OKA Predict takes your cluster usage optimization to the next level: allowing for real-time optimization of end-users’ jobs’ submissions. With OKA Predict, your team can build powerful Machine Learning predictors that will suggest or automatically apply the optimal job parameters to minimize resource use and time-to-results for end-users.
OKA Predict is a machine-learning tool to forecast jobs performance, costs and energy consumption. By integrating your job scheduler or submission portal with OKA Predict, you can:
- Improve Cluster productivity.
- Reduce waste of resources.
- Help End Users to get results Faster.
OKA Predict trains periodically on new data collected from the job scheduler or from additional logs. Its filtering functionalities make it possible to define workloads of interest to build specific predictors, and thus learn more precisely about the most frequent or most important cases. The submission parameters that OKA Predict includes as standard are the execution time and the RAM required for the job per node.
OKA Predict works in 3 phases, the training phase allows to build predictors, the prediction phase offers users parameters to specify for their jobs on the maximum execution time or on the memory required, or simply give feedback to users about their waiting or rendering time for tasks.
OKA Predict forecasts the following jobs’ characteristics at submission time:
- State – detects the risk of a job failing or finishing in timeout.
- Execution Time – predicts the execution time of jobs to plan resources and get results faster.
- Memory – predicts how much memory should be requested.
- Waiting Time & Time to Result – get feedback on when your jobs will end.
- Energy – estimate the impact on the environment.