Grid Configuration and Application Monitoring in GridGain Florin Pop, Maria-Alexandra Lovin, Catalin Negru, Valentin Cristea University POLITEHNICA of Bucharest, Romania Faculty of Automatic Control and Computers Emails: florin.pop@cs.pub.ro, alexandra.lovin@cti.pub.ro, catalin.negru@cs.pub.ro, valentin.cristea@cs.pub.ro Nik Bessis, Stelios Sotiriadis School of Computing & Maths, University of Derby, Derby, United Kingdom Bucharest, Romania Emails: n.bessis@derby.ac.uk, s.sotiriadis@derby.ac.uk Abstract—Monitoring large scale distributed systems such as Grids environments represents a means for obtaining a quantitative and qualitative measurement of performance by collecting information relevant to environment and applications running on the Grid. This paper proposes a solution for Grid monitoring on GridGain middleware platform using both indirect information gathered inside the platform and an addition tool based on MonALISA and ApMon. A useful feature would be to allow the monitoring system to present the operator with suggestions computed based on the history of monitored parameters for jobs with a longer execution time or based on some theoretical, model based assumptions that relate existing configuration to the values of some performance parameters. Keywords-Distributed System, Monitoring, GridGain, Mon- ALISA, Scheduling Service I. I NTRODUCTION Grid monitoring solutions focus on different directions of monitoring: on configuration monitoring of the entire structure of the Grid or per Grid node, on monitoring applications running on the Grid or on a combination of both configuration and applications performance monitoring. Although starting from version 3, GridGain platform [1] becomes oriented more towards cloud development, Grid middleware remains an important feature of the platform and thus the need for monitoring in order to evaluate and enhance the performance of applications running and of resources usage. The solution for GridGain follows a combined ap- proach for both configuration and applications monitoring, by gathering a large number of parameters of both types. As previously proven on the existing monitoring solutions for GridGain, information extracted from Grid metrics and configuration available with GridGain API, GridGain SPIs and other monitoring tools. Also, the analysis of the existing Grid monitoring solutions indicates MonaLISA [2] and the set of available tools as the solution that can be easily integrated with GridGain. The solution is designed for applications running on a Linux/Unix environment and uses the Java implementation part from MonALISA. Monitoring information is sent to a central location from where it can be accessed by requesters and further viewed in a separate module. According to the taxonomy introduced in [3], the solution belongs to the category of third level of monitoring tools, as a hierarchy of publishers system, based on the provision characteristics, complexity of components defined in Grid Monitoring Ar- chitecture (GMA). The remainder of this paper is organized as follows: Sec- tion 2 presents the main issues regarding Grid monitoring; Section 3 details the proposed solution at architectural level and presents the technologies used and the way the different modules are combined, with a small portion dedicated to changes at monitoring level starting from version 3 of GridGain; Section 4 focuses on the data visualization and management layer of the solution, exploring the possibility of analyzing the gathering information in order to make configuration arrangement proposals; Section 5 tries to eval- uate the solution based on the parameters gathered referring to characteristics such as number, accuracy and relevance; Sections 6 and 7 contain a number of possible development and improvement directions, while Section 8 presents the conclusions of this paper. II. GRID MONITORING.STATE OF THE ART AND SOLUTIONS A traditional Grid monitoring process consists of four stages [2] [21]. In the first phase, generated events lead to the acquiring of measurements specific to the environment and monitored application. The next phase involves process- ing acquired data according to different criteria or groups of events, followed by a distribution of the information to requesters. Presentation is often organized in a GUI application by using a stream of events or recorded trace data stored as archive or database, a process known as consumption. The data presented involves further processing to extract conclusions. Grid monitoring systems need to follow a certain schema for data representation and ongoing research tries to establish a standard for the schema and structure of description. Monitoring information or state- ments are described by a time-stamp, indicating the moment of time when the information was taken of the measurement statement was true. 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems 978-0-7695-4808-1/12 $26.00 © 2012 IEEE DOI 10.1109/iNCoS.2012.82 155