mesos
TRANSCRIPT
MotivationMesos
ImplementationEvaluationConclusion
Mesos: A Platform for Fine-Grained ResourceSharing in the Data Center
Muhammad Anis uddin Nasir
KTH Royal Institute of Technology
November 20, 2012
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MotivationMesos
ImplementationEvaluationConclusion
1 Motivation
2 MesosOverviewArchitecture
3 Implementation
4 Evaluation
5 Conclusion
Muhammad Anis uddin Nasir Mesos 2/32
MotivationMesos
ImplementationEvaluationConclusion
Motivation
Diverse cluster computer framework
Muhammad Anis uddin Nasir Mesos 3/32
MotivationMesos
ImplementationEvaluationConclusion
Motivation
No optimal framework
Run multiple frameworks
Higher UtilizationData sharing betweenclustersReduce Cost
Muhammad Anis uddin Nasir Mesos 4/32
MotivationMesos
ImplementationEvaluationConclusion
Motivation
No optimal framework
Run multiple frameworks
Higher UtilizationData sharing betweenclustersReduce Cost
Muhammad Anis uddin Nasir Mesos 4/32
MotivationMesos
ImplementationEvaluationConclusion
Motivation
No optimal framework
Run multiple frameworksHigher Utilization
Data sharing betweenclustersReduce Cost
Muhammad Anis uddin Nasir Mesos 4/32
MotivationMesos
ImplementationEvaluationConclusion
Motivation
No optimal framework
Run multiple frameworksHigher UtilizationData sharing betweenclusters
Reduce Cost
Muhammad Anis uddin Nasir Mesos 4/32
MotivationMesos
ImplementationEvaluationConclusion
Motivation
No optimal framework
Run multiple frameworksHigher UtilizationData sharing betweenclustersReduce Cost
Muhammad Anis uddin Nasir Mesos 4/32
MotivationMesos
ImplementationEvaluationConclusion
Existing Solutions
Static Partitioning
Virtual Machines
Muhammad Anis uddin Nasir Mesos 5/32
MotivationMesos
ImplementationEvaluationConclusion
Existing Solutions
Static Partitioning
Virtual Machines
Muhammad Anis uddin Nasir Mesos 5/32
MotivationMesos
ImplementationEvaluationConclusion
Fine-grained
Hadoop and Dryad
SlotsTasks
Benefits
Data LocalityUtilization
Muhammad Anis uddin Nasir Mesos 6/32
MotivationMesos
ImplementationEvaluationConclusion
Fine-grained
Hadoop and DryadSlots
Tasks
Benefits
Data LocalityUtilization
Muhammad Anis uddin Nasir Mesos 6/32
MotivationMesos
ImplementationEvaluationConclusion
Fine-grained
Hadoop and DryadSlotsTasks
Benefits
Data LocalityUtilization
Muhammad Anis uddin Nasir Mesos 6/32
MotivationMesos
ImplementationEvaluationConclusion
Fine-grained
Hadoop and DryadSlotsTasks
Benefits
Data LocalityUtilization
Muhammad Anis uddin Nasir Mesos 6/32
MotivationMesos
ImplementationEvaluationConclusion
Fine-grained
Hadoop and DryadSlotsTasks
BenefitsData Locality
Utilization
Muhammad Anis uddin Nasir Mesos 6/32
MotivationMesos
ImplementationEvaluationConclusion
Fine-grained
Hadoop and DryadSlotsTasks
BenefitsData LocalityUtilization
Muhammad Anis uddin Nasir Mesos 6/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
1 Motivation
2 MesosOverviewArchitecture
3 Implementation
4 Evaluation
5 Conclusion
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MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Mesos
Common resource sharing layer
Fine grained sharing
Across diverse cluster computing frameworks
Muhammad Anis uddin Nasir Mesos 8/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Goals
High Utilization
Data sharing among frameworks
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MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Goals
High Utilization
Data sharing among frameworks
Muhammad Anis uddin Nasir Mesos 9/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Challenges
Scalable
Support diverse frameworks
Efficient
Fault tolerant
Highly available
Muhammad Anis uddin Nasir Mesos 10/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Challenges
Scalable
Support diverse frameworks
Efficient
Fault tolerant
Highly available
