Load sharing for multiprocessor network nodes

This thesis discusses techniques for sharing the processing load among multiple processing units within systems that act as nodes in a data communications network. Load-sharing techniques have been explored in the field of computer science for many years and their benefits are well known, including better utilization of processing capacity and enhanced system fault tolerance. We discuss deploying such methods in the specifics of the networking environment. We concentrate particularly on the data plane, or the data packet-processing tasks. After reviewing the main results in the fields of load sharing and multiprocessor networking systems architectures, we conduct a preparatory optimization study of a router system to gain better understanding of the optimization issues in a particular multiprocessor system. The main contribution of this thesis, the adaptive load-sharing method, is presented next. We first formulate the optimization problem of mapping packets to processors. The goal is to minimize the likelihood of flow reordering, while respecting certain system constraints, such as the acceptable probability of a packet loss. As we show that the task is an NP-complete problem, we propose a heuristic method that uses an adaptive hash-based mapping to assign packets to processors. We demonstrate its advantages and prove that the method adaptation policy possesses the key minimal disruption property with respect to the mapping. In other words, the adaptation results in a minimum number of flows being moved among processing units. Further on, the method is validated in an extensive set of simulations designed to imitate the networking environment. Finally, two sample applications, an architecture of a multiprotocol router and an implementation of a server load balancer on a network processor demonstrate the applicability of the method.

Le Boudec, Jean Yves
Lausanne, EPFL
Other identifiers:
urn: urn:nbn:ch:bel-epfl-thesis2725-8

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 Record created 2005-03-16, last modified 2020-10-25

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