Industry executives and experts share their predictions for 2020. Read them in this 12th annual VMblog.com series exclusive.
By Nader Salessi, CEO and founder of NGD Systems
NVMe, 5G and Content Delivery Networks
As data sets grow and
data-intensive applications such as Big Data analytics, artificial intelligence
(AI), machine learning (ML), genomics, and IoT gain in use, the costs and time
needed for data movement is becoming critically challenged. Given the
terabytes, sometimes even petabytes, of data being generated each day, data
movement becomes a huge issue - especially when analytics are needed in real
time. The issue is that moving massive amounts of data from storage to host CPU
memory to process a query is costly in terms of power consumption and time.
With the impact of data movement being felt in nearly all compute applications,
change is needed in 2020 to handle this growing strain.
Below are my predictions on how
organizations will store and process data next year, particularly enterprises
relying on hyperscale, edge and CDN environments.
NVMe is NOT Enough -
Move Less and Analyze More at the Edge - As we connect more Edge
and IoT devices like video surveillance cameras and autonomous cars or
‘anything' that can churn out data 24/7 - one of the major issues that
many organizations experience is bottlenecks. The challenge: out of
these enormous data sets created each day, organizations are often only
trying to extract only approximately 10% so,
how can
that be done in a timely manner? The trick is to pinpoint the
value of data in real time. NVMe (Non-Volatile Memory Express) has
provided a measure of relief and proven to remove existing storage
protocol bottlenecks for platforms churning out terabytes and petabytes of
data on a regular basis.
- But, is that enough? Even though NVMe is substantially faster, it is not fast enough
by itself when petabytes of data are required to be analyzed and
processed in real time.
- UPSHOT: This is where
Computational Storage comes in and solves the problem of data management
and movement. Computational Storage, especially the way we
marry the use of NVMe SSDs and compute power, adds analytical power and
speed so that results can be accomplished right away and where the data
is generated.
Computational Storage Enables Better 5G Connectivity: In 2020, more edge related devices will be needed to
process massive data workloads. The advent of 5G is no
different.
- Here is why: We all know that 5G increases the
amount of bandwidth and speed of communication but along with 5G comes the
need to develop a more complex infrastructures a to support seamless
connectivity.
- UPSHOT: As more cell towers
are built to support 5G, there also needs to be more complex
infrastructure at each bay station that can manage the data "in and out
of the box" so that user data is optimally utilized. Computational
Storage with its small form factors and added compute power can pack an
analytical uppercut punch in the limited size and power enabled
edge-datacenters that live at each of these new cell tower platforms. By
providing additional compute to the confined resources that exist is
paramount to successful growth of this space. Instead of requiring even
more hardware and power to the server, the advent of high capacity
Computational Storage provides the needed offload to the system to allow
for great deployments.
Computational Storage
Can Simplify the Traffic Flow of Content
Delivery Networks:
Streaming services have continued to dominate
headlines this year, with the recent launches of Apple TV+, Disney
Plus and NBC Peacock, combined with Netflix, Hulu and Amazon Prime's
increasing investments. This poses a major hurdle for the content delivery
networks (CDNs) - and where Computational Storage can be a major asset.
- Customers will have no
patience for buffering or interruptions in video streams.Generally
speaking, video requires lots of expensive data movement, which makes it
more costly and difficult to deliver. CDNs have typically relied on a
traditional cloud model to support streaming customers, but they realized
centralized clouds were too far on average from the end points they sent
data to, racking up costs, latency and downtime.
-
UPSHOT: Computational
Storage can
solve for these issues. While a typical CDN traffic flow involves lots of
data movement and processing spread out over a variety of edge infrastructure,
Computational Storage, can simplify this flow. Thanks to in-situ processing,
this is all done within the storage device itself. This means more
people get to watch their content faster with less overall hardware overhead.
Instead of doing all the content verification and security in a centralized
location, the use of Computational Storage allows for the device to
authenticate and even decode thread for each user is an added value to this
market.
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About the Author
Nader has spent his
career leading technology teams and driving company success for over 30 years.
With history leading the STEC Engineering department and launch of the
Enterprise SSD Market. More recently Nader led a team at Western Digital in
their Enterprise SSD product platform. In 2013, he made the decision to launch
his own company, NGD Systems, with his co-founders to develop an emerging
solution to the SSD market, creating the world's first Computational Storage
device.