devopsy.kinsta.cloud

Tag: Opsani

  • Opsani Applies AI to Rein in Cloud Costs

    Opsani Applies AI to Rein in Cloud Costs

    Opsani has added support for cloud computing environments to its namesake software-as-a-service (SaaS) platform that leverages machine learning algorithms and other forms of artificial intelligence (AI) to optimize the IT environment.

    Company CEO Ross Schibler said Opsani AI not only proactively tunes resources such as CPU and memory, but it also can optimize middleware configuration variables such as Java virtual machine (JVM) type and pool sizes; kernel parameters such as page sizes and jumbo packet sizes; and application parameters such as thread pools, cache timeouts and write delays.

    Given the size and dynamic nature of most cloud computing environments, Schibler said it’s not possible for IT administrators to manually optimize cloud computing environments to match the specific needs of each class of application workload being deployed. The Opsani platform makes it possible to leverage AI to optimize settings for the lowest cost or best performance every second, he said.

    Opsani claims existing customers are seeing more than 200% increases in performance per dollar while saving up to 80% on their cloud spend.

    In addition, Opsani has made available plugins for a variety of DevOps tools including GitHub, Terraform, Jenkins, Spinnaker, Wavefront, DataDog, SignalFX, Prometheus, Splunk and New Relic, on all the major cloud computing platforms.

    Schibler said Opsani can be applied to existing monolithic applications as well as emerging microservices-based applications based on containers running on Kubernetes clusters. The goal is to make it possible for IT teams to rein in cloud costs at a time when most cloud resources are being employed inefficiently, he said.

    Individual developers are deploying applications on virtual machines in the cloud with little to no appreciation for total cost, noted Schibler. Before most IT organizations realize it, the cost of running applications on a public cloud has far exceeded budget allocations. IT organizations not only need tools to rein in those costs, but Schibler noted Opsani also makes it possible to model those costs before an application is deployed or upgraded.

    The adoption of public clouds and DevOps often go hand in hand. The challenge organizations are encountering more often is that as the number of applications running in a public cloud computing environment increases, they often wind up overprovisioning resources. Over time, the need to optimize the consumption of cloud resources becomes a more pressing financial concern. However, most IT organizations don’t have the resources or expertise needed to manually tune each instance of cloud workload. At the same time, the typical IT organization doesn’t have the ability to build its own AI engine. Opsani is making the case for a SaaS platform that provides that capability without requiring an IT organization to build its own AI models from scratch.

    Naturally, many IT professionals are dubious of AI because there may not be enough transparency into the AI models being employed. However, because Opsani is a SaaS platform, the risk of experimenting with AI to optimize cloud workloads is relatively slight. Of course, there’s no absolute requirement to employ AI at all. However, as financial pressure on IT organizations continues to mount, there doesn’t seem to be much of an alternative either.

    — Mike Vizard

  • Continuous Optimization: An Idea Whose Time Has Come?

    Continuous Optimization: An Idea Whose Time Has Come?

    Agile makes development deliver code faster. CI makes testing move faster and CD makes delivery faster, but what keeps track of the volume of change all of this introduces and delivers optimizations faster?

    That is the question Opsani wants you to be asking. And it’s a valid question. Optimization in a complex and constantly changing software world becomes more important–if it can keep up. Opsani thinks it can, with a little AIOps to help anyway.

    The premise is simple. There are ways to adjust the environment that might increase performance and/or reduce costs. Those ways are individually relatively simple (such as JVM settings or cloud instance sizing), but combined they make a complex web that impacts performance as a whole, with individual bits sometimes helping and sometimes hurting.

    Up JVM memory on a large instance and it might help. Switch to a smaller instance later, and that JVM change may cause problems on the smaller footprint, for example. So something more dynamic is needed. As code changes, best optimization options might change also. As the other parts of the environment change, that magnifies the concern that applications are not running at their best and might be costing too much money.

    Opsani’s answer to this problem is continuous optimization. Using AIOps to check different combinations of settings at a rate that operations staff couldn’t manage even if they did have the time. And they generally don’t.

    From my (not their) perspective, there are two pieces to this puzzle. First is gathering inputs and determining performance, second is tweaking settings. Then repeat. Seems simple but the environment continuous optimization would work in is increasingly complex. Any look at how many places optimization can occur in a given environment–from VM/Instance to code to network to management platform to storage/database choices–there is a lot going on, and all of these settings can impact each other.

    Hence the idea of continuous optimization appeals to me. They’ve got the interfaces that one would expect to gather inputs, names such as Jenkins, DataDog and New Relic. They’ve got the interfaces to enact and test out changes too, with interfaces to products such as Kubernetes, Terraform, Rancher and Wavefront. So it certainly appears they’ve got the basics covered. Of course there are vendor interfaces I’d like to see that aren’t in the list of integrations, and no doubt the same is true for you. But the core is here, getting to the monitoring and management tools most companies use regularly.

    Opsani seems to be aware of the different comfort levels of various organizations, also. Able to run in dev or test as well as production. Since every organization has a different taste for putting new ideas into production, this is a good idea, though personally I would aim for eventual production rollout, just because that is where the rubber meets the road. Having it running as part of normal operations means even that small change that reduces responsiveness of your application by 25% (we’ve all seen it) will be detected, and Opsani can try changing some of the environment to compensate.

    When I spoke with them, we did not discuss cost, but we did discuss savings. For budget-sensitive environments, it is worth looking into the cost in your organization versus the potential savings in actual monthly dollars. Here is a screenshot showing the cost measure combined with the performance measure. Reducing monthly overhead to pay for a tool that also improves performance might just be a no-brainer–if it suits your environment.

    I can’t say if it suits your environment, nor can any other analyst/pundit. That’s for your organization to decide. But the idea of continuous optimization is a good one. Opsani is not alone in working on this problem, so even if this particular tool doesn’t suit your needs, you can look for others that do. I recommend getting the demo and seeing what they’ve got, and how it might help in your environment.

    — Don Macvittie