Documentation
on Virtualization and its effect on High Performance Computing
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Today is an era
of High-Performance computing where business needs have increased and so have
increased the demands for resources but in any general case if we opt we will
come to see that the hardware resources are very much limited and we have to accommodate
our every possible needs within it. So here is a small view of what I came to
know going through few research papers, amongst which one describes the
limitation of cloud when coming to the point “High Performance Computing”.
Abstract
Despite cloud
computing’s tremendous benefits in the fields of applications like enterprise,
Web, game and multimedia. Its success in the field of high performance
computing has been very limited. The most obvious reason cited for it is “Virtualization”
which is a technique to run multiple instances of operating system or
server side services at a go. Meanwhile the rising need of virtualization has
compelled CPU vendors to modify the chips with virtualization. The concept of
virtualization at hardware level helped reducing the gap between high
performance computing with virtualization. This helped in accelerating context
switching, speeding up address translation and enable I/O direct access which
can basically cause overhead of resources.
Research
works indicate that hardware assisted virtualization can bring high performance
computing as a service into realization.
Limitations of virtualization on HPC
level and its solutions
So why are these
limitations perceptual to virtualization when we talk in terms of high
performance computing?????
The
answer to this question is very simple. High Performance Computing utilises the
concept of parallel processing which requires distributed system to achieve parallel
processing and apparently research work has shown virtualization to be a root
cause to the detrimental performance of high performance computing since it was
causing overhead on the CPU and I/O. So, in the field of HPC, the cloud
technology was not been able to provide proper functioning as compared to high
performance utilisation of resources.
So
cloud service providers have been trying to mitigate this performance gap. For
example Amazon Web Services (AWS) has launched “EC2 Cluster Compute Instances”
which did some promising work on this problem keeping in mind that HPC
applications may still reap the benefits from cloud computing and
virtualization.
Further
development to the concept of integrating virtualization with chips was first
propelled by Intel which released VT-i (for Itanium), VT-x
and VT-d
whereas AMD released AMD-V and AMD-Vi. The VT-i,
VT-x
and AMD-V
extensions were used to accelerate context switching between VMM (Virtual Machine Monitor)
and guest Virtual Machines. These virtual machines also helped in adding
hardware page tables to the Memory Management Unit (MMU) to
speed up page table mappings from guest virtual machines to physical machines.
Evaluating two cases of Virtual Machine
In this
discussion I have emphasized o discussing about two instances of virtual
machine that is KVM (Kernel Virtual Machine) and XEN. Both their workings and
architectural approaches of virtualization are different. The reasons for comparing
these two instances are
1) Both of them support Full
Virtualization where there is no need for modification of the guest operating
system.
2) These two instances are open source
virtualization tools which are widely supported by any LINUX distributions.
XEN architecture
XEN is basically a
hypervisor that is running in the most privileged CPU state than any other
software running on the physical machine. This hypervisor is responsible for
memory management, CPU scheduling etc. In XEN architecture there is a host
operating system which interacts with the system on behalf of the guest operating
system. The host operating system is referred as “dom0” and the guest
operating systems are referred as “domU”. The dom0 domain is launched
when the physical machine starts and is a modified operating system. The dom0
domain controls the hypervisor and manages it to launch the unprivileged guest
operating system or domU operating system. In case of paravirtualization
approach the OS on the domU is slightly modified. The domU paravirtualized
operating system can access the hardware devices by the help of paravirtualized
drivers through the dom0.
KVM architecture
Kernel
based virtual machine (KVM) is a virtualization technique which
comes integrated with the LINUX kernel. It was first developed by Qumranet. Instead
of creating a separate hypervisor the LINUX kernel was used as a hypervisor
itself. The KVM depends on the hypervisor for virtualization. It comprises of a
loadable kernel module kvm.ko providing the
core virtualization infrastructure which is loaded by the physical machine at
start up.
In this
virtualization model virtual machines are created by using a device node (/dev/kvm). Every guest VM in this virtualization is just another
regular process scheduled by the regular LINUX scheduler. Besides two modes of
execution kernel and user there is another mode that KVM
adds which is the guest mode (which has its own kernel and user modes that the
hypervisor is unconcerned with)
Since a VM in
Kernel Virtual Machine is simply a process, the standard LINUX process
management tools can be used. One can destroy, pause and resume a VM with the kill
command and view resource usage with the top command. The VM belongs to the
user who started it and all accesses to it are verified by the kernel. Hence system
administrators can easily manage VMs with the existing tools provided with the
LINUX itself.
Disk Performance status of KVM and XEN
(On the basis of I Ozone version 3-398 as the microbench mark tool for
executing some file operations and measuring their performances)
Operations
|
In Physical Machine
|
In Virtual Machine with XEN
|
In Virtual Machine with KVM
|
CPU cache effect
|
Write operation
|
Yields
a throughput of around 1.65 gb/sec
|
Yields
a throughput just above 1.0 gb/s when the file size is
below 32 mb and increases to above 1.75 gb/s afterwards.
|
Yields
a throughput of 1.2 gb/s
|
---
|
Read operation
|
Throughput
of 6.85
gb/s when the record size is 128 kb or less and it reduces to 6.25
gb/s when the record size increases
|
Throughput
is 4.8
gb/s but it increases to 6.4 gb/s when the file size is
above 128 mb
|
Throughput
reaches 4.8 gb/s when the record size is 64 kb or less and then
decreases to 3.9 gb/s
|
Throughput
goes beyond 9.2 gb/s when the file size is below 4 mb ad the record size
is less than 128 kb
|
