assignment process is carried out to satisfy network operators? objectives ,which include optimizing resource utilization, minimizing monetary costs and avoiding overloading the peer links. Due to network traffic dynamicity and unpredictability, traffic assignments should be constantly updated so that the aforementioned objectives are satisfied. Each of these updates results in traffic assignment changes that transition the network to a more optimized state. Executing these changes all at once is detrimental to the internal network infrastructure. To tackle this issue, this work targets finding execution plans involving several intermediate steps whereby each step includes a subset of traffic assignment changes. While executing these steps, the network operator?s objectives need to be guaranteed. To that end, an oracle algorithm that generates all the possible balanced subsets of traffic changes as execution plans is formulated. This algorithm is compared to two heuristics that are designed based on an analytical study of the problem itself. To effectively evaluate these algorithms, evaluation criteria are devised that encompass several technical and design quality metrics of the desired execution plan. These three approaches are evaluated on network configurations of small size networks, and the results obtained show that one of the heuristics outperforms the oracle implementation in terms of execution time while producing comparable results based on the evaluation criteria. For big networks, the best performing heuristic satisfies the quality metrics and generates its best execution plan in a short period of time.

Automated summary

The main goal of Egress Peer Engineering (EPE) is to optimize network resources, reduce costs, and avoid overloading by assigning and updating traffic flows in multiple steps. This study introduces an algorithm and two heuristics for generating execution plans based on the analysis of the problem.