Inventory of Load Balancing Parameters in MPTCP Schedulers

Abstract

In the era of ubiquitous connectivity, where multiple wired and wireless interfaces connect internet users, we can now ensure the reliability of online services. Multi-Path TCP (MPTCP) significantly improves data transport efficiency by aggregating multiple network paths into a single session to optimize network resources use. MPTCP's effectiveness depends on its scheduling decisions, which directly affect data flow, throughput, and service quality. This work presents a first labeled dataset detailing critical MPTCP scheduling parameters—congestion window sizes, unacknowledged transmitted segments, and latency—from the most popular classic schedulers: RR (RoundRobin), BLEST (Blocking Estimation-based MPTCP Scheduler), and ECF(Earliest Completion First). It allows an in-depth study of different load-balancing policies, thus clarifying the complexity of schedulers and the impact of parameters used in each load-balancing policy on data transfer. With 80033271 rows extracted across 15 scenarios, providing usable 21 numeric values per row, the objective of this dataset is to inventory all the decisive parameters in schedulers' decision-making. This dataset will not only facilitate the selection of optimal parameters for load-balancing policies, but also serve as a foundation for the development of numerous novel, supervised machine learning methods specifically tailored for scheduling tasks.