Welcome!

Agile Computing Authors: Liz McMillan, Pat Romanski, Elizabeth White, Dana Gardner, Andy Thurai

Related Topics: Recurring Revenue, Microsoft Cloud, Containers Expo Blog, @CloudExpo

Recurring Revenue: Article

Big Data on Grids or on Clouds?

Now that we have a new computing paradigm, Cloud Computing, how can Clouds help our data?

Now that we have a new computing paradigm, Cloud Computing, how can Clouds help our data? Replace our internal data vaults as we hoped Grids would? Are Grids dead now that we have Clouds? Despite all the promising developments in the Grid and Cloud computing space, and the avalanche of publications and talks on this subject, many people still seem to be confused about internal data and compute resources, versus Grids versus Clouds, and they are hesitant to take the next step. I think there are a number of issues driving this uncertainty.

Grids didn't keep all their promises
Grids did not evolve (as some of us originally thought) into the next fundamental IT infrastructure for everything and everybody. Because of the diversity of computing and data environments, we had to develop different middleware (department, enterprise, global, compute, data, sensors, scientific instruments, etc.), and had to face different usage models with different benefits. Enterprise Grids were (and are) providing better resource utilization and business flexibility, while global Grids are best suited to complex R&D collaboration with resource sharing. For enterprise usage, setting up and operating Grids was often complicated and did not remove all the (data) bottlenecks. For researchers this characteristic was seen to be a necessary evil. Implementing complex applications on supercomputers has never been easy. So what.

Grid: the way station to the Cloud
After 40 years of dealing with data processing, Grid computing was indeed the next big thing for the grand challenge R&D expert, while for the enterprise CIO, the Grid was a way station on its way to the Cloud model. For the enterprise today, Clouds are providing all the missing pieces: easy to use, economies of scale, business elasticity up and down, and pay-as you go (thus getting rid of some capital expenditure). And in cases where security matters, there is the private Cloud, within the enterprise's firewall. In more complex enterprise environments, with applications running under different policies, private Clouds can easily connect via the Internet to (external) public Clouds -- and vice versa -- forming a hybrid Cloud infrastructure that balances security with efficiency.

Different policies, what does that mean?
No data processing job is alike. Jobs differ by priority, strategic importance, deadline, budget, IP and licenses. In addition, the nature of the code often necessitates a specific computer architecture, operating system, memory, storage, and other resources. These important differentiating factors strongly influence where and when a data processing job is run. For any job, a set of specific requirements decide on the set of specific policies that have to be defined and programmed, such that any of these jobs will run just according to these policies. Ideally, this is guaranteed by a dynamic resource broker that controls submission to Grid or Cloud resources, be they local or global, private or public.

Grids or Clouds?
One important question is still open: how do I find out, and then tell the resource broker, whether my data should run on the Grid or in the Cloud? The answer, among others, depends on the algorithmic structure of the program, which might be intolerant of high latency and low bandwidth. The performance limitations are exhibited mainly by parallel applications with tightly-coupled, data-intensive inter-process communication, running in parallel on hundreds or even thousands of processors or cores.

The good news is, however, that many applications do not require high bandwidth and low latency. Parameter studies often seen in science, engineering, and business intelligence, where a single self-contained application executes with many different parameters, resulting in many independent jobs. The list of examples is extensive -  analyzing the data from a particle physics collider, identifying the solution parameter in optimization, ensemble runs to quantify climate model uncertainties, identifying potential drug targets via screening a database of ligand structures, studying economic model sensitivity to parameters, and analyzing different materials and their resistance in crash tests, to name just a few.

Big Data needs Grids or Clouds, and often both
Obviously, there is no "Grids or Clouds" for the enterprise. There is just "Grids and Clouds", it really depends on the individual scenario. In general, CIOs have to evaluate three different scenarios:

  • (1) the Private Cloud: optimizing and virtualizing the company's internal enterprise IT infrastructure, including the data layer (here is where Momentum can help);
  • (2) the Hybrid Cloud: do (1) and connect to external clouds;
  • or (3) the Public Cloud: do (2) and successively move data (processing) to the external cloud provider.

The choice for the best-suited scenario depends on many aspects: sensitive / competitive data and applications (e.g. medical patient records), individual return on investment, security policies, interoperability between private and public clouds, lose of control when moving data outside the corporation, cloud-enabling data and applications, the current software licensing model, protection of intellectual property, legal issues, and more.

The good news is that CIOs can always start with a hybrid infrastructure in mind: combining private and public cloud resources, balanced according to specific requirements. This provides the best of both worlds, avoiding the worst of each individual world.

More Stories By Pawel Plaszczak

Pawel’s international software engineering experience includes work at CERN, British Telecommunications and Argonne National Laboratory. In 2003, Pawel founded GridwiseTech to lead pioneering work for the early adopters of scalable systems. Under Pawel's leadership the company has won the trust and respect of customers including Turner Broadcasting, Ricoh, and Philips, and led numerous research efforts for international consortia. Pawel is the author of numerous articles and tutorials, the book "Grid Computing: The Savvy Manager's Guide", and a frequent speaker at professional conferences and events.

IoT & Smart Cities Stories
Contextual Analytics of various threat data provides a deeper understanding of a given threat and enables identification of unknown threat vectors. In his session at @ThingsExpo, David Dufour, Head of Security Architecture, IoT, Webroot, Inc., discussed how through the use of Big Data analytics and deep data correlation across different threat types, it is possible to gain a better understanding of where, how and to what level of danger a malicious actor poses to an organization, and to determ...
The hierarchical architecture that distributes "compute" within the network specially at the edge can enable new services by harnessing emerging technologies. But Edge-Compute comes at increased cost that needs to be managed and potentially augmented by creative architecture solutions as there will always a catching-up with the capacity demands. Processing power in smartphones has enhanced YoY and there is increasingly spare compute capacity that can be potentially pooled. Uber has successfully ...
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
We are seeing a major migration of enterprises applications to the cloud. As cloud and business use of real time applications accelerate, legacy networks are no longer able to architecturally support cloud adoption and deliver the performance and security required by highly distributed enterprises. These outdated solutions have become more costly and complicated to implement, install, manage, and maintain.SD-WAN offers unlimited capabilities for accessing the benefits of the cloud and Internet. ...
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
With 10 simultaneous tracks, keynotes, general sessions and targeted breakout classes, @CloudEXPO and DXWorldEXPO are two of the most important technology events of the year. Since its launch over eight years ago, @CloudEXPO and DXWorldEXPO have presented a rock star faculty as well as showcased hundreds of sponsors and exhibitors! In this blog post, we provide 7 tips on how, as part of our world-class faculty, you can deliver one of the most popular sessions at our events. But before reading...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.