OVIS is a modular system for HPC data collection, transport, storage, analysis, visualization, and response. The OVIS project seeks to enable more effective use of High Performance Computational Clusters via greater understanding of applications' use of resources, including the effects of competition for shared resources; discovery of abnormal system conditions; and intelligent response to conditions of interest.
Data Collection, Transport, and Storage
Lightweight Distributed Metric Service (LDMS) is the OVIS data collection and transport system. LDMS provides capabilities for lightweight run-time collection of high-fidelity data. Data can be accessed on-node or transported off node. Additionally, LDMS can store data in a variety of storage options.
Analysis and Visualization
OVIS includes 2 and 3D visual displays of deterministic information about state variables (e.g., temperature, CPU Utilization, fan speed), user-generated derived variables (e.g., aggregated memory errors over the life span of a job), and their aggregate statistics. Visual consideration of the cluster as a compartive ensemble, rather than singleton nodes, is a convenient and useful method for tuning cluster set-up and determining the effects of real-time changes in the cluster configuration and its environment.
OVIS includes a variety of statistical tools to dynamically infer models for the normal behavior of a system and to determine bounds on the probability of values evinced in the system. OVIS stores data in distributed database to provide scalability and fault tolerance. Statistical analyses are then performed in a distributed parallel fashion.
OVIS includes prototype capabilties for job log searching that can be used to search for events of interest. The OVIS interface has been designed to be highly interactive, where, for example, selection of a job of interest automatically populates an analysis pane with information relevant to that job, and dropping a job onto the 3D display highlights system values on only those components participating in the job.
Log Message Analysis
OVIS includes prototype capabilities for log message searching. Additionally, OVIS analyses include the Baler tool for log message clustering.
The OVIS project includes research work in determining intelligent response to conditions of interest. This includes dynamic application (re-)mapping based upon application needs and resource state and invocation of resiliency responses upon discovery of potential pre-failure and/or abnormal conditions.