What To Do With All Those Spare CPWs

January 25, 2016 Alex Woodie

If there’s one thing that the average IBM i shop with a typical business forecast doesn’t need, it’s more CPW. The latest generation of Power8 servers offer more than enough computational oomph to get the job done, which raises the question: What should one do with those spare CPWs? With some forward thinking, the average IBM i shop maybe doesn’t have to be so average anymore.

There is no doubt there’s a power glut of Power8 iron in IBM‘s IBM i market. Each Power8 chip has roughly 50 percent more computational power per core than the Power7 chip launched in 2010. And since IBM is adding more cores (12 in Power8 versus eight in Power7) to its systems, the horsepower quickly climbs.

So when you consider that IBM is making similar investments in keeping ahead of Intel in the memory bandwidth department, you find that an average, run-of-the-mill P20-tier system like the Power E850 has twice the system throughput as a similarly sized P20-tier Power7 box, the Power 750.

The problem is, the workloads of IBM i shops aren’t growing this fast–not by a longshot. Thanks to the relative efficiency of RPG and COBOL code, ERP systems, and other business applications that were developed decades ago to run on AS/400 and iSeries systems that were computationally constrained (relatively speaking) run with plenty of headroom on new systems. (This wouldn’t be such an issue had we all moved to Java, of course). It’s no wonder so many shops are downgrading their hardware footprint when upgrading their systems. The smallest Power8 systems are plenty powerful for most IBM i shops.

While IBM fights to retain the premium that IBM i shops have historically paid over AIX and Linux customers–and attempts to prevent shops from downgrading from enterprise-class systems into cheaper midrange-class systems–it begs the question: What will IBM i shops do with all those CPWs if their database and ERP workloads don’t need them?

Here’s one wild and crazy idea: do some analytics.

That may sound totally nuts to the average IBM i shop, which may do some reporting on the system but nothing that would be considered “business intelligence.” But the truth is, the latest incarnation of BI–big data analytics–is coming to a theater near you, and it’s coming sooner than you think.

The biggest companies in every industry are using big data tech to gain a competitive edge. These adopters often built their own solutions, but today the market for big data tech is flattening, the solutions are becoming more shrink-wrapped, and commodity forces are at work. That’s great news for the smaller fish who live downstream from the big guys.

The question that IBM i shops should be asking themselves isn’t whether they need to have a big data strategy (they should). Instead, the right question is: How should they get started?

The big data market today revolves around the commodity Lintel system. Much of the big data tech today–the distributed file systems and schema-less data stores–was developed by Silicon Valley Web giants like Google, Yahoo, Facebook, and LinkedIn, and then made open source. That’s good news for IBM, which has worked hard to retrofit some of the latest and greatest big data tech, such as Apache Hadoop and Apache Spark, to run on its Power processors, and to be supported on Power Systems and OpenPower ecosystems.

While IBM is playing catchup when it comes to big data tech on Power, it does have a few aces up its sleeve, including superior memory bandwidth compared to Intel’s architecture. When big data gets really big and you want to access it quickly, that speed turns into a concerted advantage.

Most, if not all, of the big data tech that IBM is supporting runs on Linux. This brings us to some other good news: thanks to IBM’s work on solving the Little Endian/Big Endian issue, much of the software that’s been developed to run on Linux for X64 can also run on Linux for Power without too much work. On its website, IBM says that Canonical, the company behind Ubuntu Linux, managed to port 40,000 Linux apps to Power in just 160 days.

In addition to requiring lots of storage, big data analytics also eat CPUs (and their EBCDIC cousin, CPW) for breakfast. That’s good news for Power Systems users looking for useful things to do with their big, expensive iron while it’s sitting idle. All it takes is some courage to start experimenting–and of course a couple of Linux LPARs to start playing around.

Neo4j uses IBM’s Power8 technology to take big graph analytics to another level.

One of the most interesting big data-related developments to happen in the IBM Power world lately is the partnership created between Neo Technology and IBM last October.

