Pierre Schaus

A Declarative and Expressive Approach to Control Forwarding Paths in Carrier-Grade Networks

SDN simplifies network management by relying on declarativity (high-level interface) and expressiveness (network flexibility). We propose a solution to support those features while preserving high robustness and scalability as needed in carrier-grade networks. Our solution is based on (i) a two-layer architecture separating connectivity and optimization tasks; and (ii) a centralized optimizer called framework, which translates high-level goals expressed almost in natural language into compliant network configurations. Our evaluation on real and synthetic topologies shows that framework improves the state of the art by (i) achieving better trade-offs for classic goals covered by previous works, (ii) supporting a larger set of goals (refined traffic engineering and service chaining), and (iii) optimizing large ISP networks in few seconds. We also quantify the gains of our implementation, running Segment Routing on top of IS-IS, over possible alternatives (RSVP-TE and OpenFlow).

Scalable Load Balancing in Nurse to Patient Assignment Problems

This paper considers the daily assignment of newborn infant patients to nurses in a hospital. The objective is to balance the workload of the nurses, while satisfying a variety of side constraints. Prior work proposed a MIP model for this problem, which unfortunately did not scale to large instances and only approximated the objective function, since minimizing the variance cannot be expressed in a linear model. This paper presents constraint programming (CP) models of increasing complexity to solve large instances with hundreds of patients and nurses in a few seconds using the Comet optimization system. The CP models use the recent spread global constraint to minimize the variance, as well as an exact decomposition technique.

Bound-consistent deviation constraint

Deviation is a recent constraint to balance a set of variables with respect to a given mean. We show that the propagators recently introduced are not bound-consistent when the mean is rational. We introduce bound-consistent propagators running in linear time with respect to the number of variables. We evaluate the improvement in terms of efficiency and pruning obtained with the new propagators on the Balanced Academic Curriculum Problem.

The Deviation Constraint

This paper introduces DEVIATION , a soft global constraint to obtain balanced solutions. A violation measure of the perfect balance can be defined as the L p norm of the vector variables minus their mean. SPREAD constraints the sum of square deviations to the mean [5,7] i.e.the L 2 norm. The L 1 norm is considered here. Neither criterion subsumes the other but the design of a propagator for L 1 is simpler. We also show that a propagator for DEVIATION runs in

Conflict Ordering Search for Scheduling Problems

\mathcal{O}(n)

Understanding the Potential of Propagators

(with respect to the number of variables) against

Simple and Scalable Time-Table Filtering for the Cumulative Constraint

\mathcal{O}(n^2)

Cardinality reasoning for bin-packing constraint: application to a tank allocation problem

for SPREAD .

Bound-consistent spread constraint

We introduce a new generic scheme to guide backtrack search, called Conflict Ordering Search (COS), that reorders variables on the basis of conflicts that happen during search. Similarly to generalized Last Conflict (LC), our approach remembers the last variables on which search decisions failed. Importantly, the initial ordering behind COS is given by a specified variable ordering heuristic, but contrary to LC, once consumed, this first ordering is forgotten, which makes COS conflict-driven. Our preliminary experiments show that COS - although simple to implement and parameter-free - is competitive with specialized searches on scheduling problems. We also show that our approach fits well within a restart framework, and can be enhanced with a value ordering heuristic that selects in priority the last assigned values.

Time-Table Disjunctive Reasoning for the Cumulative Constraint

Propagation is at the very core of Constraint Programming (CP): it can provide significant performance boosts as long as the search space reduction is not outweighed by the cost for running the propagators. A lot of research effort in the CP community is directed toward improving this trade-off, which for a given type of filtering amounts to reducing the computation cost. This is done chiefly by 1) devising more efficient algorithms or by 2) using on-line control policies to limit the propagator activations. In both cases, obtaining improvements is a long and demanding process with uncertain outcome. We propose a method to assess the potential gain of both approaches before actually starting the endeavor, providing the community with a tool to best direct the research efforts. Our approach is based on instrumenting the constraint solver to collect statistics, and we rely on replaying search trees to obtain more realistic assessments. The overall approach is easy to setup and is showcased on the Energetic Reasoning (ER) and the Revisited Cardinality Reasoning for BinPacking (RCRB) propagators.