Some things are simply better together. Like Han Solo and Chewbacca. Or honey ham and Swiss cheese. Each of them is great on its own, but something special happens when you put them together. SD-WAN and UCaaS are one of those combinations that is greater than the sum of its parts, but companies typically don’t see them as a dynamic duo. Companies are typically either mulling over the idea of upgrading their outdated WAN to an SD-WAN … or they are thinking of upgrading their voice systems to a Unified-Communications-as-a-Service (UCaaS) solution. These are typically seen as two completely separate technology issues, but doing one without the other would be a missed opportunity for companies’ cloud strategies because of the way they enhance one another.
The benefits of doing these two implementations together are particularly dramatic for mid-sized companies because of the legacy technologies that they are usually upgrading from. Unlike large enterprises, which are typically pretty far down the path of cloud infrastructure investments, the 200,000 mid-sized companies in the U.S. are generally starting from much further behind:
- The underlying infrastructure they are using is often traditional WAN technology, which may quite literally be decades old if the company has a long history. Because it is technology that predates the Internet, let alone the cloud, a traditional WAN is highly problematic as a foundation for companies’ web-based apps, mobile computing and other cloud-related technologies. Simply put: performance is terrible, flexibility is non-existent, and maintenance is treacherous for the IT teams. Outdated WANs create problems that are very difficult to ignore, prompting companies to take a close look at a migration strategy for moving to a cloud-friendly SD-WAN.
- The voice systems that many mid-sized companies have are often equally outdated, since voice systems are typically at the bottom of the priority lists as companies with limited IT budgets weigh the most urgent systems to invest in. As a result, many companies’ voice systems pre-date the emergence of the cloud or simply utilize VOIP in a narrow way for cost savings without addressing their larger communications needs. Compared to the true cloud-based unified communications systems that large enterprises and small businesses use, mid-sized companies are often a decade or more behind, with voice systems that are a patchwork of technologies and fixes bolted together to try to get one more year out of the system, followed by hope that it will last just one more year, and one more year, and so on. This typically results in poor voice performance, lack of integration with other corporate systems, employees supplementing the system with rogue solutions that introduce security and compatibility problems, and many other issues.
Upgrading to an SD-WAN or making a UCaaS implementation each has major benefits. For example, a UCaaS implementation can instantly transform obsolete, costly-to-maintain, difficult-to-expand legacy voice and messaging systems into an integrated combination of web-based voice/messaging/video tools that enhance everything from uptime to customer experience to productivity. And an SD-WAN implementation reduces the complexity and downtime and optimizes the costs that so many mid-market companies experience with their outdated traditional WANs, which are poorly suited to support the diverse set of cloud applications that companies are running today. SD-WAN provides a dynamic, adaptable, intelligent foundation for managing all of that traffic in a way that gives each application what it needs to perform optimally while making cost-conscious decisions about how to utilize various grades of bandwidth.
SD-WAN and UCaaS each have a major positive impact on its own, but together they are much greater than the sum of their parts:
- By implementing an SD-WAN as a central element of a cloud strategy, a company has an “application-aware, cloud ready” network that can actively manage how bandwidth is sourced and utilized, routing traffic in ways that optimize the performance of every application.
- And the SD-WAN’s dynamic management of bandwidth ensures that the UCaaS gets prioritized so that employees have high-quality voice and video tools that are reliable for customer communications and other external and internal collaboration.
- With the SD-WAN as the foundation, the UCaaS can perform to the specifications it is designed for, while also allowing the company to manage broadband/network costs effectively by using higher-cost services only when needed to ensure application performance while utilizing lower-cost, commodity services whenever and wherever it can to save money.
- In return, the UCaaS implementation provides a clear blueprint for how the SD-WAN should be designed and managed, and the unified communications applications become Exhibit A for how the SD-WAN implementation is having a positive operational impact on the organization.
For mid-sized companies, this kind of dual implementation not only addresses two sources of daily operational frustration — poor performance of the network and an outdated voice system — but can also give much-needed clarity and direction to the company’s cloud strategy. Together, these implementations can serve as a centerpiece of a cloud strategy, delivering benefits that will be easy for the entire organization to see in ways ranging from integrated cloud-based voice tools to far faster network performance. And with these wins in hand, it may even be easier to build support for the next phases of the company’s cloud strategy.
