No abstract

Distributed computing is a very broad and active research area comprising fields such as cluster computing, computational grids, desktop grids and peer-to-peer (P2P) systems. Unfortunately, it is often impossible to obtain theoretical or analytical results to compare the performance of algorithms targeting such systems. One possibility is to conduct large numbers of back-to-back experiments on real platforms. While this is possible on tightlycoupled platforms, it is infeasible on modern distributed platforms as experiments are labor-intensive and results typically not reproducible. Consequently, one must resort to simulations, which enable reproducible results and also make it possible to explore wide ranges of platform and application scenarios. In this paper we describe the SimGrid framework, a simulation-based framework for evaluating cluster, grid and P2P algorithms and heuristics. This paper focuses on SimGrid v3, which greatly improves on previous versions thanks to a novel and validated modular simulation engine that achieves higher simulation speed without hindering simulation accuracy. Also, two new user interfaces were added to broaden the targeted research community. After surveying existing tools and methodologies we describe the key features and benefits of SimGrid.

The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully repeatable and configurable experiments for arbitrary hypothetical scenarios. Two key concerns are accuracy (so that simulation results are scientifically sound) and scalability (so that simulation experiments can be fast and memory-efficient). While the scalability of a simulator is easily measured, the accuracy of many state-of-the-art simulators is largely unknown because they have not been sufficiently validated. In this work we describe recent accuracy and scalability advances made in the context of the SimGrid simulation framework. A design goal of SimGrid is that it should be versatile, i.e., applicable across all aforementioned domains. We present quantitative results that show that SimGrid compares favorably to state-of-the-art domain-specific simulators in terms of scalability, accuracy, or the trade-off between the two. An important implication is that, contrary to popular wisdom, striving for versatility in a simulator is not an impediment but instead is conducive to improving both accuracy and scalability.