Big Data Journal Authors: Yeshim Deniz, Pat Romanski, Elizabeth White, Trevor Parsons, Adrian Bridgwater

Blog Feed Post

What networking can learn from CPUs

The rapid growth in compute demand is well understood. To keep up with accelerating requirements, CPUs have gone through a massive transformation over the years. Starting with relatively low-capacity CPUs, the expansion of capability to what is available today has certainly been remarkable – enough to satisfy even Gordon Moore. But keeping up with demand was not a matter of simply making bigger and faster chips. To get more capacity, we actually went smaller.

As it turns out, there are practical limitations to just scaling things larger. To get more capacity out of individual CPUs, we went from large single cores to multi-core processors. This obviously required a change in applications to take advantage of multiple cores. The result is a distributed architecture and the proliferation of “scale out” as a buzzword in our industry.

From an application perspective, the trend continues. Applications that require performance continue to move to multi-tiered applications that are distributed across a number of VMs. This is true for massive web-scale applications like Facebook, but also for other applications like MapReduce.

To get bigger, we get smaller

The technology trend is clear: to get more output, move to smaller blocks of capacity, and coordinate workloads across that capacity.

If this is true, then the future will be lots of small pools of resources that rely on the network for interconnectivity. As applications become more distributed, then performance between these pools becomes even more critical. Even small amounts of pool-to-pool latency can aggregate up into significant impacts, either because of interesting failure conditions with asynchronous operations or because of the cumulative performance impact.

As interconnectivity takes a larger role, we should expect the discussion of commoditization of network resources to expand. Today, there is a strong argument around commoditizing the switch hardware (largely via merchant silicon) and the switch operating system (through players like Cumulus, Big Switch, and Pica8). But massive distribution will require both a commoditized interconnect and a commoditized orchestration platform.

On the latter, it would seem that OpenDaylight is poised to lead the charge. With an industry-backed open source solution, it will be difficult to justify premium control products, which should be sufficient in driving that aspect of the solution towards commodity. But that still leaves the interconnect piece unaccounted for.

Getting to a cheaper interconnect

There is probably a case to be made for leaf-spine architectures here, but if the number of servers continues to expand, there are some ugly economics at play. Scaling out in a leaf-spine architecture requires scaling up at the same time. As the interconnect demands increase, the number of spine switches increases. You eventually get into spines of spines, which starts to look an awful like like traditional three-tier architectures.

The sheer number of devices and cables drive the cost unfavorably. And when you consider the long-term operational costs tied to power, cooling, space, and management, it’s unclear where the budgetary breaking point is. Beyond just the costs, the other issue here is that every time a new layer is added, you add a couple of more fabric switch hops. If application performance is based on both capacity and latency, then every time you add switch hops, you incur a potentially heavy performance penalty.

At some point, you need to move away from multi-hop connectivity through the fabric.

Moving away from multi-hop fabrics

Instinctively, we already know this. There is already a tendency to rack gear up in close proximity to other gear to which it is tied. You might, for example, balance Hadoop loads across a number of servers that are in the same rack. Essentially, what we are doing in these cases is acknowledging that proximity matters, and we are statically designing for it.

But what happens when things aren’t static?

In a datacenter where applications are portable across servers, the network capacity cannot be statically planned. And as application requirements change (often dynamically as load changes), then the network capacity demands will also change. This requires an interconnect that is both high in capacity and dynamic.

This problem is slightly different than the compute problem. On the compute side, it was enough to free up resources (or create additional ones) and then move the application to the resource. In this case, the application is fixed, which means the capacity has to move to the application. When capacity is statically allocated, this poses a problem.

The bottom line

The only solutions here are to either over provision everything, or move towards a dynamic interconnect. The first is counter to the trends we learn from compute – make things smaller and more distributed. In this case you get out of the problem by paying for it. The question is whether this flies in the face of all the commoditization trends. What good is commoditizing something if the end solution requires buying a ton more? You would have to see cost declines match capacity increases, but this seems unlikely as there is no upper limit for capacity whereas cost will asymptotically approach some profit threshold.

If the trends in compute and storage hold true for networking, then the current trajectory of some networking solutions will need to change. Learning from the past is a great way to shape the future.

[Today’s fun fact: Lobster was one of the main entrees at the first Thanksgiving dinner. They also had Cheddar Bay Biscuits I think.]

The post What networking can learn from CPUs appeared first on Plexxi.

Read the original blog entry...

