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Performance Evaluation of Container-based Virtualization
for High Performance Computing Environments
Miguel G. Xavier, Marcelo V. Neves, Fabio D. Rossi, Tiago C.
Ferreto, Timoteo Lange, Cesar A. F. De Rose
miguel.xavier@acad.pucrs.br
Faculty of Informatics, PUCRS
Porto Alegre, Brazil
February 27, 2013
Outline
• Introduction
• Container-based Virtualization
• Evaluation
• Conclusion
Introduction
• Virtualization
• Hardware independence, availability, isolation and security
• Better manageability
• Widely used in datacenters/cloud computing
• Total cost of ownership is reduced
• HPC and Virtualization
• Usage scenarios
• Better resource sharing
• Custom environments
• However, hypervisor-based technologies in HPC environments has
traditionally been avoided
Container-based Virtualization
• A lightweight virtualization layer
• Non virtualized drivers
• Linux-Vserver, OpenVZ and LXC
Evaluation
• Experimental Environment
• Cluster composed by 4 nodes
• Two processors with 8 cores (without threads)
• 16GB of memory
• Evaluations
• Analyzing the best results of performance
• Through micro-benchmarks (such as CPU, disk, memory, network)
in a single node
• Through macro-benchmarks (such as HPC)
• Analyzing the best results of isolation
• Through IBS benchmark
CPU Evaluation
• All of Container-based systems
obtained performance results similar
to native
• No influence of the different CPU
schedulers when a single CPU-
intensive process is run in a single
processor
• Xen presents a average overhead of
4.3%
Linpack
Mflops
0100200300
Native
LXC
OpenVZ
VServer
Xen
LINPACK Benchmark (source: http://www.netlib.org/linpack/)
Memory Bandwidth Evaluation
STREAM Benchmark (source: https://www.cs.virginia.edu/stream/)
• Container-based systems have the
ability to return unused memory to the
host and other containers
• Xen presented 31% of performance
overhead when compared to the
native throughput
Disk Evaluation
IOZone Benchmark (source: https://www.iozone.org)
• LXC and Linux-VServer use the
”deadline” linux scheduler
• OpenVZ uses CFQ scheduler in
order to provide the container
disk priority functionality
• Xen uses virtualized drivers
which are not able to achieve a
high performance yet
Network Evaluation
50
100
150
200
10 1000
Message size (bytes)
Latency(microseconds)
Legend
LXC
Native
OpenVZ
VServer
Xen
0
250
500
750
1000
10
1
10
3
105
107
Message size (bytes)
Bandwidth(Mbps)
Legend
LXC
Native
OpenVZ
VServer
Xen
NETPIPE Benchmark (source: http://www.scl.ameslab.gov/netpipe/)
• Xen obtained the worst performance among the virtualization systems probably due to
network driver virtualized
HPC Evaluation
NAS-MPI Benchmark (source: http://www.nas.nasa.gov/publications/npb.html)
• At this moment, is possible to
observe that all container-based
systems slightly exceeds the
native performance
• All HPC benchmarks while
performed on Xen suffered even
more overheads by reason of the
network penalties
bt cg ep ft is lu mg
NAS Parallel Benchmarks
Executiontime(seconds)
050100150200
Native
LXC
OpenVZ
VServer
Xen
Isolation
Isolation Benchmark Suite (source: http://web2.clarkson.edu/class/cs644/isolation/
• The results represent how much the applications performance is impacted by
different stress tests in another vm/container
• DNR means that application was not able to run
• All of Container-based systems had some impact in isolation
LXC OpenVZ Vserver Xen
CPU 0 0 0 0
Memory Bomb 88,2% 89,3% 20,6% 0,9%
Disk Stress 9% 39% 48,8% 0
Fork Bomb DNR DNR DNR 0
Network Receiver 2,2% 4,5% 13,6% 0,9%
Network Sender 10,3% 35,4% 8,2% 0,3%
Conclusions
• All container-based systems have a near-native performance of CPU,
memory, disk and network
• The only resource that could be successfully isolated was CPU. All three
systems showed poor performance isolation for memory, disk and
network
• Since the HPC applications were tested, so far, LXC demonstrates to be
the most suitable of the container-based systems for HPC due its use
facilities and management
Thank you!

