SAS: Speculative Locality Aware Scheduling for I/O Intensive Analysis in Clouds

The execution of data intensive analysis workflows in a multi-cloud environment, such as World Large hadron collider Computing Grid (WLCG) at CERN, requires a large amount of input data, which is stored in multiple storage elements. The turnaround time taken by an individual analysis workflow is mostly affected by the data reading time. Minimizing the data reading time can improve the overall efficiency of data analysis process. To overcome this problem, we have used Speculative Scheduling to optimize the multi-cloud analysis workflows by intelligently streaming data before a task arrives for execution. We propose an Event Server (ES) which is an in-memory process responsible for proactively providing input data to the workflow processes. It prefetches the data from the storage elements to the memory of the worker node, which executes the workflow. Using locality aware scheduling and prefetching algorithms, it performs Speculative Scheduling on the basis of the evaluation of historic execution logs. ES learns about the incoming jobs ahead of time and makes use of the intelligent data streaming to supply data to these jobs, thus reducing the overall scheduling and data access latencies and leading to significant improvements in the overall turnaround time. We have evaluated the proposed system using a large analysis workflow from High Energy Physics (HEP) by emulating the WLCG infrastructure in a controlled environment. The results have shown that by using speculative and locality aware scheduling techniques, significant improvements (i.e. over 30%) can be achieved in the execution of data intensive workflows in a multi-cloud environment.