Typical cluster management tools help virtualize a set of machines and manage them as a single cluster. Cluster management tools also help move the workload or containers across machines while being transparent to the consumer. Technology evangelists and practitioners use different terminologies, such as cluster orchestration, cluster management, data center virtualization, container schedulers, or container life cycle management, container orchestration, data center operating system, and so on.
Many of these tools currently support both Docker-based containers as well as noncontainerized binary artifact deployments, such as a standalone Spring Boot application. The fundamental function of these cluster management tools is to abstract the actual server instance from the application developers and administrators.
Cluster management tools help the self-service and provisioning of infrastructure rather than requesting the infrastructure teams to allocate the required machines with a predefined specification. In this automated cluster management approach, machines are no longer provisioned upfront and preallocated to the applications. Some of the cluster management tools also help virtualize data centers across many heterogeneous machines or even across data centers, and create an elastic, private cloud-like infrastructure. There is no standard reference model for cluster management tools. Therefore, the capabilities vary between vendors.
Some of the key capabilities of cluster management software are summarized as follows:
- Cluster management: It manages a cluster of VMs and physical machines as a single large machine. These machines could be heterogeneous in terms of resource capabilities, but they are, by and large, machines with Linux as the operating system. These virtual clusters can be formed on the cloud, on-premises, or a combination of both.
- Deployments: It handles the automatic deployment of applications and containers with a large set of machines. It supports multiple versions of the application containers and also rolling upgrades across a large number of cluster machines. These tools are also capable of handling the rollback of faulty promotes.
- Scalability: It handles the automatic and manual scalability of application instances as and when required, with optimized utilization as the primary goal.
- Health: It manages the health of the cluster, nodes, and applications. It removes faulty machines and application instances from the cluster.
- Infrastructure abstraction: It abstracts the developers from the actual machine on which the applications are deployed. The developers need not worry about the machine, its capacity, and so on. It is entirely the cluster management software's decision to decide how to schedule and run the applications. These tools also abstract machine details, their capacity, utilization, and location from the developers. For application owners, these are equivalent to a single large machine with almost unlimited capacity.
- Resource optimization: The inherent behavior of these tools is to allocate container workloads across a set of available machines in an efficient way, thereby reducing the cost of ownership. Simple to extremely complicated algorithms can be used effectively to improve utilization.
- Resource allocation: It allocates servers based on resource availability and the constraints set by application developers. Resource allocation is based on these constraints, affinity rules, port requirements, application dependencies, health, and so on.
- Service availability: It ensures that the services are up and running somewhere in the cluster. In case of a machine failure, cluster control tools automatically handle failures by restarting these services on some other machine in the cluster.
- Agility: These tools are capable of quickly allocating workloads to the available resources or moving the workload across machines if there is change in resource requirements. Also, constraints can be set to realign the resources based on business criticality, business priority, and so on.
- Isolation: Some of these tools provide resource isolation out of the box. Hence, even if the application is not containerized, resource isolation can be still achieved.
A variety of algorithms are used for resource allocation, ranging from simple algorithms to complex algorithms, with machine learning and artificial intelligence. The common algorithms used are random, bin packing, and spread. Constraints set against applications will override the default algorithms based on resource availability:
The preceding diagram shows how these algorithms fill the available machines with deployments. In this case, it is demonstrated with two machines:
- Spread: This algorithm performs the allocation of workload equally across the available machines. This is showed in diagram A.
- Bin packing: This algorithm tries to fill in data machine by machine and ensures the maximum utilization of machines. Bin packing is especially good when using cloud services in a pay-as-you-use style. This is shown in diagram B.
- Random: This algorithm randomly chooses machines and deploys containers on randomly selected machines. This is showed in diagram C.
There is a possibility of using cognitive computing algorithms such as machine learning and collaborative filtering to improve efficiency. Techniques such as oversubscription allow a better utilization of resources by allocating underutilized resources for high-priority tasks—for example, revenue-generating services for best-effort tasks such as analytics, video, image processing, and so on.