Ludovic Beliveau

StEERING: A software-defined networking for inline service chaining

Network operators are faced with the challenge of deploying and managing middleboxes (also called inline services) such as firewalls within their broadband access, datacenter or enterprise networks. Due to the lack of available protocols to route traffic through middleboxes, operators still rely on error-prone and complex low-level configurations to coerce traffic through the desired set of middleboxes. Built upon the recent software-defined networking (SDN) architecture and OpenFlow protocol, this paper proposes StEERING, short for SDN inlinE sERvices and forwardlNG. It is a scalable framework for dynamically routing traffic through any sequence of middleboxes. With simple centralized configuration, StEERING can explicitly steer different types of flows through the desired set of middleboxes, scaling at the level of per-subscriber and per-application policies. With its capability to support flexible routing, we further propose an algorithm to select the best locations for placing services, such that the performance is optimized. Overall, StEERING allows network operators to monetize their middlebox deployment in new ways by allowing subscribers flexibly to select available network services.

Runtime Resource Allocation Model over Network Processors

Delivering high performance when several virtual nodes share the same physical resources requires finding the optimal resource allocation between them. In the context of Software Defined Network (SDN) and Network Virtualization, data plane requires the design of a new and more flexible flow packet processing. Virtual nodes involves several packet processing functions such as search operations in different data structures, processing the packets by modifying their respective contents and buffering them. Each packet processing requires a set of shared resources. If there is a conflict for a given resources, resource reassignment strategy is needed to ensure the continuity of the processing and solve resource congestion in accordance with the available hardware resources. In this paper, we propose a resource allocation strategy to share fairly the network processor resources. It is based on network calculus model and game theory algorithms. This strategy maps dynamically the suitable resources according to virtual nodes processing. In our implementation, we focus on packet processing tasks in regard to OpenFlow forwarding model within several processors to reassign resources.

Extensions to decision-tree based packet classification algorithms to address new classification paradigms

Abstract The decision-tree based packet-classification algorithm field has seen many contributions since the first algorithm using a geometrical rule representation, HiCuts, has been proposed. While hardware reported implementations for this class of algorithms have proven that a high throughput can be reached, those algorithms are inherently facing a tradeoff between speed and memory consumption. This paper presents two extensions applicable to decision-tree based algorithms designed to tackle two of their common drawbacks. Applied together, they achieve a reduction of the number of memory accesses, while reducing the data structure size. The first contribution consists of a new rule-clustering method aimed for the reduction of the number of trees built. The second contribution relies on a leaf compression method that allows tackling the problem that stems from linear leaf traversal. Applied together, as shown by simulations, those two new methods improve the trade-off between search-time complexity and data structure size. These strategies provide gains in many contexts, although they are tailored for handling complex rule sets used in the context of Software Defined Networking. For sets of 100,000 and 10,000 rules, those two strategies reduce the number of memory accesses by a factor of 3 on average, while decreasing the size of the data structure by about 45% over EffiCuts, a well-known decision-tree based algorithm.