Muhammad Anis uddin Nasir Mesos 10/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Challenges
Scalable
Support diverse frameworks
Efficient
Fault tolerant
Highly available
Muhammad Anis uddin Nasir Mesos 10/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Challenges
Scalable
Support diverse frameworks
Efficient
Fault tolerant
Highly available
Muhammad Anis uddin Nasir Mesos 10/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Challenges
Scalable
Support diverse frameworks
Efficient
Fault tolerant
Highly available
Muhammad Anis uddin Nasir Mesos 10/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Other Benefits
Run Multiple instances of same framework
Isolate production and experimental jobsRun multiple versions of a framework
Build special framework targeting particular problemdomain
Muhammad Anis uddin Nasir Mesos 11/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Other Benefits
Run Multiple instances of same frameworkIsolate production and experimental jobs
Run multiple versions of a framework
Build special framework targeting particular problemdomain
Muhammad Anis uddin Nasir Mesos 11/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Other Benefits
Run Multiple instances of same frameworkIsolate production and experimental jobsRun multiple versions of a framework
Build special framework targeting particular problemdomain
Muhammad Anis uddin Nasir Mesos 11/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Other Benefits
Run Multiple instances of same frameworkIsolate production and experimental jobsRun multiple versions of a framework
Build special framework targeting particular problemdomain
Muhammad Anis uddin Nasir Mesos 11/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Design Elements
Fine-grained sharing
Allocation at the level of tasks within a jobImprove utilization, latency and data locality
Resource offers
Simple and ScalableApplication-controlled scheduling mechanism
Muhammad Anis uddin Nasir Mesos 12/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Design Elements
Fine-grained sharingAllocation at the level of tasks within a job
Improve utilization, latency and data locality
Resource offers
Simple and ScalableApplication-controlled scheduling mechanism
Muhammad Anis uddin Nasir Mesos 12/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Design Elements
Fine-grained sharingAllocation at the level of tasks within a jobImprove utilization, latency and data locality
Resource offers
Simple and ScalableApplication-controlled scheduling mechanism
Muhammad Anis uddin Nasir Mesos 12/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Design Elements
Fine-grained sharingAllocation at the level of tasks within a jobImprove utilization, latency and data locality
Resource offers
Simple and ScalableApplication-controlled scheduling mechanism
Muhammad Anis uddin Nasir Mesos 12/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Design Elements
Fine-grained sharingAllocation at the level of tasks within a jobImprove utilization, latency and data locality
Resource offersSimple and Scalable
Application-controlled scheduling mechanism
Muhammad Anis uddin Nasir Mesos 12/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Design Elements
Fine-grained sharingAllocation at the level of tasks within a jobImprove utilization, latency and data locality
Resource offersSimple and ScalableApplication-controlled scheduling mechanism
Muhammad Anis uddin Nasir Mesos 12/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Fine-Grained Sharing
Muhammad Anis uddin Nasir Mesos 13/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Resource Offers
Offers resources to framework
Framework choose resources according to needs
Benefits
gives freedom to framework for implementationkeep Mesos simple and scalable
Drawback
decentralized decision might not be optimal