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Businesses and business units of all sizes can benefit from cloud computing, but many don’t want the cost, performance, and governance concerns of public cloud nor the complexity and operational overhead of building their own private clouds. Today, some cloud vendors are using artificial intelligence (AI) to simplify private cloud deployment and management, making it possible for clouds to be self-driving (e.g., self-installing, self-healing, and self-managing). In this article, we’ll look at the requirements for a self-driving cloud.
Self-Driving Cloud Requirements
Just like any other technology in this space, one needs several systems to work well together, do self-monitoring, healing, learning and to create models for self-optimization. Here is a list of technologies that need to be present for self-driving clouds:
- ‘No Day 0’ — Automatic install and configuration: The first step is an install process that does not require much human intervention. The building blocks for a cloud are servers, storage, and networking. With hyper-converged systems, servers and storage are combined and one needs software-defined networking to minimize the reliance on physical network changes. So, the first requirement is a server + storage building block with all the software pre-installed and baked into the operating system image. You just need to image a few servers and power them on. Once that is done, the cloud should come up automatically without admins knowing anything about various services and their persistent stores. The image software should pool together servers, storage, and networking resources to create a highly resilient cloud.
- Integration with other clouds and internal systems: A cloud is not supposed to work in isolation, so one should be able to quickly connect it with existing virtualized infrastructure and other public clouds. Even better would be to add your existing storage systems and make them part of this cloud through open (e.g., representational state transfer or RESTful) APIs. This is an optional step, but it’s critical if you want to leverage your existing investments in storage and servers. Similarly, most customers want to integrate with AD/LDAP as well to have a single source of users and authentication.
- Deploy applications in a self-service manner: The goal for any cloud is to provide you with an IaaS and PaaS platform that can be consumed by various teams in a self-service manner. For example, developers can use it for application development, continuous integration / continuous development (CI/CD); support teams can use it to bring up replicas of customer environments to troubleshoot any support issues; sales can bring up quick PoCs for trial and finally IT can bring up staging or production deployment of various applications. These steps need to be fully automated, so that one can repeat them without spending too much time. Any cloud solution should provide a self-service interface with pre-built application templates for quick deployment.
- Real-time monitoring for events, stats, logging, and auditing: Since cloud is a shared environment, one needs to be able to monitor various events, stats, and dashboards in real-time. This is required to know the state of applications and what actions other users have performed. IT should be able to get logs and audit the action of all users. For example, if a service was down since 10 p.m. last night, it is good to know if a user or script mistakenly shut down a VM providing that service.
- Self-monitoring and self-healing: Any system as complex as a cloud needs to monitor all the critical services and help monitor the workloads. If any hardware component or software service fails, the system should detect and fix the situation. Then, it can alert the admin as to which component had failed. If this was a hardware component like a server, hard disk, SSD, or NIC, the admin could take corrective action to restore the capacity of the system. This is an absolute minimum requirement for a self-driving cloud.
- Machine learning for long-term decision making: Since the self-healing layer takes care of short-term decisions, we need another layer of automation that can observe the cloud and applications over a longer period to help optimize the cloud, improve efficiency, and plan for future. A self-driven cloud platform collects telemetry or operational data and leverages machine learning to guide data scientists how to develop algorithms that now model this behavior. The algorithms help customers make decisions.
This layer should observe the usage to do prediction capacity modeling and order new servers. It should also determine what sort of servers to add in terms of their CPU, memory and IO ratio. For instance, if the applications are more CPU-intensive, one should order servers with more cores and less storage. Another area is to help optimize the size of VMs based on utilization. Customers pay for peak capacity on public clouds, but the average utilization is less than 15% in most cases. At that point, you are paying five times the cost that you would pay in a private environment if you consolidated the workloads. All these savings can be passed to you instead of cloud vendors keeping them. A learning system can also help you detect any anomalies in your environment.
For example, you might notice that suddenly your VM was sending a lot of data to other public IPs. This was a result of the machine getting hacked by a bot, and any such security risk can be detected using a smart anomaly detection system. The list of learning-based algorithms can get long, but the key is to have a platform where these can be easily added over time.
‘Hands Free’ Upgrades!
Upgrading a cloud is like changing the tires on a running car. Admins spend a lot of time dreading it and finally doing it. With a live cloud running a variety of workloads, it is critical that the upgrade process be completely handled by an intelligent software layer, and not by humans who are reading release notes from vendors to figure out the right path to upgrade for their environment.
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