|By Nikita Ivanov||
|March 22, 2014 02:00 PM EDT||
After five days (and eleven meetings) with new customers in Europe, Russia, and the Middle East, I think time is right for another refinement of in-memory computing's definition. To me, it is clear that our industry is lagging when it comes to explaining in-memory computing to potential customers and defining what in-memory computing is really about. We struggle to come up with a simple, understandable definition of what in-memory computing is all about, what problems it solves, and what uses are a good fit for the technology.
In-Memory Computing: What Is It?
In-memory computing means using a type of middleware software that allows one to store data in RAM, across a cluster of computers, and process it in parallel. Consider operational datasets typically stored in a centralized database which you can now store in "connected" RAM across multiple computers. RAM, roughly, is 5,000 times faster than traditional spinning disk. Add to the mix native support for parallel processing, and things get very fast. Really, really, fast.
RAM storage and parallel distributed processing are two fundamental pillars of in-memory computing.
RAM storage and parallel distributed processing are two fundamental pillars of in-memory computing. While in-memory data storage is expected of in-memory technology, the parallelization and distribution of data processing, which is an integral part of in-memory computing, calls for an explanation.
Parallel distributed processing capabilities of in-memory computing are... a technical necessity. Consider this: a single modern computer can hardly have enough RAM to hold a significant dataset. In fact, a typical x86 server today (mid-2014) would have somewhere between 32GB to 256GB of RAM. Although this could be a significant amount of memory for a single computer, that's not enough to store many of today's operational datasets that easily measure in terabytes.
To overcome this problem in-memory computing software is designed from the ground up to store data in a distributed fashion, where the entire dataset is divided into individual computers' memory, each storing only a portion of the overall dataset. Once data is partitioned - parallel distributed processing becomes a technical necessity simply because data is stored this way.
And while it makes the development of in-memory computing software challenging (literally fewer than 10 companies in the world have mastered this type of software development) - end users of in-memory computing seeking dramatic performance and scalability increas benefit greatly from this technology.
In-Memory Computing: What Is It Good For?
Let's get this out of the way first: if one wants a 2-3x performance or scalability improvements - flash storage (SSD, Flash on PCI-E, Memory Channel Storage, etc.) can do the job. It is relatively cheap and can provide that kind of modest performance boost.
To see, however, what a difference in-memory computing can make, consider this real-live example...
Last year GridGain won an open tender for one of the largest banks in the world. The tender was for a risk analytics system to provide real-time analysis of risk for the bank's trading desk (common use case for in-memory computing in the financial industry). In this tender GridGain software demonstrated one billion (!) business transactions per second on 10 commodity servers with the total of 1TB of RAM. The total cost of these 10 commodity servers? Less than $25K.
Now, read the previous paragraph again: one billion financial transactions per second on $25K worth of hardware. That is the in-memory computing difference - not just 2-3x times faster; more than 100x faster than theoretically possible even with the most expensive flash-based storage available on today's market (forget about spinning disks). And 1TB of flash-based storage alone would cost 10x of entire hardware setup mentioned.
Importantly, that performance translates directly into the clear business value:
- you can use less hardware to support the required performance and throughput SLAs, get better data center consolidation, and significantly reduce capital costs, as well as operational and infrastructure overhead, and
- you can also significantly extend the lifetime of your existing hardware and software by getting increased performance and improve its ROI by using what you already have longer and making it go faster.
And that's what makes in-memory computing such a hot topic these days: the demand to process ever growing datasets in real-time can now be fulfilled with the extraordinary performance and scale of in-memory computing, with economics so compelling that the business case becomes clear and obvious.
In-Memory Computing: What Are the Best Use Cases?
I can only speak for GridGain here but our user base is big enough to be statistically significant. GridGain has production customers in a wide variety of industries:
- Investment banking
- Insurance claim processing & modeling
- Real-time ad platforms
- Real-time sentiment analysis
- Merchant platform for online games
- Hyper-local advertising
- Geospatial/GIS processing
- Medical imaging processing
- Natural language processing & cognitive computing
- Real-time machine learning
- Complex event processing of streaming sensor data
And we're also seeing our solutions deployed for more mundane use cases, like speeding the response time of a student registration system from 45 seconds to under a half-second.
By looking at this list it becomes pretty obvious that the best use cases are defined not by specific industry but by the underlying technical need, i.e. the need to get the ultimate best and uncompromised performance and scalability for a given task.
In many of these real-life deployments in-memory computing was an enabling technology, the technology that made these particular systems possible to consider and ultimately possible to implement.
The bottom line is that in-memory computing is beginning to unleash a wave of innovation that's not built on Big Data per se, but on Big Ideas, ideas that are suddenly attainable. It's blowing up the costly economics of traditional computing that frankly can't keep up with either the growth of information or the scale of demand.
As the Internet expands from connecting people to connecting things, devices like refrigerators, thermostats, light bulbs, jet engines and even heart rate monitors are producing streams of information that will not just inform us, but also protect us, make us healthier and help us live richer lives. We'll begin to enjoy conveniences and experiences that only existed in science fiction novels. The technology to support this transformation exists today - and it's called in-memory computing.