Muhammad Anis uddin Nasir Mesos 14/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Resource Offers
Offers resources to framework
Framework choose resources according to needs
Benefits
gives freedom to framework for implementationkeep Mesos simple and scalable
Drawback
decentralized decision might not be optimal
Muhammad Anis uddin Nasir Mesos 14/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Resource Offers
Offers resources to framework
Framework choose resources according to needs
Benefits
gives freedom to framework for implementationkeep Mesos simple and scalable
Drawback
decentralized decision might not be optimal
Muhammad Anis uddin Nasir Mesos 14/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Resource Offers
Offers resources to framework
Framework choose resources according to needs
Benefitsgives freedom to framework for implementation
keep Mesos simple and scalable
Drawback
decentralized decision might not be optimal
Muhammad Anis uddin Nasir Mesos 14/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Resource Offers
Offers resources to framework
Framework choose resources according to needs
Benefitsgives freedom to framework for implementationkeep Mesos simple and scalable
Drawback
decentralized decision might not be optimal
Muhammad Anis uddin Nasir Mesos 14/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Resource Offers
Offers resources to framework
Framework choose resources according to needs
Benefitsgives freedom to framework for implementationkeep Mesos simple and scalable
Drawback
decentralized decision might not be optimal
Muhammad Anis uddin Nasir Mesos 14/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Resource Offers
Offers resources to framework
Framework choose resources according to needs
Benefitsgives freedom to framework for implementationkeep Mesos simple and scalable
Drawbackdecentralized decision might not be optimal
Muhammad Anis uddin Nasir Mesos 14/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Architecture
Muhammad Anis uddin Nasir Mesos 15/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
Architecture
Muhammad Anis uddin Nasir Mesos 16/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource Allocation
Fair sharingStrict prioritiesDelay sharing
Isolation
Linux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharing
Strict prioritiesDelay sharing
Isolation
Linux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict priorities
Delay sharing
Isolation
Linux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
Isolation
Linux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
Isolation
Linux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux Containers
Solaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
Filters
Re-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resources
Incentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault Tolerance
Soft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault ToleranceSoft state master
Report node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault ToleranceSoft state masterReport node failure toframework
Multiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
OverviewArchitecture
More Features
Resource AllocationFair sharingStrict prioritiesDelay sharing
IsolationLinux ContainersSolaris Project
Scalability andRobustness
FiltersRe-offering resourcesIncentives
Fault ToleranceSoft state masterReport node failure toframeworkMultiple schedulers
Muhammad Anis uddin Nasir Mesos 17/32
MotivationMesos
ImplementationEvaluationConclusion
1 Motivation
2 MesosOverviewArchitecture
3 Implementation
4 Evaluation
5 Conclusion
Muhammad Anis uddin Nasir Mesos 18/32
MotivationMesos
ImplementationEvaluationConclusion
Implementation
10,000 lines of code in C++
Linux, Solaris, OS X
support framework written in Java, C++, Python
ZooKeeper
Frameworks
HadoopTorque and MPISpark
