Throughput Optimal On-Line Algorithms for Advanced Resource Reservation in Ultra High-Speed Networks

TL;DR

This paper introduces two new online algorithms, BatchAll and BatchLim, for advanced resource reservation in ultra high-speed networks, achieving optimal throughput with provable delay bounds and practical efficiency.

Contribution

The paper presents novel polynomial-time online algorithms for advanced reservation that guarantee optimal throughput and bounded delays, with practical path-limiting strategies.

Findings

BatchAll and BatchLim achieve optimal throughput performance.

Simulations show 3 to 5 paths suffice for near-optimal results.

Algorithms outperform greedy benchmarks in throughput and delay.

Abstract

Advanced channel reservation is emerging as an important feature of ultra high-speed networks requiring the transfer of large files. Applications include scientific data transfers and database backup. In this paper, we present two new, on-line algorithms for advanced reservation, called BatchAll and BatchLim, that are guaranteed to achieve optimal throughput performance, based on multi-commodity flow arguments. Both algorithms are shown to have polynomial-time complexity and provable bounds on the maximum delay for 1+epsilon bandwidth augmented networks. The BatchLim algorithm returns the completion time of a connection immediately as a request is placed, but at the expense of a slightly looser competitive ratio than that of BatchAll. We also present a simple approach that limits the number of parallel paths used by the algorithms while provably bounding the maximum reduction factor in the transmission throughput. We show that, although the number of different paths can be exponentially large, the actual number of paths needed to approximate the flow is quite small and proportional to the number of edges in the network. Simulations for a number of topologies show that, in practice, 3 to 5 parallel paths are sufficient to achieve close to optimal performance. The performance of the competitive algorithms are also compared to a greedy benchmark, both through analysis and simulation.