A few years ago, AWS announced the release of their Scheduled Reserved Instances. These reserved instances are designed for workloads that recur on a daily, weekly, or monthly schedule, and are purchased for a one-year term. AWS says that Scheduled Reserved Instances provide a 5-10% savings over On-Demand instances used for this same purpose.
While we always appreciate ways to save on AWS, there are a few reasons that Scheduled Reserved Instances are unlikely to make a useful addition to your toolbox when compared to other cost-savings options available.
First of all, they have a decidedly limited use case, only for predictably scheduled operations that will go on for at least one year. Many companies would need to see a much higher savings rate than 10% with this year-long commitment when looking at AWS on demand vs reserved.
Secondly, they are inflexible. Once you set a schedule, you cannot change or override it, and the options to set schedules are limited to daily, weekly, or monthly recurrence on a set duration. Since one of the main benefits of cloud is the ultra-flexibility you get on short notice, this might be a deal-breaker by itself.
Additionally, as Beth Pariseau pointed in a TechTarget article, additional management overhead is required to manage every additional type of instance that a company leverages.
Note that Scheduled Reserved Instances are also limited by region – only available in US East (Northern Virginia), US West (Oregon), and Europe (Ireland) regions – and by instance type, currently supporting C3, C4, M4, and R3 instance types. This means that modern versions of those EC2 instance families, like M5, M5a, M5n, M6g, C5, or C5n, are not available for scheduling, so you’re using an older version of those EC2 instance types. While this might not matter much now, the list of usable instance types has not been updating along with the on-demand instance types, which means this problem will only get worse with time.
When compared to standard AWS reserved instance pricing, you’re not really getting the savings you’d expect from this kind of commitment. Reserved Instances typically save 30% for a convertible 1-year purchase, and can be about 60% for a standard 3-year purchase. This means that if you have an EC2 instance you need that would match the scheduled reserved instance but are using that same EC2 size for other workloads throughout the month, then the non-scheduled EC2 reserved instance pricing works much better with more savings.
For your recurring workloads, you can run instances only when you need them but maintain flexibility by using ParkMyCloud to schedule on/off times for On-Demand instances. By keeping workloads on just during business hours, you’ll save 65% using ParkMyCloud, with even more savings achievable if you need that instance even less throughout the month (and doesn’t even account for savings achieved through RightSizing). This beats any AWS RI pricing while maintaining flexibility for your organization. Keep this in mind while you are evaluating your reserved instance vs on demand decisions.
See the chart below for a full comparison of using Scheduled Reserved Instances vs. using ParkMyCloud.
This comparison shows that AWS Scheduled Reserved Instances are unlikely to be worth any effort or investigation by your cloud operations team. ParkMyCloud provides more benefits and much higher savings with more flexibility and less commitment. Even standard AWS EC2 Reserved Instance pricing and savings can give you more bang for your buck.
If you’ve got workloads and servers that don’t need to run very frequently throughout the month, but you need to ensure they can be spun up at a moment’s notice, then ParkMyCloud can help you save money and enable your users for maximum cloud efficiency. Give ParkMyCloud a try for yourself – start seeing savings today.
As we lurch into the roaring twenties we thought it might be interesting to consider what lies ahead in the ‘exciting’ world of cloud governance. We are unsure exactly when ‘shadow IT’ reached ‘peak sprawl’ but it was likely at some point in the middle of the twenty-tens. What seems certain is that when it comes to IT governance there is still the same need to balance the benefits of agility and speed which come from decentralization, against key business risks be they security and/or cost management.
One thing for sure is that what is encompassed within cloud governance very much depends on where you sit within an organization. Microsoft has produced some interesting content on these different perspectives, which they have boiled down into a notional ‘Five Disciplines’. From our own perspective, we probably think mostly around cost management and cost optimization. Obviously, if you sit within a security related function within a company or are a vendor of security tools, cloud governance means something quite different. The other factor impacting perspective is where you stand on the so-called ‘cloud journey’. If you are still working on migrating your first workloads to the cloud you will have a completely different outlook than if you have been in the cloud for the last 10 years and built your entire business model from the ground up in the public cloud.
Now that we are in 2020, what does this all mean? The cloud world is full of predictions but one often cited that caught our eye is that in 2020 some 83% of enterprise workloads will be in the cloud with approximately half of these being in the public cloud (AWS, Azure and GCP for example). The growth in public cloud over the last decade has been enormous and with it a management task that has moved beyond a scale that humans can manage. Automation has been part of the cloud since its inception, but the move to automated governance has begun and without a doubt will continue to accelerate in the coming years. Be it automated, from cloud guardrails which prevent misconfigurations which enable malicious attackers to penetrate what was considered well protected systems, to automated cloud cost control which automatically schedules resources to be available when required (and off when not) or adjusted to the rightsize to meet the needs of the workload. It’s also not just the infrastructure layer that’s going to get automated as new tools emerge including application resource management, which enables the entire application stack to be automated using software.