Muhammad Anis uddin Nasir Mesos 19/32
MotivationMesos
ImplementationEvaluationConclusion
Implementation
10,000 lines of code in C++
Linux, Solaris, OS X
support framework written in Java, C++, Python
ZooKeeper
Frameworks
HadoopTorque and MPISpark
Muhammad Anis uddin Nasir Mesos 19/32
MotivationMesos
ImplementationEvaluationConclusion
Implementation
10,000 lines of code in C++
Linux, Solaris, OS X
support framework written in Java, C++, Python
ZooKeeper
Frameworks
HadoopTorque and MPISpark
Muhammad Anis uddin Nasir Mesos 19/32
MotivationMesos
ImplementationEvaluationConclusion
Implementation
10,000 lines of code in C++
Linux, Solaris, OS X
support framework written in Java, C++, Python
ZooKeeper
Frameworks
HadoopTorque and MPISpark
Muhammad Anis uddin Nasir Mesos 19/32
MotivationMesos
ImplementationEvaluationConclusion
Implementation
10,000 lines of code in C++
Linux, Solaris, OS X
support framework written in Java, C++, Python
ZooKeeper
Frameworks
HadoopTorque and MPISpark
Muhammad Anis uddin Nasir Mesos 19/32
MotivationMesos
ImplementationEvaluationConclusion
Implementation
10,000 lines of code in C++
Linux, Solaris, OS X
support framework written in Java, C++, Python
ZooKeeper
FrameworksHadoop
Torque and MPISpark
Muhammad Anis uddin Nasir Mesos 19/32
MotivationMesos
ImplementationEvaluationConclusion
Implementation
10,000 lines of code in C++
Linux, Solaris, OS X
support framework written in Java, C++, Python
ZooKeeper
FrameworksHadoopTorque and MPI
Spark
Muhammad Anis uddin Nasir Mesos 19/32
MotivationMesos
ImplementationEvaluationConclusion
Implementation
10,000 lines of code in C++
Linux, Solaris, OS X
support framework written in Java, C++, Python
ZooKeeper
FrameworksHadoopTorque and MPISpark
Muhammad Anis uddin Nasir Mesos 19/32
MotivationMesos
ImplementationEvaluationConclusion
Spark
Data flow for logistic regression
Muhammad Anis uddin Nasir Mesos 20/32
MotivationMesos
ImplementationEvaluationConclusion
1 Motivation
2 MesosOverviewArchitecture
3 Implementation
4 Evaluation
5 Conclusion
Muhammad Anis uddin Nasir Mesos 21/32
MotivationMesos
ImplementationEvaluationConclusion
Dynamic Resource Sharing
96 Node Mesos Cluster
4 CPU Cores and 15GB Ram
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MotivationMesos
ImplementationEvaluationConclusion
Dynamic Resource Sharing
96 Node Mesos Cluster4 CPU Cores and 15GB Ram
Muhammad Anis uddin Nasir Mesos 22/32
MotivationMesos
ImplementationEvaluationConclusion
Dynamic Resource Sharing
Muhammad Anis uddin Nasir Mesos 23/32
MotivationMesos
ImplementationEvaluationConclusion
Data Locality with Resource Offers
16 instance of Hadoop using 93 EC2 nodes
1.7x speed up with Mesos
97% data locality with 5sec data scheduling
Muhammad Anis uddin Nasir Mesos 24/32
MotivationMesos
ImplementationEvaluationConclusion
Data Locality with Resource Offers
16 instance of Hadoop using 93 EC2 nodes
1.7x speed up with Mesos
97% data locality with 5sec data scheduling
Muhammad Anis uddin Nasir Mesos 24/32
MotivationMesos
ImplementationEvaluationConclusion
Data Locality with Resource Offers
16 instance of Hadoop using 93 EC2 nodes
1.7x speed up with Mesos
97% data locality with 5sec data scheduling
Muhammad Anis uddin Nasir Mesos 24/32
MotivationMesos
ImplementationEvaluationConclusion
Scalability
99 Amazon EC2 nodes
Scaled to 50,000 emulated slaves, 200 frameworks, 100K tasks
unable to scale beyond 50,000 slaves as Amazon EC2 clusterwas the bottleneck
Muhammad Anis uddin Nasir Mesos 25/32
MotivationMesos
ImplementationEvaluationConclusion
Scalability
99 Amazon EC2 nodes
Scaled to 50,000 emulated slaves, 200 frameworks, 100K tasks
unable to scale beyond 50,000 slaves as Amazon EC2 clusterwas the bottleneck
Muhammad Anis uddin Nasir Mesos 25/32
MotivationMesos
ImplementationEvaluationConclusion
Scalability
99 Amazon EC2 nodes
