Petra Berenbrink

Not all scale free networks are Born equal: the role of the seed graph in PPI network emulation

The (asymptotic) degree distributions of the best-known “scale-free” network models are all similar and are independent of the seed graph used; hence, it has been tempting to assume that networks generated by these models are generally similar. In this paper, we observe that several key topological features of such networks depend heavily on the specific model and the seed graph used. Furthermore, we show that starting with the “right” seed graph (typically a dense subgraph of the protein–protein interaction network analyzed), the duplication model captures many topological features of publicly available protein–protein interaction networks very well.

Simple routing strategies for adversarial systems

In this paper we consider the problem of delivering dynamically changing input streams in dynamically changing networks where both the topology and the input streams can change in an unpredictable way. In particular, we present two simple distributed balancing algorithms (one for packet injections and one for flow injections) and show that for the case of a single receiver these algorithms will always ensure that the number of packets or flow in the system is bounded at any time step, even for an injection process that completely saturates the capacities of the available edges and even if the network topology changes in a completely unpredictable way. We also show that the maximum number of packets or flow that can be in the system at any time is essentially best possible by providing a lower bound that holds for any online algorithm, whether distributed or not. Interestingly, our balancing algorithms do not behave well in a completely adversarial setting. We show that also in the other extreme of a static network and a static injection pattern the algorithms will converge to a point in which they achieve an average routing time that is close to the best possible average routing time that can be achieved by any strategy. This demonstrates that there are simple algorithms that can be efficient for very different scenarios.

Balanced Allocations: The Heavily Loaded Case

We investigate balls-into-bins processes allocating m balls into n bins based on the multiple-choice paradigm. In the classical single-choice variant each ball is placed into a bin selected uniformly at random. In a multiple-choice process each ball can be placed into one out of

The Natural Work-Stealing Algorithm is Stable

d \ge 2

The natural work-stealing algorithm is stable

randomly selected bins. It is known that in many scenarios having more than one choice for each ball can improve the load balance significantly. Formal analyses of this phenomenon prior to this work considered mostly the lightly loaded case, that is, when

Convergence to Equilibria in Distributed, Selfish Reallocation Processes with Weighted Tasks

m \approx n

SIMLAB-a simulation environment for storage area networks

. In this paper we present the first tight analysis in the heavily loaded case, that is, when

Parallel rotor walks on finite graphs and applications in discrete load balancing

m \gg n

Distributed selfish load balancing with weights and speeds

rather than

Balls-into-bins with nearly optimal load distribution

m \approx n