In reality, most of what is termed ‘automation’ in the world of cloud governance in 2020 are in truth recommendations which are then manually implemented or for those who are able to align with internal process some kind of semi-automation such as the use of policies. These often still require sophisticated workflows, approval processes and sign-offs from operations and business owners. Few organizations have moved to fully automated governance actions were, in essence, the machines are being used to manage machines. Just as with the move towards autonomous vehicles, driver augmentation via adaptive cruise control, lane-centering, etc. is now considered almost standard on new cars, and so is at least some level of automation in governance is becoming a standard requirement. Being delivered a list of hundreds of recommendations in the last decade was considered a vast improvement on the status quo. In the next decade, these recommendations will likely increasingly become invisible as infrastructure optimization is managed in an ongoing and continuous manner and will require little or no human input.
The range of governance tasks to be automated is also likely to grow. We can already observe the way cost management is increasingly being automated and our own customers are getting comfortable with more ‘set it and forget it’ automation processes based on policies they define. Teams anxious about cloud security are turning to a growing market of automation tools that cover off monitoring, compliance, and threat management and remediate these issues in real-time.
For sure there is a lot of headroom when it comes to automating governance and we look forward to seeing where we land by 2030.
After hearing a lot of buzz about this concept in AI, we decided to see what’s next for robotic process automation. The promise of the technology is that it can automate processes that employees are doing manually, saving your employees’ time and potentially reducing operational costs. While robotic process automation (RPA) interest has been high for a while, actual adoption is now catching up and will only continue to grow in the future. Organizations are understanding the power of process automation, so in turn, more industries are expected to deploy more RPA bots to eliminate manual repetitive actions performed by employees.
RPA software is en route to becoming a billion-dollar category in 2020. Last year, Gartner projected that spending on RPA software was expected to hit $1.3 billion. However, there are still some growing pains to address with RPA and is not exactly a 100 percent perfect, but it fits right in with the current trends in cloud computing toward optimization. And, since, we’re all about saving time and money – let’s recap on this trend to see how it can help to do these things.
What is Robotic Process Automation?
To recount, RPA, whether it’s called “intelligent automation” or “cognitive automation” in the future, is a way to automate business processes by creating software robots paired with artificial intelligence (AI) and machine learning capabilities to perform manual and mundane work-tasks. It allows users to configure within an application and gives them the capability to handle a variety of repetitive tasks by processing, employing, generating and communicating information automatically. For example, you might program RPA bots to do first-level customer support tasks by searching for answers; copy and paste data from one system to another for invoicing or expense management or issue refunds. This video from IBM shows an example in action.
RPA software is not part of an organization’s IT infrastructure. Instead, it sits on top of it, enabling a company to implement the technology quickly and efficiently. Furthermore, RPA tools can be trained to make judgments about future outputs. Many users appreciate its non-intrusive nature and the ability to integrate within infrastructures without causing disruption to systems already in place.
How can you use Robotic Process Automation?
RPA technology can help organizations on their digital transformation journeys by:
- Enabling better customer service.
- Ensuring business operations and processes comply with regulations and standards.
- Allowing processes to be completed much more rapidly.
- Providing improved efficiency by digitizing and auditing process data.
- Creating cost savings for manual and repetitive tasks.
- Enabling employees to be more productive.
Companies like Walmart, AT&T, and Walgreens are adopting the use of RPA. Clay Johnson, the CIO of Walmart, says they use RPA bots to automate pretty much anything from answering employee questions to retrieving useful information from audit documents. The CIO of American Express Global Business Travel, David Thompson, says they implement the use of RPA to automate the process for canceling an airline ticket and issuing refunds. In addition, Thompson is looking to use RPA to facilitate automatic rebooking recommendations, and to automate certain expense management tasks in the company.
But more specific to cloud computing and IT, one great application for RPA is in automated software testing. If testing involves multiple applications and monotonous work, RPA can replace workers’ time spent testing. Automated tests can run repeatedly at any time of day. This approach fits in with continuous testing as well as continuous integration (CI) and continuous delivery (CD) software development practices. Additionally, RPA can be used to automate processes in monolithic legacy systems that are not worth developers’ time to update, to bring automation while work on newer microservices systems is in progress.
Is Robotic Process Automation the Best Way to Automate Cost Control?
A study found that not all automation is achievable with RPA. In the study, they conclude that only three percent of organizations have managed to scale RPA to a high level. Additionally, Gartner placed RPA tools at the “Peak of Inflated Expectations” in their Hype Cycle guide for artificial intelligence – another vote for more buzz than potential. In reality, it is only as efficient as the person configuring the automation flow and organizations that have overly idealized expectations of the technology’s capabilities. Those that don’t have a solid grasp of their own processes may find it difficult to find the right tool to automate jobs.
However, RPA is expected to deliver tangible results to organizations that make automation a key component of their digital transformation as the collaboration between digital workers and human talent become more efficiently aligned in the future.
So can it save you time and money? If employees at your company are spending a large percentage of their time on repetitive tasks that require little to no decision making, then yes, it probably can. It’s also important because it will free up developer time that is spent on automatable tasks, like scripting, so they can focus on creating value for your business.
For complex and long-term automation, though, purpose-built software is a better solution. If there is already a solution to your automation needs on the market, it will probably serve you better than RPA because there won’t be an upfront period needed to program bots, you won’t need to make frequent changes to your processes like many RPA bots will require, and it’s a better solution for the long run.