SimGrid: A Generic Framework for Large-Scale Distributed Experiments

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.

DynamicCloudSim: Simulating heterogeneity in computational clouds

“…See Table 2 for a comparison of features available in CloudSim, WorkflowSim, and DynamicCloudSim. Donassolo et al [42] altered the SimGrid framework [43], another popular grid simulator besides GridSim [26] to increase the scalability of simulation runs. These improvements allow SimGrid to simulate the computation of workloads on tens or hundreds of thousands of heterogeneous and possibly volatile machines as encountered in volunteer computing.…”

A survey on formal active and passive testing with applications to the cloud

“…Researchers have designed cloud models and then performed adhoc testing by manually simulating different scenarios and comparing obtained results. Among the available simulation tools that can be used to model and simulate cloud computing environments are CloudSim [12], GreenCloud [38], SimGrid [14], Virtual-GEMS [26] and iCanCloud [61].…”

Setting up Clusters of Computing Units to Process Several Data Streams Efficiently

“…All the simulations, which results are presented in this section, have been conducted with the SimGrid framework [19]. We consider 10 sets (S k ) 0≤k≤9 of values for the execution parameters of the workers, given (speeds in bytes/second and latencies in seconds) in Table 1.…”