Scalable co-scheduling strategies in distributed computing

In this paper, we present an approach to scalable co-scheduling in distributed computing for complex sets of interrelated tasks (jobs). The scalability means that schedules are formed for job models with various levels of task granularity, data replication policies, and the processor resource and memory can be upgraded. The necessity of guaranteed job execution at the required quality of service causes taking into account the distributed environment dynamics, namely, changes in the number of jobs for servicing, volumes of computations, possible failures of processor nodes, etc. As a consequence, in the general case, a set of versions of scheduling, or a strategy, is required instead of a single version. We propose a scalable model of scheduling based on multicriteria strategies. The choice of the specific schedule depends on the load level of the resource dynamics and is formed as a resource query which is sent to a local batch-job management system.