Christian Grimme

Prospects of collaboration between compute providers by means of job interchange

This paper empirically explores the advantages of the collaboration between different parallel compute sites in a decentralized grid scenario. To this end, we assume independent users that submit their jobs to their local site installation. The sites are allowed to decline the local execution of jobs by offering them to a central job pool. In our analysis we evaluate the performance of three job sharing algorithms that are based on the commonly used algorithms First-Come-First-Serve, EASY Backfilling, and List-Scheduling. The simulation results are obtained using real workload traces and compared to single site results. We show that simple job pooling is beneficial for all sites even if the local scheduling systems remain unchanged. Further, we show that it is possible to achieve shorter response times for jobs compared to the best single-site scheduling results.

Inside a predator-prey model for multi-objective optimization: a second study

In this article, new variation operators for evolutionary multi-objective algorithms (EMOA) are proposed. On the basis of a predator-prey model theoretical considerations as well as empirical results lead to the development of a new recombination operator, which improves the approximation of the set of efficient solutions significantly. Furtheron, it is shown that applying speciation to the analysed model makes it possible to handle even more complex problems.

Robust Load Delegation in Service Grid Environments

In this paper, we address the problem of finding well-performing workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a noninvasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC) centers and which leaves the local resource managers completely untouched. In this environment of fully autonomous sites, independent users are assumed to submit their jobs to the Grid middleware layer of their local site, which in turn decides on the delegation and execution either on the local system or on remote sites in a situation-dependent, adaptive way. We find for different scenarios that the exchange policies show good performance characteristics not only with respect to traditional metrics such as average weighted response time and utilization, but also in terms of robustness and stability in changing environments.

Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results

One main task in evolutionary multiobjective optimization (EMO) is to obtain a suitable finite size approximation of the Pareto front which is the image of the solution set, termed the Pareto set, of a given multiobjective optimization problem. In the technical literature, the characteristic of the desired approximation is commonly expressed by closeness to the Pareto front and a sufficient spread of the solutions obtained. In this paper, we first make an effort to show by theoretical and empirical findings that the recently proposed Averaged Hausdorff (or

Designing multi-objective variation operators using a predator-prey approach

\Delta _p

Discovering performance bounds for grid scheduling by using evolutionary multiobjective optimization

Δp-) indicator indeed aims at fulfilling both performance criteria for bi-objective optimization problems. In the second part of this paper, standard EMO algorithms combined with a specialized archiver and a postprocessing step based on the

Generalizing the data management of three community grids

\Delta _p