Hardness studies for distributed systems

Patrick De CausmaeckerStefaan Haspeslagh

Tommy Messelis

P. De Causmaecker, S. Haspeslagh, T. Messelis 2

What is empirical hardness?empirical: performance of some algorithmhardness: measured by some performance criteria Time spent by an algorithm searching for a solution Quality of (optimal) solution Gap between optimal and found solution

Prediction based on efficiently computable features of the instance at hand

E.g. clauses-to-variables ratio of uniform random 3SAT problems

Empirical Hardness Indicators

P. De Causmaecker, S. Haspeslagh, T. Messelis 3

Build models that predict the empirical hardness of an instance◦ Know in advance what to expect from a given

instance◦ Algorithm portfolios

Because there is no single best algorithm for all instances of a given distribution

◦ Automated parameter tuning

General procedure first introduced by Leyton-Brown et al.

K. Leyton-Brown, E. Nudelman, Y. Shoham. Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions. In: Lecture Notes in Computer Science, 2002.

Key idea

P. De Causmaecker, S. Haspeslagh, T. Messelis 4

1. Select a problem instance distribution2. Define a set of algorithms3. Come up with a set of inexpensive,

distribution independent features4. Generate an instance set. Calculate

features and determine algorithm performances

5. Eliminate correlated & uninformative features

6. Use machine learning techniques to select a function of the features that predicts algoritm performance for all algoritms

“Leyton-Brown procedure”

P. De Causmaecker, S. Haspeslagh, T. Messelis 5

Yes it does!

Winner determination problem for combinatorial auctions.

Propositional Satisfiability Problems◦ SATzilla

Our new approach: Nurse Rostering◦ Still on its way

Does it work?

P. De Causmaecker, S. Haspeslagh, T. Messelis 6

Dicomas:◦ 2 application domains:

• Supply Chain Management• eHealth

• Similar hardness studies needed

Hardness studies for DICOMAS

P. De Causmaecker, S. Haspeslagh, T. Messelis 7

Supply Chain Management

4PL

K1 K2 K3 Kx

Fixed contract

3PL

Temporary3PL

P. De Causmaecker, S. Haspeslagh, T. Messelis 8

Optimisation:◦Costs: Fixed contracts

Capacity Temporary contracts

Optimisation

4PL

K1 K2 K3 Kx

Fixed contract

3PL

Temporary3PL

P. De Causmaecker, S. Haspeslagh, T. Messelis 9

Input from other partners:◦ Solution methods to tackle SCM/eHealth problem

Question:◦ What is the performance of these methods

How?◦ Simulation◦ Determine properties of the problem (instances)

for a certain method See our work for non distributed methods

Research questions

P. De Causmaecker, S. Haspeslagh, T. Messelis 10

Online monitoring:◦ research for parameters that can identify and

might predict problem situations and/or critical points E.g. bullwhip effect

Research questions