Architecting solutions for Cluster Computing as opposed to Cloud Computing

Recently, while evaluating storage options as part of a consulting engagement, I came across the Isilion offering from EMC and some of the articles in the associated literature talked about the use of Isilion for cluster computing. Given that the emphasis is still on storage, specifically HDFS, it was intriguing that the possibility of compute functions being delegated to nodes a la Map Reduce was discussed quite a bit. Further reading in to what is considered to be cluster computing got me to the Wikipedia article on Computer Clusters.

So it is quite clear as to what the difference is between cloud computing and cluster computing to the extent that we can even safely say the cluster computing is a subset of cloud computing especially given the offerings from Amazon Web Services such as Elastic Map Reduce and the newly launched Lamda. Hence in this blog article I will focus instead on how a solution needs to be architected to leverage cluster computing effectively to get the best bang for the buck out of cloud computing.

Lets start by addressing the biggest challenge with implementing cluster computing: co-location of data on the compute node. While this is an easy problem to solve while utilizing the Map Reduce paradigm it represents a real challenge to use cluster computing for achieving scalability in the typical usage scenarios. Although the use of technologies such as InfiniBand may be an option in some cases the cost benefit analysis would render it useless for most of the typical business applications.

One immediate option is to utilize microservices based architecture. But it is clear from the description in the seminal article by Martin Fowler that it does not address co-location of transaction data although he does talk about decentralized data management and polygot persistence. Clearly is not really meant to allow for easy adoption in a cluster computing scenario. Interestingly though there is a reference to Enterprise Service Bus as an example of smart end points and that is what got me thinking about extending the concept to cluster computing.

The trick then is to apply the event based programming model to the microservices architecture concept leveraging in turn the smart end points aspect. All the transaction data needs to be embedded in the event combined with any contextual state data. Through the use of interceptors or other adapters the data can be deserialized in to the appropriate service specific representation. This is key since the service need not and actually should not be built to consume the event data structure.

While the approach described above would require you to invest significantly in setting up the requisite infrastructure components to provision compute nodes on the fly to handle events, given the recent release of the AWS Lambda service provides us with an opportunity to apply this concept more easily albeit with some new terminology: microservices are implemented as AWS Lamda functions! It would be very interesting to figure out if argument reduction is supported! Check this blog again in a few weeks to find out…

Virtual Machines, Containers, and now LXD: What’s best for me?

First there were virtual machine instances running on bare metal or para-virtual hypervisors, then came containers allowing for better utilization of virtual machine instances, and now we have the Linux Container Daemon (LXD) pronounced “lex-dee”. As the tag line goes “The new hypervisor isn’t a hypervisor, and it’s much, much faster”; one quickly realizes that hyperboles are not where your troubles end! Instead your choices for virtualization just got trickier to sort out.

So here are some simplifying rules to get going quickly:

1. If you have the luxury of hosting your application entirely on the public cloud, stick to the good old fashioned VMs managed using the auto-scale functionality provided by the cloud platform of your choice.

2. If you have the luxury of creating your private cloud using paid software such as the vCloud suite, stick to the good old fashioned VMs managed using the components of the suite such as vRealize.

3. If you are stuck with the free bare metal hypervisors such as vSphere ESXi or Hyper-V(not entirely free) or with paravirtual hypervisors such as KVM then get the right DevOps skills on your team and use Docker containers managed using Fig for development and Chef for production environments.

4. If you are the brave spirit capable of dealing with the hardcore hardware and low level kernel configuration matters opt for Metal As A Service (MAAS) offering from Canonical combined with Juju.

5. If you are lucky to have the hardware setup exactly as required for running OpenStack/CloudStack and have the chops to customize the provided management apps then by all means rock your world by replicating most of the basic AWS offerings within your datacenter.

6. Finally, if you are truly bored of your mind by all the mundane options listed above and have the distinct air of “Been there – Done that” around you with the appropriate management support within your back pocket venture in to the brand new entrants such as LXD!

Of course you might also just be lucky enough to be in my position: Make recommendations and then sit back to enjoy the show! And, of course, come back to this blog for more …