Abstract: Systems and methods are disclosed for scheduling jobs processed in a distributed fashion to realize unharnessed efficiencies latent in the characteristics of the jobs and distributed processing technologies. A job store may be communicatively coupled to a job analyzer. The job analyzer may be operable to read information characterizing a job to identify multiple data blocks to be processed during the job at multiple locations in a cluster of nodes. A scheduling module may use information about the multiple data blocks, their storage locations, their status with respect to being provisioned to processing logic, data blocks to be processed by other jobs, data blocks in cache that have been pre-fetched for a prior job, quality-of-services parameters, and/or job characteristics, such as job size, to schedule the job in relation to other jobs.

Abstract: Systems and methods are disclosed for reducing latency in processing data sets in a distributed fashion. A job-queue operable for queuing data-processing jobs run on multiple nodes in a cluster may be communicatively coupled to a job analyzer. The job analyzer may be operable to read the data-processing jobs and extract information characterizing those jobs in ways that facilitate identification of resources in the cluster serviceable to run the data-processing jobs and/or data to be processed during the running of those jobs. The job analyzer may also be coupled to a resource warmer operable to warm-up a portion of the cluster to be used to run a particular data-processing job prior to the running of the job. In some embodiments, mappers and/or reducers may be extracted from the jobs and converted into compute node identifiers and/or data units identifying blocks for processing, informing the warm-up operations of the resource warmer.

Abstract: Systems and methods are disclosed for reducing latency in processing data sets in a distributed fashion. A job-queue operable for queuing data-processing jobs run on multiple nodes in a cluster may be communicatively coupled to a job analyzer. The job analyzer may be operable to read the data-processing jobs and extract information characterizing those jobs in ways that facilitate identification of resources in the cluster serviceable to run the data-processing jobs and/or data to be processed during the running of those jobs. The job analyzer may also be coupled to a resource warmer operable to warm-up a portion of the cluster to be used to run a particular data-processing job prior to the running of the job. In some embodiments, mappers and/or reducers may be extracted from the jobs and converted into compute node identifiers and/or data units identifying blocks for processing, informing the warm-up operations of the resource warmer.

Abstract: Systems and methods are disclosed for scheduling jobs processed in a distributed fashion to realize unharnessed efficiencies latent in the characteristics of the jobs and distributed processing technologies. A job store may be communicatively coupled to a job analyzer. The job analyzer may be operable to read information characterizing a job to identify multiple data blocks to be processed during the job at multiple locations in a cluster of nodes. A scheduling module may use information about the multiple data blocks, their storage locations, their status with respect to being provisioned to processing logic, data blocks to be processed by other jobs, data blocks in cache that have been pre-fetched for a prior job, quality-of-services parameters, and/or job characteristics, such as job size, to schedule the job in relation to other jobs.