The issue appears to be independent of the type of processor in the ThinkPad. Multiple forum members are claiming that the processor of the X220 will throttle down to lower clock speeds, even when the notebook is set to maximum performance and plugged into an outlet. Request an IBM Turbonomic demo today.The recently released ThinkPad X220 is reportedly facing CPU throttling issues, according to users in the official Lenovo ThinkPad forums. You can continuously automate critical actions in real-time-and without human intervention-that proactively deliver the most efficient use of compute, storage and network resources to your apps at every layer of the stack. IBM Turbonomic can help optimize your cloud spend and performance. Not only can Turbonomic monitor CPU throttling metrics, but the platform can also automatically right-size your CPU limit and bring the throttling down to a manageable level. The benefit of Turbonomic is our ability to quickly identify and solve a consequence of a platform strategy rather than have the customer redesign their multitenant platform strategy. Customers have the ability to see the KPIs and ask “Which one of my services is being throttled?” It also allows them to understand the history of CPU throttling for each service and remember that each service is directly correlated to application response time. On top of this, Turbonomic is generating actions to move your pods and scale your clusters-as we all know, it’s a full-stack challenge. Adding the dimension of CPU throttling will ensure low application response times. This is all through the power of adding CPU throttling as a dimension for the platform to analyze and manage the tradeoffs that appear. Turbonomic can determine the CPU limits that will mitigate the risk of throttling and allow your applications to perform unincumbered. When determining container rightsizing actions, Turbonomic is able to analyze four dimensions: IBM Turbonomic has built that analytics platform. You need to take all the analytics that go into application performance into account. To ensure that your application response times remain low and CPU doesn’t get throttled, you need to first understand that when CPU throttling is occurring, you can’t just look at CPU utilization. This is great news for you, as you can get this metric directly from Kubernetes and OpenShift. Using IBM Turbonomic to avoid CPU throttling in KubernetesĬPU throttling is a key application performance metric due to the direct correlation between response time and CPU throttling. Your application’s performance will suffer due to the increase in response time caused by throttling. If your task is longer than 20ms, you will be throttled, and it will take you 4x longer to complete the task. The container is only able to use 20ms of CPU at a time because the default enforcement period is only 100ms. To bring some color to this, imagine you set a CPU limit of 200ms and that limit is translated to a group quota in the underlying Linux system. The high response times are directly correlated to periods of high CPU throttling, and this is exactly how Kubernetes was designed to work. Even if you have more than enough resources on your underlying node, your container workload will still be throttled because it was not configured properly. So, what’s going on here? CPU throttling occurs when you configure a CPU limit on a container, which can invertedly slow your application’s response time. In the above figure, the CPU utilization of a container is only 25%, which makes it a natural candidate to resize down: Figure 2: Huge spike in response time after resizing to ~50% CPU utilization.īut after we resize down the container (container CPU utilization is now 50%, still not high), the response time quadrupled. Take a look at this example: Figure 1: CPU with 25% utilization. Some devs will set CPU limits for benchmark testing for their applications.ĬPU throttling is the unintended consequence of this design. ![]() These multitenant environments rely on the setting of limits to regulate the tenant workloads or to use limits for chargebacks. Today, the majority of enterprise organizations running mission-critical applications on Kubernetes are doing so in multitenant environments.
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