PDF-Cooperative Resource Scheduling

xx List of Acronyms CRS TOCRS Trust Oriented Cooperative Resource Scheduling TORS Trust Oriented Resource Scheduling CCS Cooperative Computing System CCSCB Cooperative Computing System with Customize. . Mutli-core. . Scheduling. Moris Behnam. Introduction. Single processor scheduling. E.g., t. 1. (P=10,C=5), t. 2. (10, 6) . U=0.5+0.6>1. Use a faster processor. Thermal and power problems impose limits . Manufacturing . Systems. By . Djamila. . Ouelhadj. and . Sanja. . Petrovic. Okan Dükkancı. 02.12.2013. Introduction. Dynamic . environments . with inevitable unpredictable real time events. ;. Machine failures . Reference:. Mesos. : A Platform for Fine-Grained Resource Sharing in the Data Center NSDI’2011. Multi-agent Cluster Scheduling for Scalability and. Flexibility. . Berkerly. . techdoc. EECS-2012-273. (doctoral dissertation). Deterministic Proportional-Share Resource Management. Carl A. . Waldspurger. , William E. . Weihl. MIT Laboratory for Computer Science. Presenter: Dong-hyeon Park. EECS 582 – W16. 1. Background: Proportional-Share Schedulers. Data-Intensive Systems. Bernie . Acs. , . Magda. . Balazinska. , John Ford, . Karthik. . Kambatla. , Alex . Labrinidis. , Carlos . Maltzahn. , . Rami. . Melhem. , Paul . Nowoczynski. , Matthew . Woitaszek. Dr. Ahmed Elyamany. Outline. Definition of Resources. Resource Aggregation/Loading. Problems Associated with Resource. Resource Leveling. Resource Scheduling. What a resource?. Any thing that is used by an activity to get the work done, such as: Material, Equipment, Labor, Money, …... Burst Buffer Enabled HPC Clusters. Chunxiao. Liao. 1. Background. High performance storage is critical to achieving computational efficiency on high performance computing (HPC) systems. . Capacity growth of disks continues to outpace increases in their bandwidth. Robert . Grandl. , University of Wisconsin—Madison; . Mosharaf. Chowdhury, University of Michigan; Aditya . Akella. , University of Wisconsin—Madison; Ganesh . Ananthanarayanan. , Microsoft. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16). Implementation. Designating/Terminating Fleet vs Unit. Scheduling Rights. Tagging with NITS on OASIS . Implementation Timeline. Questions. Agenda. 2. Designate/Terminate Fleet vs Unit. 3. Must specify a Gen Group. performance to your Microsoft Dynamics NAV. Who is Ortems?. Your next visual planning provider. Enhance . your Microsoft Dynamics . NAV. Discover PlannerOne. Wrap Up . Q&A. Agenda. 1. 2. 3. 4. Add planning and scheduling performance. Robert Grandl, Mosharaf Chowdhury, . Aditya Akella, Ganesh Ananthanarayanan. Carbyne. Performance of Cluster Schedulers. We observe that:. Existing cluster schedulers focus on. . instantaneous. . fairness. for High-End CPU-GPU Architectures. Vignesh. Ravi. Dept. of Computer Science and Engineering. Advisor: . Gagan. . Agrawal. 1. The Death of Single-core CPU Scaling. 2. The Landscape of Computing – Moore’s Law. Problem. What is it?. Implementation. Benefits. Experimentation. Findings. Other Scheduling Algorithms. Conclusion. . Outline. Problem. “Scheduling computations in multi-threaded systems is complex, and challenging problem.”. , 2017. Critical properties of Apollo. Distributed and coordinated scheduling framework. Assign tasks to server with minimal estimated completion time. Provide near-future states of servers. Correction mechanism.