Grid Matrix: a grid simulation tool to focus on the propagation of resource and monitoring information

Abstract

Grid Computing proposes unlimited access to different computational resources in a transparent way. High performance execution in grid environments is virtually impossible without timely access to accurate and up-to-date information related to distributed resources and services. Due to inherent difficulty of testing the different information propagation policies in real grid infrastructures, several simulation frameworks arose to help in this issue. In this work, we present Grid Matrix, an extension to one of the most used grid simulation tools (SimGrid2) to focus on the propagation of monitoring and resource information allowing the creation of virtual grid infrastructures. This extension enables GUI editing of network topology and provides the feature of scripting to define simulation details based on the newly developed C++ and Python bindings of SimGrid2 API. As a case study, Grid Matrix was used to test four different policies: hierarchical, super-peer, best-neighbor and random. The simulated scenario consisted of 96 master nodes based on the real Teragrid infrastructure as was publicly available at the time of writing this paper. We introduce three metrics that capture and summarize the information propagation behavior: LIR, GIR and GIV. LIR captures the local behavior quantifying the amount of up-to-date information in each node. GIR evaluates the global information state in the whole network averaging the LIR values, while GIV measures the variability of LIR. In the presented case, the best results in terms of the proposed metrics were attained by the hierarchical policy, followed by super-peer which outperformed random and best-neighbor. The modern and modular design of the scripting features included in Grid Matrix, in close conjunction with the user friendly GUI happened to be a very powerful tool for the evaluation of new propagation policies of resource information.

Using distributed local information to improve global performance in Grids

“…This function involves an implicit relation between their weighting coefficients (see Materials and Methods for details). The selection of each weight in this relation leads to focusing on some of the aspect of the system, in this work a standard set of parameters were used following previous works [19,20,14]. Using the standard scoring function, the scoring function may select a node that do not have much proper information and instead has lots of data about its neighbors.…”

“…Based on these results, in Mocskos et al [14] some good initial results of this policy were shown. Best-neighbor get good performance and, mainly, the overall information known by the system increases with time.…”

“…To evaluate the different scenarios and policies, we used GridMatrix 1 , an open source tool focused on the analysis of discovery and monitoring information policies, based on SimGrid2 [16]. This simulator includes three different metrics [14] for the study of information propagation, described below:…”

“…All theses topologies and scenarios were generated by the included features in the GridMatrix simulator. The standard best-neighbor implementation (BN) ranks the nodes with the following scoring function [14]:…”

“…In Mocskos et al [14] the authors introduced a new set of metrics (LIR, GIR and GIV) that incorporate the notion of time decay of information for evaluating system performance. The best results in terms of the proposed metrics were attained by the hierarchical policy, followed by super-peer which outperformed random and best-neighbor.…”