Overview of Scheduling and Optimization Algorithms Sample Clauses

Overview of Scheduling and Optimization Algorithms. ‌ The second major objective of the DECICE project besides connecting Cloud, Edge and HPC into a compute continuum is to establish optimized AI-driven scheduling systems that are capable of finding near-optimal scheduling decisions for workloads across the compute continuum. As the underlying platform for the prototype of the DECICE system, Kubernetes, and all the considered alternatives (Apache Mesos, Hashicorp Nomad) are container orchestration platforms, the target for scheduling decisions are containers. More precisely, pods, in the case of Kubernetes, which are single containers or groups of containers that operate closely together. For example, one container might provide a service and another container in the same pod might provide a proxy with queuing and rate-limiting for that service. Kubernetes was chosen as it has become the de facto standard in terms of container orchestration platforms [Car22] and is part of the graduated projects of the Cloud Native Computing Foundation. The task of a scheduler is to find a placement for a pod across all nodes that are part of a cluster. A pod commonly has resource requirements in terms of memory and CPU time that should be available on the target node. Furthermore, a pod might require more specific resources such as access to a GPU or other special resources exposed to Kubernetes. Depending on the required resources and the state of the cluster, a scheduling decision might not always be possible when no node is able to fulfill the resource requirements. Then the pod has to wait for the cluster state to change such that its required resources become available before it can be scheduled. In addition to limitations in terms of availability, further scheduling restrictions might apply, such as pods only being able to be scheduled on certain nodes or not on the same node as other pods. The first case could be used to ensure that in a cluster with worker nodes with and without GPUs, workloads that do not require a GPU, are not scheduled on the GPU nodes. The second case could be used to ensure better availability of a service by distributing multiple instances of the service across multiple nodes such that a node failure only takes down a single instance of the service and not potentially all instances on a single node causing a service disruption or a complete failure. When multiple pods await scheduling, the scheduler should consider the priority of the pods. Some pods might provide cluster critical services, w...
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