Is Edge Computing Right For Your Business?
By Sandeep Bhargava, Managing Director of Asia Pacific Japan (APJ), Rackspace
Telcos in Singapore have been experimenting with 5G testing in recent years, with 5G set to enable edge computing to help organisations act on insights closer to where the data is created.
Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.” Edge computing augments and expands the possibilities of today’s primarily centralised, hyperscale cloud model, supports the systemic evolution and deployment of the IoT, and supports entirely new application types, enabling next-generation digital business applications.”
Processing data closer to where it is collected makes huge sense from a cost, speed and security point of view. Unfortunately, edge computing is in real danger of becoming the new “blockchain” of enterprise tech: over-hyped and underutilised. Marketers and analysts have distracted many organisations from thinking clearly about edge computing, by talking them into believing every company needs to have an edge strategy. They don’t.
So it’s time to hit reset. Because approached in the right way, the edge computing philosophy, and the outcomes it encourages, can drive powerful transformations in local companies with data and drive effective process for decision-making.
Businesses are already generating huge amounts of data, with exponential growth every year as more devices come online — including many items that previously had no business being anywhere near the internet: toothbrushes, doorbells, coffee machines, smart speakers, watches, and – infamously – juicers. As the World Economic Forum reports: “The world produces 2.5 quintillion bytes a day, and 90% of all data has been produced in just the last two years.”
This has coincided with the growth in hyperscale cloud adoption. Edge computing solves today’s cloud-era challenges around moving and managing data to ensure it generates the most value for the minimum of cost, and that it can be processed and acted on in a timely fashion.
Edge’s advantages can be summarised as follows:
- Lower latency, since it’s quicker and more reliable to send data to a local point-of-presence than it is to send it to a hyperscale cloud provider’s data centre.
- Decreased bandwidth use and associated costs.
- Decreased need for large server resources and associated costs.
- Data which it previously wasn’t feasible to send to a centralised data centre can now be analysed to drive new functionality or insights.
To put the philosophy to work and unlock these advantages, we first need to define the foundational elements of edge computing.
Illustrative edge computing use cases include sensors in a factory or medical setting, a remote oil pipeline, or surveillance cameras designed to recognise security threats. The high volume of data generated by these devices drives time-sensitive decisions or actions. It simply wouldn’t be ideal or cost effective to send it all to the cloud for processing and storage.
In cases like these we’re looking to create the fewest number of hops between the device generating the data and the first – but not the only or last – “thing” that will do something with that data.
On this basis, the key components of an edge computing network are the public cloud, compute edge, device edge and the sensor.
It is clear that many enterprise models typically encompass at least two of these things – cloud plus one other – whether you formally recognise them as having an edge component or not. So it’s a diversion to focus too much on “edge,” or to hype edge computing as inherently and automatically transformative. Instead, the intended business outcome – and the practical application of technology to deliver on it – should always be your strategic driver. Not edge computing for edge computing’s sake.
To understand the extent to which you need to integrate an edge computing philosophy into your tech strategy, you first need to identify your data types. How critical is your data? Is it time-sensitive? How big is it?
You also need to understand the level of compute required. Processing data in the cloud can quickly get expensive. So can pulling it back out or making repeat queries if your initial ones are wrong or need following up on. So do you really need to send it to the cloud? And even if you can afford to, is the latency involved acceptable for your use case?
Edge computing is not just a computing challenge either. It’s often about short- or medium-term storage of data for reuse, for example. So the level and location of storage you need requires careful consideration.
Another key question, and one that demands some creative thinking, is whether or not processing your data nearer to the point it is generated would unlock new functionality for your business or customers.
Then, as always, there’s the need for security. While an edge solution is as secure as any other system, if it’s well designed, the biggest consideration is the increase in exploitable attack vectors that comes with a proliferation of edge devices. Most edge-specific security issues relate to poorly implemented and maintained code on those devices. There have been instances of these being hijacked into botnets for orchestrating distributed denial-of-service (DDoS) attacks, for example. You need to adopt a security-first mindset, but that’s no different to working with any other technology.
A note of caution, however. Services running in non-centralised locations for no good reason are not an edge computing challenge or strategy. That’s just things in the wrong place. Think about an important intranet site which started small but now contains essential documentation. That really should be moved into the redundant, centralised cloud-provider space and out of the local communications room. The same goes for mail servers and most classes of object storage.
In the case of edge computing, every company should start by asking itself, “is my data in the right place?” rather than “what is my edge computing strategy?”
As data volumes are growing, more companies are beginning to think harder about where to put data for optimal costs and performance. And the importance of a solid data strategy continues to grow as the IoT revolution forges ahead and more devices are brought online, fuelling an explosion in data processing and storage requirements.
If you’re one of those companies, to get to the right answers about your data processing requirements and where that should be happening, you first need to ask the right questions.
The answer you get won’t always be edge computing. And that’s OK, no matter what anybody else is doing.