More Stories By Michael Bushong

The best marketing efforts leverage deep technology understanding with a highly-approachable means of communicating. Plexxi's Vice President of Marketing Michael Bushong has acquired these skills having spent 12 years at Juniper Networks where he led product management, product strategy and product marketing organizations for Juniper's flagship operating system, Junos. Michael spent the last several years at Juniper leading their SDN efforts across both service provider and enterprise markets. Prior to Juniper, Michael spent time at database supplier Sybase, and ASIC design tool companies Synopsis and Magma Design Automation. Michael's undergraduate work at the University of California Berkeley in advanced fluid mechanics and heat transfer lend new meaning to the marketing phrase "This isn't rocket science."

Latest Stories from Big Data Journal
Enthusiasm for the Internet of Things has reached an all-time high. In 2013 alone, venture capitalists spent more than $1 billion dollars investing in the IoT space. With “smart” appliances and devices, IoT covers wearable smart devices, cloud services to hardware companies. Nest, a Google company, detects temperatures inside homes and automatically adjusts it by tracking its user’s habit. These technologies are quickly developing and with it come challenges such as bridging infrastructure gaps,...
There are 182 billion emails sent every day, generating a lot of data about how recipients and ISPs respond. Many marketers take a more-is-better approach to stats, preferring to have the ability to slice and dice their email lists based numerous arbitrary stats. However, fundamentally what really matters is whether or not sending an email to a particular recipient will generate value. Data Scientists can design high-level insights such as engagement prediction models and content clusters that a...
Whether you're a startup or a 100 year old enterprise, the Internet of Things offers a variety of new capabilities for your business. IoT style solutions can help you get closer your customers, launch new product lines and take over an industry. Some companies are dipping their toes in, but many have already taken the plunge, all while dramatic new capabilities continue to emerge. In his session at Internet of @ThingsExpo, Reid Carlberg, Senior Director, Developer Evangelism at salesforce.com, t...
There is no doubt that Big Data is here and getting bigger every day. Building a Big Data infrastructure today is no easy task. There are an enormous number of choices for database engines and technologies. To make things even more challenging, requirements are getting more sophisticated, and the standard paradigm of supporting historical analytics queries is often just one facet of what is needed. As Big Data growth continues, organizations are demanding real-time access to data, allowing immed...
Cloudian on Tuesday announced immediate availability of Cloudian HyperStore appliances and Cloudian HyperStore 5.0 software. Flash-optimized, rack-ready HyperStore appliances make it easy to economically deploy full-featured, highly scalable S3-compliant storage with three enterprise-focused configurations. HyperStore appliances come fully integrated with Cloudian HyperStore software to assure unlimited scale, multi-data center storage, fully automated data tiering, and support for all S3 applic...
Scene scenario: 10 am in a boardroom somewhere, second round of coffees served, Danish and donuts untouched, a quiet hush settles. “Well you know what guys? (and, by the use of the term guys I mean to include both sexes here assembled) – the trouble that we have as a company is that we are, to put it bluntly, just a little analytics poor,” said the newly appointed Chief Analytics Officer. That we should consider a firm to be analytically deficient or poor is a profound comment on our modern ag...
Cloud and Big Data present unique dilemmas: embracing the benefits of these new technologies while maintaining the security of your organization’s assets. When an outside party owns, controls and manages your infrastructure and computational resources, how can you be assured that sensitive data remains private and secure? How do you best protect data in mixed use cloud and big data infrastructure sets? Can you still satisfy the full range of reporting, compliance and regulatory requirements? I...
Gridstore has announced that NAC, Inc. and Sky Tech have joined its innovative Accelerate Partner Program. Both new members cite Gridstore's expertise in enabling the Hybrid Cloud and their solution purpose-built for Hyper-V as the key criteria for their decision to join the program. Integrating seamlessly with business clients, these new partners provide industry-proven storage solutions that promote satisfied customers, profitable businesses, and communities that thrive.
General Electric (GE) has been a household name for more than a century, thanks in large part to its role in making households easier to run. Starting with the light bulb invented by its founder, Thomas Edison, GE has been selling devices (“things”) to consumers throughout its 122-year history. Last week, GE announced that it is officially leaving that job to others. While the lighting division will stay, GE will now turn its attention to selling industrial machinery and analytics as a service t...
It's time to condense all I've seen, heard, and learned about the IoT into a fun, easy-to-remember guide. Without further ado, here are Five (5) Things About the Internet of Things: 1. It's the end-state of Moore's Law. It's easy enough to debunk the IoT as “nothing new.” After all, we've have embedded systems for years. We've had devices connected to the Internet for decades; the very definition of a network means things are connected to it. But now that the invariable, self-fulfilling prop...