slides.021711 - Cloud Scheduling Presented by: Muntasir...

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Department of Computer  Science, UIUC Presented by: Muntasir Raihan Rahman and Anupam Das CS 525 Spring 2011 Advanced Distributed Systems Cloud Scheduling Cloud Scheduling 1
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Department of Computer  Science, UIUC Papers Presented Improving MapReduce Performance in Heterogeneous Environments @ OSDI 2008 Matei Zahari, Andy Konwinski, Anthony D. Joseph. Randy Katz, Ion Matei Zahari, Andy Konwinski, Anthony D. Joseph. Randy Katz, Ion Stoica @ UC Berkeley RAD Lab Stoica @ UC Berkeley RAD Lab Quincy: Fair Scheduling for Distributed Computing Clusters @ SOSP 2009 Michael Isard, Vijayan Prabhakaran, Jon Currey, Udi Wieder, Kunal Michael Isard, Vijayan Prabhakaran, Jon Currey, Udi Wieder, Kunal Talwar, Andrew Goldberg @ MSR Silicon Valley Talwar, Andrew Goldberg @ MSR Silicon Valley Reining in the Outliers in Map-Reduce Clusters using Mantri @ OSDI 2010 Ganesh Ananthanarayanan, Ion Stoica @ UC Berkeley RAD Lab, Ganesh Ananthanarayanan, Ion Stoica @ UC Berkeley RAD Lab, Srikanth Kandula, Albert Greenberg @ MSR, Yi Lu @ UIUC, Bikash Srikanth Kandula, Albert Greenberg @ MSR, Yi Lu @ UIUC, Bikash Saha, Edward Harris @ Microsoft Bing Saha, Edward Harris @ Microsoft Bing 2
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Department of Computer  Science, UIUC Quincy: Fair Scheduling for Distributed Computing Clusters [@ SOSP 2009] Michael Isard, Vijayan Prabhakaran, Jon Currey, Udi Wieder, Kunal Michael Isard, Vijayan Prabhakaran, Jon Currey, Udi Wieder, Kunal Talwar, and Andrew Goldberg @ MSR Silicon Valley Talwar, and Andrew Goldberg @ MSR Silicon Valley 3
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Motivation Fairness : Existing dryad scheduler unfair [ greedy approach]. Subsequent small jobs waiting for a large job to finish. Data Locality : HPC jobs fetch data from a SAN, no need for co- location of data and computation. Data intensive workloads have storage attached to computers. Scheduling tasks near data improves performance. 4 Department of Computer  Science, UIUC
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Fair Sharing Job X takes t seconds when it runs exclusively on a cluster . X should take no more than Jt seconds when cluster has J concurrent jobs. Formally, for N computers and J jobs, each job should get at-least N/J computers. 5 Department of Computer  Science, UIUC
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Quincy Assumptions Homogeneous clusters Heterogeneity is discussed in next paper [LATE scheduler] Global uniform cost measure E.g. Quincy assumes that the cost of preempting a running job can be expressed in the same units as the cost of data transfer. 6 Department of Computer  Science, UIUC
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Fine Grain Resource Sharing For MPI jobs, coarse grain scheduling Devote a fixed set of computers for a particular job Static allocation, rarely change the allocation For data intensive jobs (map-reduce, dryad) We need fine grain resource sharing multiplex all computers in the cluster between all jobs Large datasets attached to each computer Independent tasks (less costly to kill a task and restart) 7 Department of Computer  Science, UIUC
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Example of Coarse Grain Sharing Department of Computer  Science, UIUC 8
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Example of Fine Grain Sharing 9 Department of Computer  Science, UIUC
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This note was uploaded on 12/08/2011 for the course CS 525 taught by Professor Gupta during the Spring '08 term at University of Illinois, Urbana Champaign.

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slides.021711 - Cloud Scheduling Presented by: Muntasir...

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