There is an overarching fear that artificial intelligence (AI) and machine learning will take over people’s jobs, but a counterargument is that their main purpose is to support humans as enabling technologies. In their proponents’ viewpoint, they aren’t disabling anyone. Organizations that don’t train their staff now to learn new skills, however, may find themselves left behind. This includes IT, which is of increasingly strategic importance to most organizations today. Both technologies are becoming a fundamental part of our lives, and with the advent of semiautonomous and autonomous vehicles, they’ll become more so—both in consumer and enterprise applications.
SD-WANs are good at the branch-office level, but as technology progresses, data volumes will increase and the time to intelligence must shrink. Although SD-WANs are great for low-bandwidth applications, high-bandwidth ones need a different approach to move ever larger amounts of data.
During its transit, much of this data will pass through or be stored in a data center. Data centers therefore need to constantly update their skills and network capabilities. Yet doing so may not require organizations to completely abandon their existing facilities for new ones. That’s because even AI and machine learning can make what you already have more efficient. They can also ensure that you avoid risks and disruptions caused by human error.
Humans make mistakes—that’s part of our nature. Manmade anomalies could, for example, lead to unexpected network downtime. Poor manual configuration of a wide-area network (WAN) could cause this issue. Thankfully, the concepts of AI and machine learning in IT networking are not science fiction. Rather than making us weaker, they can make us stronger and enable us to increase our performance. They’re no Armageddon; they’re an enabler that can permit organizations to do more with fewer resources.
The science fiction of autonomous networking—which is discussed by David Hughes, Founder and CEO of Silver Peak Systems, in a sponsored article at Network World—is already here today in solutions such as PORTrockIT and WANrockIT. Such products can correctly mitigate the effects of latency without forcing an organization to unnecessarily spend money on increasingly large bandwidths, WAN optimization and SD-WAN solutions. Using AI and machine learning, you can achieve much with you already have, and an ever larger pipe won’t defeat the laws of physics no matter how much you spend. The problems that latency creates will still remain.
Hughes says that many enterprises are using SD-WAN solutions to connect employees consistently and securely to cloud and data center applications, but by themselves, they don’t provide any optimization to enhance the data flow. You must add WAN optimization, as many of the SD-WAN providers do. But with security concerns requiring that encrypted data and rich media be an increasing part of the data mix, they provide little or no performance improvement. Hughes is nevertheless right to explain that automation is playing a role in SD-WANs to eliminate many of the repetitive and mundane manual steps, which are essential to configuring and connecting remote offices.
He believes it has limitations though: “Automation…is not sufficient to translate high-level business goals or intent into specific actions across the network, and automation is not good at dealing with the many unanticipated situations across production WAN deployments.” In his view, these are areas where machine learning and artificial intelligence can play a role. With machine learning, WANs can be directed to adapt to changing environments without human intervention.
AI and machine-learning techniques permit us to better manage and cope with the ever growing data volumes, too. Clint Boulton, Senior Writer at CIO, talks about freight-forwarding company JAS Global in his article How Logistics Firm Leverages SD-WAN for Competitive Advantage. He refers to the company as taking a gamble on an unknown technology.
The firm is using an SD-WAN to run cloud applications, but hopes to use it as the backbone of a predictive-analytics strategy to expand its business. The claim is that JAS Global managed to cut millions of dollars from its bandwidth costs. That’s good.
Boulton also explains, “SD-WANs allow companies to set up and manage networking functionality, including VPNs, WAN optimization, VoIP and firewalls, using software to program traffic routing typically conducted by routers and switches. Just as virtualization software disrupted the server market, SD-WANs are shaking up the networking equipment market.”
He’ll discover, as many on the big data path have also, that the volumes of data start to increase exponentially. The need to gather data from further afield at an increasing rate makes SD-WAN limitations begin to bite. There will also be a need to invest in larger bandwidths and data-acceleration techniques. What’s certain is that data acceleration makes big data and predictive analytics increasingly viable. Machine learning can help us humans to understand what the data is telling us. Latency, on the other hand, can lead to inaccurate data analysis.
Go Beyond Hype
To me this just sounds like hype—particularly since WAN optimization won’t necessarily increase WAN performance as it should do. On the other hand, data-acceleration solutions can increase performance. Your data centers and disaster-recovery sites need not be situated in the same circles of disruption. Boosted by machine learning, they can reside thousands of miles apart, and because the transmitted data is encrypted, it’s highly secure. The analysis of the network’s performance happens in real time too, eliminating the risks of being reactive as opposed to being active.
Managing network performance, protecting your data, mitigating latency and reducing packet loss needn’t be the gamble that Boulton discusses. Mark Baker, CIO of JAS Global, felt he had to embrace SD-WANs because his company was already supporting global applications and email with MPLS networks and VPNs. The costs of running an enterprise-resource-planning (ERP) system over them caused him to worry, though. The ERP software required less than 150 milliseconds of latency. “Setting up and provisioning an MPLS system also takes several months,” says Boulton. Baker was therefore drawn to SD-WANs from Aryaka.
This situation is fine, but organizations should also look beyond SD-WAN to a data-acceleration solution, as it can do more for less. Baker would probably have achieved many of his goals more quickly and more simply using data acceleration to address the latency challenge of having a global company “go from Atlanta to L.A. to London and Paris.” He adds, “But when you start talking about going across the pond or [to the northern] and [southern] hemispheres, you will find there is a huge latency challenge to overcome when you’re lacking a traditional MPLS network.” Using AI and machine learning, such a challenge is minimized—and that’s simply because machines can support humans effectively, and sometimes outperform them. With machine learning behind data acceleration, you’ll always be a step ahead, too.
About the Author
David Trossell is CEO and CTO of Bridgeworks.