Scaled to 50,000 emulated slaves, 200 frameworks, 100K tasks
unable to scale beyond 50,000 slaves as Amazon EC2 clusterwas the bottleneck
Muhammad Anis uddin Nasir Mesos 25/32
MotivationMesos
ImplementationEvaluationConclusion
Further Experiments
Fault Tolerance
Mesos masters connected to a 5-node ZooKeeper quorumfault detection and recovery in 10 sec
Overhead
LINPACK for MPI and Wordcount Hadoop BecnhmarkOverhead of Mesos was less than 4%
Muhammad Anis uddin Nasir Mesos 26/32
MotivationMesos
ImplementationEvaluationConclusion
Further Experiments
Fault ToleranceMesos masters connected to a 5-node ZooKeeper quorum
fault detection and recovery in 10 sec
Overhead
LINPACK for MPI and Wordcount Hadoop BecnhmarkOverhead of Mesos was less than 4%
Muhammad Anis uddin Nasir Mesos 26/32
MotivationMesos
ImplementationEvaluationConclusion
Further Experiments
Fault ToleranceMesos masters connected to a 5-node ZooKeeper quorumfault detection and recovery in 10 sec
Overhead
LINPACK for MPI and Wordcount Hadoop BecnhmarkOverhead of Mesos was less than 4%
Muhammad Anis uddin Nasir Mesos 26/32
MotivationMesos
ImplementationEvaluationConclusion
Further Experiments
Fault ToleranceMesos masters connected to a 5-node ZooKeeper quorumfault detection and recovery in 10 sec
Overhead
LINPACK for MPI and Wordcount Hadoop BecnhmarkOverhead of Mesos was less than 4%
Muhammad Anis uddin Nasir Mesos 26/32
MotivationMesos
ImplementationEvaluationConclusion
Further Experiments
Fault ToleranceMesos masters connected to a 5-node ZooKeeper quorumfault detection and recovery in 10 sec
OverheadLINPACK for MPI and Wordcount Hadoop Becnhmark
Overhead of Mesos was less than 4%
Muhammad Anis uddin Nasir Mesos 26/32
MotivationMesos
ImplementationEvaluationConclusion
Further Experiments
Fault ToleranceMesos masters connected to a 5-node ZooKeeper quorumfault detection and recovery in 10 sec
OverheadLINPACK for MPI and Wordcount Hadoop BecnhmarkOverhead of Mesos was less than 4%
Muhammad Anis uddin Nasir Mesos 26/32
MotivationMesos
ImplementationEvaluationConclusion
1 Motivation
2 MesosOverviewArchitecture
3 Implementation
4 Evaluation
5 Conclusion
Muhammad Anis uddin Nasir Mesos 27/32
MotivationMesos
ImplementationEvaluationConclusion
Conclusion
Mesos shares clusters efficiently among diverseframeworks
Fine-grained sharing at the level of tasksResource Sharing, a scalable mechanism forapplication-controlled scheduling
Enabales co-existence of current frameworks anddevelopment of new specialized frameworks
Muhammad Anis uddin Nasir Mesos 28/32
MotivationMesos
ImplementationEvaluationConclusion
Conclusion
Mesos shares clusters efficiently among diverseframeworks
Fine-grained sharing at the level of tasks
Resource Sharing, a scalable mechanism forapplication-controlled scheduling
Enabales co-existence of current frameworks anddevelopment of new specialized frameworks
Muhammad Anis uddin Nasir Mesos 28/32
MotivationMesos
ImplementationEvaluationConclusion
Conclusion
Mesos shares clusters efficiently among diverseframeworks
Fine-grained sharing at the level of tasksResource Sharing, a scalable mechanism forapplication-controlled scheduling
Enabales co-existence of current frameworks anddevelopment of new specialized frameworks
Muhammad Anis uddin Nasir Mesos 28/32
MotivationMesos
ImplementationEvaluationConclusion
Conclusion
Mesos shares clusters efficiently among diverseframeworks
Fine-grained sharing at the level of tasksResource Sharing, a scalable mechanism forapplication-controlled scheduling
Enabales co-existence of current frameworks anddevelopment of new specialized frameworks
Muhammad Anis uddin Nasir Mesos 28/32
MotivationMesos
ImplementationEvaluationConclusion
References
Hindman, B., Konwinski, A., Zaharia, M., Ghodsi, A., Joseph,A. D., Katz, R., Shenker, S., et al. (n.d.). Mesos : A Platformfor Fine-Grained Resource Sharing in the Data Center.
