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Transmission Cost Savings at the Edge


These days, it seems like every edge computing article you read presents edge deployment as the solution for bandwidth and latency issues. A vast multitude of these articles seem to imply – either directly or indirectly – that addressing bandwidth and latency hurdles will result in satisfied, and presumably more, end-users. On the surface, this is of course, true for the most part. So why, you may ask, is the adoption timeline of edge computing a little slower than what many pundits have been breathlessly expecting? It’s been posited that like many of its technological predecessors, the rocket fuel everyone has been waiting for is the coveted “killer app”. For the more financially focused organization, this level of edge trepidation is the lack of a compelling business case. But what if the killer app was the primary element of the business case itself? In other words, the holy grail for edge deployment is its ability to dramatically reduce transmission costs.

For many providers of cloud-delivered offerings, gaming companies for instance, the ability of their end-users to instantaneously lay waste to an opponents’ fortified stronghold is of obvious importance. The unseen aspect of satisfying thousands of controller-crazed customers is that it costs a lot of money to do so, particularly the cost of transmission. The prevailing mode of delivering an application involves performing all of the required computing in one or more centrally or regionally located data centers with all transmissions back to each locality made via high bandwidth connections. These transmissions, along with their required connectivity, are collectively referred to as “backhaul”.

Backhaul, or more specifically, the cost of the high bandwidth transmission lines required, is traditionally the largest cost element when planning for the most effective mode of service provision. Edge computing transforms the planning equation through its ability to offer organizations “backhaul bypass” capability. To demonstrate the massive impact of edge delivered backhaul bypass capability to the traditional transmission cost model, let’s take a look at the impact made in a Virtual Reality scenario in a major U.S. market.

As you might have guessed, the number of transmission lines required to support a local application like VR, back to a central or regional data center, is driven by the volume of data that must be transmitted and processed to maintain an acceptable level of performance. And, since transmission lines are not currently available for free, backhaul cost is the aggregate of the number of lines required to provide the necessary capacity.

Using the calculator below, we see that if 25% of New York’s population were taking a virtual tour of the Grand Canyon simultaneously, we are talking about a user community of approximately 2.2 million. Assuming that each headset was operating at the recommended level of 25 GBPS our virtual canyon visitors would require enough bandwidth capacity to support almost 14 million GBPS. To determine our backhaul cost, we multiple the number of 100 GBPS equivalent circuits required to support bandwidth requirements by a national average of $1950 per circuit. The end result is a monthly backhaul cost of $536,250. If you’re saying “that’s a lot of money”, you’d be right.

The Impact of Edge Data Centers and Backhaul Bypass
Edge data centers substantially alter the cost model for supporting high bandwidth applications. The reason for this equation altering effect is that the number of high-cost circuits leading back to a single data center is reduced by moving the majority of compute and storage closer to the end-user community. The cost benefit of using edge data centers for the purpose of backhaul bypass is considerable for our New York scenario.

By delivering the ability to “localize” a large portion of compute and processing functionality, edge data centers change the economics of our VR application dramatically by reducing the number of 100 GBPS equivalent backhaul circuits by a factor of 10. Using our same figure of $1950 per circuit the total backhaul cost is approximately $26,175 per month (v. $536,250). The backhaul bypass savings make a substantial contribution to a provider’s bottom line.

Summary:
In accessing the level of cost reduction that can be derived by decreasing the number of transmission circuits it appears that the emphasis on what will drive edge computing has been misplaced. While the emphasis has been placed on the business and lifestyle transforming potential of applications residing within edge data centers and corresponding decreases in service impacting factors like latency, the “killer app” that will drive the edge is actually something a little more mundane—but CFO’s will love it. Edge computing’s ability to drive down a significant cost element by providing backhaul bypass capability is an immediate, as opposed to envisioned, benefit to cloud and SaaS providers. In other words, the catalyst for the growth of edge computing isn’t something we’re waiting for, it’s been with us the whole time.

If you’d like to see the impact of backhaul bypass using other applications in other major cities use our Backhaul Cost Calculator.


Keith Rutledge

General Manager

Keith Rutledge General Manager for Edgepoint at Compass Datacenters. Compass Datacenters Edgepoint is a zero touch Edge datacenter solution at scale. Keith is responsible for client engagements for Edgepoint.

In a long career at IBM, Keith was a printed circuit board designer, a software developer, a product manager, and a sales executive. As a product manager, Keith wrote The Business Case for Java and edited The Business Case for eBusiness. He ran Central Europe Middle East and Africa for IBM’s AS/400 product and was Worldwide Director of Sales for the AS/400. Keith led a strategic outsourcing practice in China for IBM.

At Sirius, Keith was responsible for partners and routes to market before becoming Director of Sales and General Manager for Sirius Southeast.

He also had a long military career in Special Forces, retiring in 1999 as a Team Sergeant from 19th Special Forces Group.

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