The New Era of Intelligent Application Mobility

By Duncan Johnston-Watt

July 19, 2011

Duncan Johnston-Watt, founder & CEO of Cloudsoft describes the concept of intelligent application mobility and what it signals for the new era of being able to seamlessly move applications across clouds and locations.

The dramatic growth in the use of multiple networked computers – often spread across the globe – in order to support business applications makes it compelling for an application to have mobility. For example, the impact of maintaining a server machine is reduced if the application(s) it hosts can be moved to an alternative machine before starting maintenance; disasters can be avoided if an application can be moved off failing machines; network load can be reduced by moving (all or parts of) an application closer to its data; performance can be improved if (all or parts of) an application is moved closer to its users.

Consequently a number of approaches are well established for achieving application mobility. For example, infrastructure virtualization vendors offer application mobility by moving virtual-machines between physical machines (VMware and IBM both refer to this as “live application mobility” 1). Distributed caching vendors facilitate mobility via the data tier2.

However these approaches are only partially effective. The “all-or-nothing” approach of moving entire virtual machines around, especially across wide-area networks, is expensive and slow.  The complex co-ordination of data across distributed caches will often fatally compromise performance and/or integrity, especially for high throughput systems or again where wide-area networks are involved. Instead what’s needed is a far more agile form of application mobility, and one that’s far better suited to the cloud generation.

What is Intelligent Application Mobility?

Intelligent Application Mobility enables business applications to dynamically distribute themselves as needed across multiple machines, locations, and clouds – while they are still running and under the full control of user-defined policies.
Intelligent Application Mobility achieves this by:

–    creating an all-software overlay network (on top of, and with no change to, existing networks)  that dynamically spans machines, locations and clouds as needed to form an Elastic Process Fabric

–    activating applications as fine-grained segments that can flow across the Elastic Process Fabric as needed

–    using policies combined with real-time monitoring to continually optimize segment deployment – for example to ensure that each segment is in the right location to deliver best performance

What types of applications need Intelligent Application Mobility?

It’s probably fair to say that most types of application would benefit to some extent from Intelligent Application Mobility: a self-optimizing application with real-time elasticity and that can near-instantly move itself out of harm’s way will always be advantageous. However there are particular types of application for which the approach is compelling.

Applications that execute business transactions are difficult to scale and distribute as they must maintain consistency and integrity when changes are made to data. Maintaining these constraints is rarely a problem when data contention and transaction volumes are low but challenges quickly emerge as business applications scale-out, particularly where applications involve wide area networks. Traditional approaches to solving scalability challenges include statically partitioning an application across multiple resources, replicating and synchronising multiple instance of the application, and the prevailing vogue of “stateless programming”.

The use of “stateless applications” is particularly interesting as ‘received wisdom’ deems that this approach is essential for cloud deployments: by removing application state from the server tier it doesn’t matter which instance of a server handles any given request, so “instance-on-demand” is available whereby you can spin up and spin down as many instances of the server as you want.

However so-called stateless approaches simply delegate the management of data contention to the data tier, which invariably makes the application less efficient: all necessary state has to be fetched from the data tier prior to servicing each request; any changes to business state must be mediated by the data tier; and all state must be given back to the data tier after each request. Consequently what would be simple and lightweight to achieve in a stateful process now becomes more complex and long-winded in a stateless process.

So the sweet-spots for Intelligent Application Mobility include any or all of the following characteristics:

–    distribution across multiple machines, locations or clouds

–    high volumes of transactions

–    volatile or unpredictable workloads

How is it used?

The capabilities that make up Intelligent Application Mobility, as discussed above, are exactly the kind of capabilities that middleware is intended to implement and make available as a service to developers. And with the availability of development frameworks such as Spring (from SpringSource) and  Seam (from Red Hat), this type of middleware can now be all-but completely hidden from the developer.  Consequently the main requirement for using Intelligent Application Mobility is to ensure that your applications are designed in a way that allows their deployment as fine-grained segments. For example, Microsoft actually calls these fine-grained segments “grains” and puts them at the heart of their “Framework for Cloud Computing”3.

One of the key advantages of encapsulating Intelligent Application Mobility in middleware in this way is that the developer can now, for the first time, code completely scale-agnostic, distribution-agnostic, and cloud-agnostic applications. Development returns to the simplicity of coding just the business logic and making method calls in order to use another service – the Intelligent Application Mobility middleware takes care of scaling, distribution and management issues at run-time.

About the Author

Duncan Johnston-Watt (Founder & Chief Executive Officer) is a serial entrepreneur and industry visionary with over twenty years experience in the software industry. Immediately prior to founding Cloudsoft Duncan was CTO at Enigmatec Corporation, the enterprise data center automation company he founded in 2001.  

A Computerworld Smithsonian Laureate for his pioneering work introducing Java Enterprise to financial services, Duncan holds an MSc in Computation from Oxford University and a BA in Mathematics and Philosophy from Leeds University

REFERENCES

1.    

“AIX 6 Workload Partition and Live Application Mobility”
http://www.ibm.com/developerworks/aix/library/au-wpar/index.html

“VMWare and F5 Announce Collaboration for Cloud Live Application Mobility”
http://news.softpedia.com/news/VMWare-and-F5-Announce-Collaboration-for-Cloud-Live-Application-Mobility-120583.shtml

“Enhance Business Continuance with Application Mobility Across Data Centers”
http://www.cisco.com/en/US/prod/collateral/switches/ps9441/ps9402/white_paper_c11-591960.pdf

2.

NetApp DataMotion
http://www.netapp.com/us/products/platform-os/datamotion.html

“What Is an Enterprise Data Fabric?”
http://community.gemstone.com/pages/viewpage.action?pageId=6032133

Scaleout Geoserver
http://www.scaleoutsoftware.com/products/product-extensions/scaleout-geoserver/

3.

“Orleans: A Framework for Cloud Computing” by Sergey Bykov, Alan Geller, Gabriel Kliot, James Larus, Ravi Pandya, and Jorgen Thelin; 30 November 2010
http://research.microsoft.com/apps/pubs/default.aspx?id=141999

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference phase of neural networks (NN). Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This