http://incubator.apache.org/mesos/
http://datainthecloud.blogspot.se/2011/10/
mesos-platform-for-fine-grained.html
https://www.usenix.org/conference/nsdi11/
mesos-platform-fine-grained-resource-sharing-data-center
http://static.usenix.org/event/nsdi11/tech/
slides/hindman.pdf
Muhammad Anis uddin Nasir Mesos 29/32
MotivationMesos
ImplementationEvaluationConclusion
Mesos: A Platform for Fine-Grained ResourceSharing in the Data Center
Muhammad Anis uddin Nasir
KTH Royal Institute of Technology
November 20, 2012
Muhammad Anis uddin Nasir Mesos 30/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Tasks
elastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferences
weighted fair allocation policylottery scheduling
Heterogeneous Tasks
random task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task duration
rigid frameworks with exponential task duration
Placement Preferences
weighted fair allocation policylottery scheduling
Heterogeneous Tasks
random task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferences
weighted fair allocation policylottery scheduling
Heterogeneous Tasks
random task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferences
weighted fair allocation policylottery scheduling
Heterogeneous Tasks
random task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policy
lottery scheduling
Heterogeneous Tasks
random task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasks
random task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasks
random task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasksrandom task assignment
reserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasksrandom task assignmentreserve some resources on each node for small tasks
maximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasksrandom task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasksrandom task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentives
short taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasksrandom task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentivesshort tasks
elastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasksrandom task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentivesshort taskselastic tasks
do not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Different workloads
Homogeneous Taskselastic frameworks with constant task durationrigid frameworks with exponential task duration
Placement Preferencesweighted fair allocation policylottery scheduling
Heterogeneous Tasksrandom task assignmentreserve some resources on each node for small tasksmaximum task duration
Framework incentivesshort taskselastic tasksdo not accept unknown resources
Muhammad Anis uddin Nasir Mesos 31/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentation
not optimal bin packinglarge jobs may starveminimum offer size on each slave
Interdependent framework constraints
scenarios where only one task can be accommodated
Framework Complexity
framework scheduling is complexframework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packing
large jobs may starveminimum offer size on each slave
Interdependent framework constraints
scenarios where only one task can be accommodated
Framework Complexity
framework scheduling is complexframework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packinglarge jobs may starve
minimum offer size on each slave
Interdependent framework constraints
scenarios where only one task can be accommodated
Framework Complexity
framework scheduling is complexframework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packinglarge jobs may starveminimum offer size on each slave
Interdependent framework constraints
scenarios where only one task can be accommodated
Framework Complexity
framework scheduling is complexframework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packinglarge jobs may starveminimum offer size on each slave
Interdependent framework constraints
scenarios where only one task can be accommodated
Framework Complexity
framework scheduling is complexframework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packinglarge jobs may starveminimum offer size on each slave
Interdependent framework constraintsscenarios where only one task can be accommodated
Framework Complexity
framework scheduling is complexframework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packinglarge jobs may starveminimum offer size on each slave
Interdependent framework constraintsscenarios where only one task can be accommodated
Framework Complexity
framework scheduling is complexframework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packinglarge jobs may starveminimum offer size on each slave
Interdependent framework constraintsscenarios where only one task can be accommodated
Framework Complexityframework scheduling is complex
framework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packinglarge jobs may starveminimum offer size on each slave
Interdependent framework constraintsscenarios where only one task can be accommodated
Framework Complexityframework scheduling is complexframework has a choice
failures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32
MotivationMesos
ImplementationEvaluationConclusion
Limitations
Fragmentationnot optimal bin packinglarge jobs may starveminimum offer size on each slave
Interdependent framework constraintsscenarios where only one task can be accommodated
Framework Complexityframework scheduling is complexframework has a choicefailures are easy to handle with resource offers
Muhammad Anis uddin Nasir Mesos 32/32