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Peformance Evaluation of Container-based Vi

  • 1. Performance Evaluation of Container-based Virtualization for High Performance Computing Environments Miguel G. Xavier, Marcelo V. Neves, Fabio D. Rossi, Tiago C. Ferreto, Timoteo Lange, Cesar A. F. De Rose miguel.xavier@acad.pucrs.br Faculty of Informatics, PUCRS Porto Alegre, Brazil February 27, 2013
  • 2. Outline • Introduction • Container-based Virtualization • Evaluation • Conclusion
  • 3. Introduction • Virtualization • Hardware independence, availability, isolation and security • Better manageability • Widely used in datacenters/cloud computing • Total cost of ownership is reduced • HPC and Virtualization • Usage scenarios • Better resource sharing • Custom environments • However, hypervisor-based technologies in HPC environments has traditionally been avoided
  • 4. Container-based Virtualization • A lightweight virtualization layer • Non virtualized drivers • Linux-Vserver, OpenVZ and LXC
  • 5. Evaluation • Experimental Environment • Cluster composed by 4 nodes • Two processors with 8 cores (without threads) • 16GB of memory • Evaluations • Analyzing the best results of performance • Through micro-benchmarks (such as CPU, disk, memory, network) in a single node • Through macro-benchmarks (such as HPC) • Analyzing the best results of isolation • Through IBS benchmark
  • 6. CPU Evaluation • All of Container-based systems obtained performance results similar to native • No influence of the different CPU schedulers when a single CPU- intensive process is run in a single processor • Xen presents a average overhead of 4.3% Linpack Mflops 0100200300 Native LXC OpenVZ VServer Xen LINPACK Benchmark (source: http://www.netlib.org/linpack/)
  • 7. Memory Bandwidth Evaluation STREAM Benchmark (source: https://www.cs.virginia.edu/stream/) • Container-based systems have the ability to return unused memory to the host and other containers • Xen presented 31% of performance overhead when compared to the native throughput
  • 8. Disk Evaluation IOZone Benchmark (source: https://www.iozone.org) • LXC and Linux-VServer use the ”deadline” linux scheduler • OpenVZ uses CFQ scheduler in order to provide the container disk priority functionality • Xen uses virtualized drivers which are not able to achieve a high performance yet
  • 9. Network Evaluation 50 100 150 200 10 1000 Message size (bytes) Latency(microseconds) Legend LXC Native OpenVZ VServer Xen 0 250 500 750 1000 10 1 10 3 105 107 Message size (bytes) Bandwidth(Mbps) Legend LXC Native OpenVZ VServer Xen NETPIPE Benchmark (source: http://www.scl.ameslab.gov/netpipe/) • Xen obtained the worst performance among the virtualization systems probably due to network driver virtualized
  • 10. HPC Evaluation NAS-MPI Benchmark (source: http://www.nas.nasa.gov/publications/npb.html) • At this moment, is possible to observe that all container-based systems slightly exceeds the native performance • All HPC benchmarks while performed on Xen suffered even more overheads by reason of the network penalties bt cg ep ft is lu mg NAS Parallel Benchmarks Executiontime(seconds) 050100150200 Native LXC OpenVZ VServer Xen
  • 11. Isolation Isolation Benchmark Suite (source: http://web2.clarkson.edu/class/cs644/isolation/ • The results represent how much the applications performance is impacted by different stress tests in another vm/container • DNR means that application was not able to run • All of Container-based systems had some impact in isolation LXC OpenVZ Vserver Xen CPU 0 0 0 0 Memory Bomb 88,2% 89,3% 20,6% 0,9% Disk Stress 9% 39% 48,8% 0 Fork Bomb DNR DNR DNR 0 Network Receiver 2,2% 4,5% 13,6% 0,9% Network Sender 10,3% 35,4% 8,2% 0,3%
  • 12. Conclusions • All container-based systems have a near-native performance of CPU, memory, disk and network • The only resource that could be successfully isolated was CPU. All three systems showed poor performance isolation for memory, disk and network • Since the HPC applications were tested, so far, LXC demonstrates to be the most suitable of the container-based systems for HPC due its use facilities and management