"We are the public cloud providers. We are currently providing 50% of the resources they need for doing e-commerce business in China and we are hosting about 60% of mobile gaming in China," explained Yi Zheng, CPO and VP of Engineering at CDS Global Cloud, in this SYS-CON.tv interview at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 4, 2016 11:45 PM EST Reads: 908
"Once customers get a year into their IoT deployments, they start to realize that they may have been shortsighted in the ways they built out their deployment and the key thing I see a lot of people looking at is - how can I take equipment data, pull it back in an IoT solution and show it in a dashboard," stated Dave McCarthy, Director of Products at Bsquare Corporation, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 4, 2016 10:45 PM EST Reads: 992
Fact is, enterprises have significant legacy voice infrastructure that’s costly to replace with pure IP solutions. How can we bring this analog infrastructure into our shiny new cloud applications? There are proven methods to bind both legacy voice applications and traditional PSTN audio into cloud-based applications and services at a carrier scale. Some of the most successful implementations leverage WebRTC, WebSockets, SIP and other open source technologies. In his session at @ThingsExpo, Da...
Dec. 4, 2016 10:45 PM EST Reads: 1,652
@DevOpsSummit taking place June 6-8, 2017 at Javits Center, New York City, is co-located with the 20th International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. @DevOpsSummit at Cloud Expo New York Call for Papers is now open.
Dec. 4, 2016 08:30 PM EST Reads: 1,799
Predictive analytics tools monitor, report, and troubleshoot in order to make proactive decisions about the health, performance, and utilization of storage. Most enterprises combine cloud and on-premise storage, resulting in blended environments of physical, virtual, cloud, and other platforms, which justifies more sophisticated storage analytics. In his session at 18th Cloud Expo, Peter McCallum, Vice President of Datacenter Solutions at FalconStor, discussed using predictive analytics to mon...
Dec. 4, 2016 07:00 PM EST Reads: 4,909
"We are an all-flash array storage provider but our focus has been on VM-aware storage specifically for virtualized applications," stated Dhiraj Sehgal of Tintri in this SYS-CON.tv interview at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 4, 2016 06:30 PM EST Reads: 556
Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before. They have a question on their mind like “How is my application doing” but no id...
Dec. 4, 2016 06:30 PM EST Reads: 2,166
@GonzalezCarmen has been ranked the Number One Influencer and @ThingsExpo has been named the Number One Brand in the “M2M 2016: Top 100 Influencers and Brands” by Onalytica. Onalytica analyzed tweets over the last 6 months mentioning the keywords M2M OR “Machine to Machine.” They then identified the top 100 most influential brands and individuals leading the discussion on Twitter.
Dec. 4, 2016 06:30 PM EST Reads: 2,030
As data explodes in quantity, importance and from new sources, the need for managing and protecting data residing across physical, virtual, and cloud environments grow with it. Managing data includes protecting it, indexing and classifying it for true, long-term management, compliance and E-Discovery. Commvault can ensure this with a single pane of glass solution – whether in a private cloud, a Service Provider delivered public cloud or a hybrid cloud environment – across the heterogeneous enter...
Dec. 4, 2016 06:00 PM EST Reads: 1,537
In IT, we sometimes coin terms for things before we know exactly what they are and how they’ll be used. The resulting terms may capture a common set of aspirations and goals – as “cloud” did broadly for on-demand, self-service, and flexible computing. But such a term can also lump together diverse and even competing practices, technologies, and priorities to the point where important distinctions are glossed over and lost.
Dec. 4, 2016 05:45 PM EST Reads: 1,522
All clouds are not equal. To succeed in a DevOps context, organizations should plan to develop/deploy apps across a choice of on-premise and public clouds simultaneously depending on the business needs. This is where the concept of the Lean Cloud comes in - resting on the idea that you often need to relocate your app modules over their life cycles for both innovation and operational efficiency in the cloud. In his session at @DevOpsSummit at19th Cloud Expo, Valentin (Val) Bercovici, CTO of Soli...
Dec. 4, 2016 04:45 PM EST Reads: 1,640
"We're a cybersecurity firm that specializes in engineering security solutions both at the software and hardware level. Security cannot be an after-the-fact afterthought, which is what it's become," stated Richard Blech, Chief Executive Officer at Secure Channels, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 4, 2016 04:15 PM EST Reads: 617
"IoT is going to be a huge industry with a lot of value for end users, for industries, for consumers, for manufacturers. How can we use cloud to effectively manage IoT applications," stated Ian Khan, Innovation & Marketing Manager at Solgeniakhela, in this SYS-CON.tv interview at @ThingsExpo, held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 4, 2016 04:15 PM EST Reads: 4,166
Join Impiger for their featured webinar: ‘Cloud Computing: A Roadmap to Modern Software Delivery’ on November 10, 2016, at 12:00 pm CST. Very few companies have not experienced some impact to their IT delivery due to the evolution of cloud computing. This webinar is not about deciding whether you should entertain moving some or all of your IT to the cloud, but rather, a detailed look under the hood to help IT professionals understand how cloud adoption has evolved and what trends will impact th...
Dec. 4, 2016 03:00 PM EST Reads: 2,526
Information technology is an industry that has always experienced change, and the dramatic change sweeping across the industry today could not be truthfully described as the first time we've seen such widespread change impacting customer investments. However, the rate of the change, and the potential outcomes from today's digital transformation has the distinct potential to separate the industry into two camps: Organizations that see the change coming, embrace it, and successful leverage it; and...
Dec. 4, 2016 03:00 PM EST Reads: 3,254