In Kubernetes, we often hear terms like resource management, scheduling and load balancing. While Kubernetes offers many capabilities, understanding these concepts is key to appreciating how workloads are placed, managed and made resilient. In this short article, I provide an overview of each facility, explain how they are implemented in Kubernetes, and how they interact with one another to provide efficient management of containerized workloads. If you’re new to Kubernetes and seeking to learn the space, please consider reading our case for Kubernetes article.
Resource management is all about the efficient allocation of infrastructure resources. In Kubernetes, resources are things that can be requested by, allocated to, or consumed by a container or pod. Having a common resource management model is essential, since many components in Kubernetes need to be resource aware including the scheduler, load balancers, worker-pool managers and even applications themselves. If resources are underutilized, this translates into waste and cost-inefficiency. If resources are over-subscribed, the result can be application failures, downtime, or missed SLAs.
Resources are expressed in units that depend on the type of resource being described — as examples, the number of bytes of memory or the number of milli-cpus of compute capacity. Kubernetes provides a clear specification for defining resources and their various properties.
While cpu and memory are the main resource types used today, the resource model is extensible, allowing for a variety of system and user-defined resource types. Additional types include things like network-bandwidth, network-iops and storage-space.
Resource specifications have different meanings in different contexts. The three main ways we specify resources in Kubernetes are described below:
- A ResourceRequest refers to a combined set of resources being requested for a container or Pod. For example, a Pod might request 1.5 cpus and 600MB of memory for each pod instance. A ResourceRequest can be thought of as describing the application services’ “demand” for resources.
- A ResourceLimit refers to an upper boundary on combined resources that a container or pod can consume. For example, if a pod uses more than 2.5 cpus or 1.2GB of memory at run-time, we might consider it to have gone “rogue” owing to a memory leak or some other issue. In this case, the Scheduler might consider the pod a candidate for eviction to prevent it from interfering with other cluster tenants.
- A ResourceCapacity specification describes the amount of resource available on a cluster node. For example, a physical cluster host might have 48 cores and 64GB or RAM. Clusters can be comprised of nodes with different resource capacities. The capacity specification can be thought of a describing the resource “supply”.
In Kubernetes, scheduling is the process by which pods (the basic entity managed by the scheduler) are matched to available resources. The scheduler considers resource requirements, resource availability and a variety of other user-provided constraints and policy directives such as quality-of-service, affinity/anti-affinity requirements, data locality and so on. In essence, the scheduler’s role is to match resource “supply” to workload “demand” as illustrated below:
Some scheduling constraints (referred to as FitPredicates) are mandatory. For example, if a pod requires a cluster node with four cpu cores and 2GB of memory, the pod will remain in a pending state until a cluster host satisfying this requirement is found.
In other cases, there may be multiple hosts that meet a mandatory criterion. In this case, PriorityFunctions are considered that reflect scheduling preferences. Basically the scheduler takes the list of hosts that meet the mandatory FitPredicates, scores each host based on the results of user-configurable priority functions, and finds an optimal placement solution where the maximum number of scheduling priorities are satisfied.
In Kubernetes, workloads can be comprised of a variable number of pods, each with specific resource requirements. Also, workloads and clusters are dynamic and with scaling and auto-scaling capabilities, the number of pods can change with time requiring the scheduler to constantly re-evaluate placement decisions. Also, with Kubernetes features like cron jobs, the scheduler needs to consider not just present supply, demand and cluster state, but reserved capacity for future workloads as well.
A useful metaphor for understanding the scheduling challenge is a game of Tetris. The goal is to pack all the pieces as tightly as possible (using resources efficiently). Rather than the game pieces (pods) being two dimensional however, they are multi-dimensional (requiring specific memory, cpu, label selectors etc..). Failing to fit a game piece is analogous to an application that can’t run. As if things aren’t hard enough already, instead of the gameboard being static, it is changing with time as hosts go in and out of service and services scale up and down. Such is the challenge of scheduling in Kubernetes.
Finally, load balancing involves spreading application load uniformly across a variable number of cluster nodes such that resources are used efficiently. Application services need to be scalable and remain accessible even individual nodes are down or components fail. While load balancing is a different challenge than scheduling, the two concepts are related.
Kubernetes relies on the concept of pods to realize horizontal scaling. As a reminder, pods are collections of containers related to an application function that run on the same host. To scale, multiple pods sharing a common label will run across multiple cluster hosts. Replication controllers are responsible for ensuring that a target number of pods for an application are running, and will create or destroy pods as needed to meet this target. Each pod will have its own virtual IP address on the cluster that can change with time, so this is where services come in.
A service in Kubernetes abstracts a set of pods, providing a single network endpoint. Because the service IP address (like the pods) has an IP that is only routable within the cluster, services are often coupled with an ingress resource providing a means to proxy an external IP address and port to the service endpoint. This makes the application available to the outside world. While there are multiple ways to achieve load balancing in Kubernetes (including using load balancers provided by cloud providers) the scenario described above involving an ingress and service is common.
What does all this have to do with scheduling? As outlined above, with pod autoscaling, Kubernetes can scale the number of pods managed by a replication controller dynamically based on observed cpu utilization. The controller periodically queries the resource metrics API to obtain utilization for each pod, compares this to a target cpu utilization specified when the autoscaler is created, and, based on the result, instructs the replication controller to adjust the target number of pod replicas.
The result of this is an elegant interplay between load balancing and scheduling. As external clients create load, accessing an application service through an ingress, the cpu utilized by pods will increase or fall. Beyond certain thresholds, the autoscaler will interact with the replication controller and scheduler to adjust the number of pods based on load. The revised number of pods and their locations will be available to the service, so the fact the number of pods may have changed is transparent to the ingress and external clients.
This delicate ballet, balancing resource requirements with application demand involves constant negotiation between autoscalers, replication controllers and the Kubernetes scheduler factoring resource demand, supply, constraints and priorities. All this takes place behind the scenes without client applications being aware. The ability to perform these operations efficiently, transparently and reliably such that applications just run are the reasons that Kubernetes is a popular orchestration solution for containerized workloads.
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