Jiří Sitera

Practical approaches to Grid workload and resource management in the EGEE project

Resource management and scheduling of distributed, data-driven applications in a Grid environment are challenging problems. Although significant results were achieved in the past few years, the development and the proper deployment of generic, reliable, standard components present issues that still need to be completely solved. Interested domains include workload management, resource discovery, resource matchmaking and brokering, accounting, authorization policies, resource access, reliability and dependability. The evolution towards a service-oriented architecture, supported by emerging standards, is another activity that will demand attention. All these issues are being tackled within the EU-funded EGEE project (Enabling Grids for E-science in Europe), whose primary goals are the provision of robust middleware components and the creation of a reliable and dependable Grid infrastructure to support e-Science applications. In this paper we present the plans and the preliminary activities aiming at providing adequate workload and resource management components, suitable to be deployed in a production-quality Grid.

The DataGrid Workload Management System: Challenges and Results

The workload management task of the DataGrid project was mandated to define and implement a suitable architecture for distributed scheduling and resource management in a Grid environment. The result was the design and implementation of a Grid Workload Management System, a super-scheduler with the distinguishing property of being able to take data access requirements into account when scheduling jobs to the available Grid resources. Many novel issues in various fields were faced such as resource management, resource reservation and co-allocation, Grid accounting. In this paper, the architecture and the functionality provided by the DataGrid Workload Management System are presented.

gLite job provenance

The Job Provenance (JP) service is designed to automate keeping track of computations on large scale Grids, giving thus users a tool to correctly archive information about their jobs and to re-submit any job in a reconstructed environment. JP provides a permanent minimal record of job (and its environment) related information, to which free-form user annotations can be added. JP also offers the capability of configuring any number of indexed logical views on the large collections of raw data, allowing efficient processing of even complex user queries selecting on both system data and the annotations. The scalable architecture, capable to handle millions of jobs in a single JP installation, and integrated into the EGEE gLite middleware environment is presented.

Distributed Tracking, Storage, and Re-use of Job State Information on the Grid

The Logging and Bookkeeping service tracks jobs passing through the Grid. It collects important events generated by both the grid middleware components and applications, and processes them at a chosen LB server to provide the job state. The events are transported through secure and reliable channels. Job tracking is fully distributed and does not depend on a single information source, the robustness is achieved through speculative job state computation in case of reordered, delayed or lost events. The state computation is easily adaptable to modified job control flow.

Real Time Monitor of Grid job executions

In this paper we describe the architecture and operation of the Real Time Monitor (RTM), developed by the Grid team in the HEP group at Imperial College London. This is arguably the most popular dissemination tool within the EGEE [1] Grid. Having been used, on many occasions including GridFest and LHC inauguration events held at CERN in October 2008. The RTM gathers information from EGEE sites hosting Logging and Bookkeeping (LB) services. Information is cached locally at a dedicated server at Imperial College London and made available for clients to use in near real time. The system consists of three main components: the RTM server, enquirer and an apache Web Server which is queried by clients. The RTM server queries the LB servers at fixed time intervals, collecting job related information and storing this in a local database. Job related data includes not only job state (i.e. Scheduled, Waiting, Running or Done) along with timing information but also other attributes such as Virtual Organization and Computing Element (CE) queue – if known. The job data stored in the RTM database is read by the enquirer every minute and converted to an XML format which is stored on a Web Server. This decouples the RTM server database from the client removing the bottleneck problem caused by many clients simultaneously accessing the database. This information can be visualized through either a 2D or 3D Java based client with live job data either being overlaid on to a 2 dimensional map of the world or rendered in 3 dimensions over a globe map using OpenGL.

Experimental evaluation of job provenance in ATLAS environment

Grid middleware stacks, including gLite, matured into the state of being able to process up to millions of jobs per day. Logging and Bookkeeping, the gLite job-tracking service, keeps pace with this rate; however, it is not designed to provide a long-term archive of information on executed jobs. ATLAS — representative of a large user community — addresses this issue with its own job catalogue (ProdDB). Development of such a customized service, not easily reusable, took considerable effort which is not affordable by smaller communities. On the contrary, Job Provenance (JP), a generic gLite service designed for long-term archiving of information on executed jobs focusing on scalability, extensibility, uniform data view, and configurability, allows more specialized catalogues to be easily built. We present the first results of an experimental JP deployment for the ATLAS production infrastructure where a JP installation was fed with a part of ATLAS jobs, and also stress tested with real production data. The main outcome of this work is a demonstration that JP can complement large-scale application-specific job catalogue services, while serving a similar purpose where there are none available.

Multiple Ligand Trajectory Docking Study - Semiautomatic Analysis of Molecular Dynamics Simulations using EGEE gLite Services

Interactions between large biomolecules and smaller bio-active ligands are usually studied through a process called docking. Its aim is to find an energetically favorable orientation of a ligand within an active site of a biomolecule. Chemical reactions take place in active site and the role of the ligand is either to speed up, slow down or change the reaction (e.g., an enzyme catalyzed hydrolysis), which is why it can have huge pharmaceutical or other commercial impact. We present a tool that supports effective management and control of a typical workflow of docking parametric study. Selected subsets of ligands and protein trajectory snapshots can be displayed in three different views and further analyzed. Finally, the application supports spawning and steering underlying computations running on the grid.

Job Provenance --- Insight into Very Large Provenance Datasets

Following the job-centric monitoring concept, Job Provenance (JP) service organizes provenance records on the per-job basis. It is designed to manage very large number of records, as was required in the EGEE project where it was developed originally. The quantitative aspect is also a focus of the presented demonstration. We show JP capability to retrieve data items of interest from a large dataset of full records of more than 1 million of jobs, to perform non-trivial transformation on those data, and organize the results in such a way that repeated interactive queries are possible. The application area of the demo is derived from that of previous Provenance Challenges. Though the topic of the demo -- a computational experiment -- is arranged rather artificially, the demonstration still delivers its main message that JP supports non-trivial transformations and interactive queries on large data sets.

Supporting the Development Process of the DataGrid Workload Management System Software with GNU autotools, CVS and RPM

Supporting the development of the Workload Management System in the context of the European DataGrid was a challenging task as the team was characterized by a high geographic and administrative dispersion, with developers distributed in various institutions and countries. Further, software dependencies were complex as it was required to integrate and interface a significant number of external software packages. In this paper, we discuss how a combination of Concurrent Version System, GNU autotools and other tools and practices was organized to allow the development, build, test and distribution of the software. With the proposed solution, we managed to combine ease-of-use for distributed developers while preserving the central coordination needed by the project-wide steering.