Neo, if you’re not familiar, is the leading developer of graph databases for big data analytics, and its flagship product, called Neo4j, is being used in hundreds of blue-chip companies such as Wal-Mart, Pitney Bowes, and UBS. Just as Facebook uses a graph to connect systems, Neo4j can quickly find similarities among billions of connected entities, and the software runs at the heart of a variety of applications in fraud detection, master data management, and real-time recommendation generation.

The cool thing about Neo and IBM teaming up is the capability to solve truly massive graph problems on Power8 servers. The companies did the work to enable Neo4j to run directly atop the Coherent Accelerator Processor Interface (CAPI) cards that IBM uses to connect co-processors, such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs) to the PCI bus. Neo executives say they’re able to access up to 40TB of CAPI-supplied flash memory as if it were main memory, bypassing Linux and the file system in the process (see graphic above).

This enables Neo4j to build and query graph databases that are 10x bigger than anything possible on X64, according to Neo–graphs measured in the hundreds of billions to trillions of nodes. Fraud detection, bioinformatics, and analytics for the Internet of Things (IoT) are the top workloads expected to be enabled by this technology, which will become available later this year.

OK, so you’re not going to casually do some entity analytics against a trillion-edge graph using the spare capacity in your Power8 system. You’ll probably buy a dedicated system to do this work. But the point is that these sorts of big data analytic capabilities are more accessible than you may think.

After all, IBM is adding support for semi-structured formats, like JSON, in DB2 for i and other DB2 flavors. What will it do next? Chances are good, some sort of data analytics is near the top of the list, so it may be a good time to start familiarizing yourself with the technology that’s available, and how you might better leverage your existing data–and your spare CPWs–to compete better in our uber-connected, big data world.

Power Chips To Get A GPU Boost Through Nvidia Partnership

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Do the Math When Looking at IBM i Hosting for Cost Savings

COVID-19 has accelerated certain business trends that were already gaining strength prior to the start of the pandemic. E-commerce, telehealth, and video conferencing are some of the most obvious examples. One example that may not be as obvious to the general public but has a profound impact on business is the shift in strategy of IBM i infrastructure from traditional, on-premises environments to some form of remote configuration. These remote configurations and all of their variations are broadly referred to in the community as IBM i hosting.

“Hosting” in this context can mean different things to different people, and in general, hosting refers to one of two scenarios. In the first scenario, hosting can refer to a client owned machine that is housed in a co-location facility (commonly called a co-lo for short) where the data center provides traditional system administrator services, relieving the client of administrative and operational responsibilities. In the second scenario, hosting can refer to an MSP owned machine in which partition resources are provided to the client in an on-demand capacity. This scenario allows the client to completely outsource all aspects of Power Systems hardware and the IBM i operating system and database.

The scenario that is best for each business depends on a number of factors and is largely up for debate. In most cases, pursuing hosting purely as a cost saving strategy is a dead end. Furthermore, when you consider all of the costs associated with maintaining and IBM i environment, it is typically not a cost-effective option for the small to midsize market. The most cost-effective approach for these organizations is often a combination of a client owned and maintained system (either on-prem or in a co-lo) with cloud backup and disaster-recovery-as-a-service. Only in some cases of larger enterprise companies can a hosting strategy start to become a potentially cost-effective option.

However, cost savings is just one part of the story. As IBM i expertise becomes scarce and IT resources run tight, the only option for some firms may be to pursue hosting in some capacity. Whatever the driving force for pursing hosting may be, the key point is that it is not just simply an option for running your workload in a different location. There are many details to consider and it is to the best interest of the client to work with an experienced MSP in weighing the benefits and drawbacks of each option. As COVID-19 rolls on, time will tell if IBM i hosting strategies will follow the other strong business trends of the pandemic.

When we say do the math in the title above, it literally means that you need to do the math for your particular scenario. It is not about us doing the math for you, making a case for either staying on premises or for moving to the cloud. There is not one answer, but just different levels of cost to be reckoned which yield different answers. Most IBM i shops have fairly static workloads, at least measured against the larger mix of stuff on the public clouds of the world. How do you measure the value of controlling your own IT fate? That will only be fully recognized at the moment when it is sorely missed the most.

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