PPT-Efficient Coflow Scheduling with Varys
Author : alexa-scheidler | Published Date : 2018-09-30
Mosharaf Chowdhury Yuan Zong Ion Stoica Presenters ChiFan Chu ChaoHan Tsai EECS 582 W16 1 Outline Background and motivation SEBF and MADD Varys overview Evaluation
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Efficient Coflow Scheduling with Varys: Transcript
Mosharaf Chowdhury Yuan Zong Ion Stoica Presenters ChiFan Chu ChaoHan Tsai EECS 582 W16 1 Outline Background and motivation SEBF and MADD Varys overview Evaluation Q amp A EECS 582 W16. . . Ellidiss. Technologies, France . University of Brest/UBO, Lab-STICC/UMR 6285, France. . 2. /18. Talk overview. Cheddar project : context and motivations . Research . Roadmap. . 3. /17. About scheduling analysis and its use . Main-Memory Workloads. Iraklis. . Psaroudakis. (EPFL). , Tobias Scheuer (SAP AG), Norman May (SAP AG), Anastasia Ailamaki (EPFL). 1. Scheduling for high concurrency. 2. Queries >> H/W contexts. Martha Garcia. Static Process scheduling. Goals of Static Process Scheduling. Types of Static Process Scheduling. Future Research. References. Static Process scheduling. GOALS :. Given a set or partially ordered tasks, define a mapping of processes to processors before the execution of the processes.. Manufacturing . Systems. By . Djamila. . Ouelhadj. and . Sanja. . Petrovic. Okan Dükkancı. 02.12.2013. Introduction. Dynamic . environments . with inevitable unpredictable real time events. ;. Machine failures . OVERVIEW. Room Scheduling System. (How to find a GPC and schedule it!). Review . Policies. Login to . Room Scheduling System. Learn . 3 . types of searches. Demonstration . Tips. Q&A. Agenda. Sara Cheatham • Stephanie Gonzales • Michael Lee . -Ashish Singh. Introduction. History and Background. Linux Scheduling. Modification in Linux Scheduling. Results. Conclusion. References. Questions. History and Background. In 1991 Linus Torvalds took a college computer science course that used the Minix operating system. CS 3100 CPU Scheduling. 1. Objectives. To introduce CPU scheduling, which is the basis for . multiprogrammed. operating systems. To describe various CPU-scheduling algorithms. To discuss evaluation criteria for selecting a CPU-scheduling algorithm for a particular system. Multi-core Processors. Dawei Li . and Jie . Wu. Department of Computer and Information Sciences. Temple University, Philadelphia, . USA. The 43. rd. International . C. onference on Parallel . P. rocessing. Chapter 16. Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.. You should be able to:. LO 16.1 Explain what scheduling involves and the importance of good scheduling. 1. 5.1 Basic Concepts. The . goal of multi-programming is to maximize the utilization of the CPU as a system resource by having a process running on it at all . times. Supporting multi-programming means encoding the ability in the O/S to switch between currently running jobs. By. Dr. Amin Danial Asham. References. Real-time Systems Theory and Practice. . By . Rajib. mall. Task Scheduling. Real-Time task scheduling essentially refers to determining the order in which the various tasks are to be taken up for execution by the operating system. Every operating system relies on one or more task schedulers to prepare the schedule of execution of various tasks it needs to run. Each task scheduler is characterized by the scheduling algorithm it employs. A large number of algorithms for scheduling real-Time tasks have so far been developed. Real-Time task scheduling on uniprocessors is a mature discipline now with most of the important results having been worked out in the early 1970's. The research results available at present in the literature are very extensive and it would indeed be grueling to study them exhaustively. In this text, we therefore classify the available scheduling algorithms into a few broad classes and study the characteristics of a few important ones in each class. . 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). Anirudh . Sivaraman. , . Suvinay. Subramanian, . Anurag. Agrawal, . Sharad. . Chole. , Shang-. Tse. Chuang, Tom . Edsall. , Mohammad . Alizadeh. , . Sachin. . Katti. , Nick . McKeown. , . Hari. .
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