International Business Machines Corporation

Stefania Costache

Parijat Dube

Zehra Noman Sura

Alaa Salah Youssef

Ali Kanso

Chen Wang

Diana Jeanne Arroyo

Priya Ashok Nagpurkar

Gauri Joshi

Malgorzata Steinder

Tonghoon Suk

Asser Narsreldin Tantawi

Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model. The estimated runtime can define a period of time beginning at an initiation of the machine learning task and ending at a completion of the machine learning task.

Runtime estimation for machine learning tasks

Systems, computer-implemented methods, and computer program products that can facilitate performance biased resource scheduling based on runtime performance of a certain workload type on one or more nodes are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a performance component that assigns performance points to different nodes based on execution of one or more workload types. The computer executable components can further comprise a scheduler extender component that modifies a scheduling decision to run a workload type on a node based on the performance points.

Performance biased resource scheduling based on runtime performance

Technology for selecting job characteristics to determine the similarity among jobs in terms of performance. Technology based on similarity among jobs calculated by selected characteristics for determining jobs that are likely to lead to successful performance of a requested new job by a cloud. Also, technology based on similarity among jobs calculated by selected characteristics for determining jobs that are likely to lead to failure when performing a requested new job by the cloud. When the new job request is accepted, because its characteristics of the new job matches job characteristics characterized by success and/or fails to match job characteristics characterized by failure, then the new job is said to lead to a 'reward' or an 'expected reward' because the new job will be rewarded by being allowed to use, by an admission controller of a cloud management system, use of cloud computing resources of the cloud.

Reward-based admission controller for resource requests in the cloud

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White Plains, NY, United States of America

Stefania Costache