In the quest to wring out greater efficiencies from the IT
infrastructure, many organizations have found relief in a number of different
virtualization technologies: on the endpoint, in the storage infrastructure and
at the server level. But even with the advantages offered by the most common
virtualization techniques, the server utilization rate of the typical
enterprise is still limited by memory constraints.
“It’s a well-known, although sometimes little-discussed
problem,” says Andy Mallinger, vice president of marketing for Portland,
Ore.-based RNA Networks. “Server
virtualization certainly helps with server consolidation and reduces some
degree of server sprawl, but you can't get your mission-critical applications
on a virtualized server. There are just too many constraints. If you think
about it, if you take a server and you put 10 instances of the virtual machine
on there and that server has 4 or 8 gigabytes of memory, you have to over-commit
that memory to all of those virtual instances and you really run into
performance problems.”
As Mallinger puts it, many times servers are swapped out of
data centers not because they’re in need of a processing boost or a bump in
storage, but because they just don’t have memory parity, especially as server
virtualization improves processor utilization.
Enter RNA, which seeks to add memory virtualization as yet
another subset of virtualization that can give organizations a boost in
performance, improved power efficiency and better overall utilization rates.
“We believe that this is part of the very big trend of
virtualization of servers, storage virtualization. There has been a lot of
innovation and a lot of benefits driven out of those two types of
virtualization, but memory has been left out of the party, so to speak,” Mallinger
says.
The first of its kind in the field of memory virtualization,
RNA’s products work by separating out the physical resource of memory within
individual servers, pooling them together logically and then making that pool
available to all of the servers so that the data sets they can hold are many times
larger than what they could if they only depended on their individual memory
resources.
“What I see, especially in the market that I support, which
includes financial markets and high-performance computing, is that for certain
classes of problems the amount of memory that you have in an individual
commodity server is no longer sufficient,” says John Barr, research director
for The 451 Group. “And so having some way of presenting a larger amount of
memory across multiple systems as a single resource for a program running on
one is an excellent idea.”
Initially conceived several years ago by a number of
veterans in the high-performance computing field, RNA
came out of stealth mode in February 2009. According to Mallinger, the company
has a handful of customers, with still more in the pilot phase. The company is
backed by $7 million in venture capital from Menlo Ventures and has already
garnered interest from analysts such as Barr and Nik Simpson, senior analyst
for Burton Group.
Simpson says he believes RNA’s
biggest market opportunities are for organizations that maintain very large Internet
database stores that have to be "sliced and diced and searched in multiple
different ways."
“At the moment what [organizations with these stores] do is
distribute that over many small servers and keep a small portion of the
database locked in memory on each server,” he says. “What this would enable
them to do is to use one larger server to do the actual slicing and dicing and
to store the database engine or the Website environment in memory. The idea is
that you're going to be able save power and save cost and improve performance
when you're working with large data sets like that.”
RNA technology also provides relief for large financial
organizations trying to optimize their financial messaging systems, Barr says.
One of RNA’s first
customers, a large hedge fund that chose not to be disclosed for competitive
reasons, reported that it experienced a 700 percent improvement in messaging
volume using RNA technology.
“They were sending about 6,300 transactions per second when
they started, they had a goal of getting up to about 10,000 per second; they
would have been thrilled with that,” Mallinger says. “In the end, we reached
well over 50,000 transactions per second.”
While the numbers look impressive in specific scenarios,
such as financial markets, cloud computing scenarios and in industries doing
complicated modeling such as oil exploration, both Barr and Simpson warn that RNA
is not necessarily a general-purpose technology.
“For classes of application where you're reading a lot of
data, it’s fantastic,” Barr says. “But for classes of application where you're
doing as much writing as reading, then the performance penalty of writing data
to systems which use memory on another system becomes an issue.”
Additionally, Simpson wonders whether the promise of flash
memory in solid-state disks may eventually make this approach obsolete.
“The question is whether technologies like flash memories in
the form of SSDs or in other form factors actually inside the host rather than
being shared across a network might not [be] the better solution in the long
run,” he says. “If you interface in such a way directly onto the motherboard or
onto the server, then your latency is probably going to be less than going out
over the network to use memory on another server.”
In the meantime, though, for the right situations, both
analysts agree that the approach is worth a look.
