A cluster computing system is provided. The cluster computing system includes: a host including a first processor and a first buffer memory; computing nodes, each of which includes a second processor and a second buffer memory configured to store data received from the host; a network configured to connect the host and the computing nodes; and storage devices respectively corresponding to the computing nodes. The first processor is configured to control a task allocator to monitor a task performance state of each of the computing nodes, select at least one of the computing nodes as a task node based on the task performance state of each of the computing nodes, and distribute a background task to the task node, and the second processor of the task node is configured to perform the background task on sorted files stored in the second buffer memory, the sorted files being received by the second buffer memory from the first buffer memory via the network.

A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

Techniques are disclosed for the cell/cluster formation of compute nodes and workload and processing resource scheduling. Compute nodes within an environment may be grouped (clustered) together to perform one or more designated workload tasks. The clustered compute nodes may be associated with (or assigned to) a workload cell formed to perform one or more identified task(s).

Methods, computer program products, and/or systems are provided that perform the following operations: identifying, in an environment that includes a plurality of edge clusters of edge nodes, a first edge cluster having a resource gap; broadcasting a resource requirement of the first edge cluster to other edge clusters in the plurality; obtaining resource commitments from one or more of the other edge clusters; selecting edge cluster resources from the one or more of the other edge clusters based, at least in part, on the resource commitments; and creating a new cluster including the first edge cluster and the selected edge cluster resources.

A processor may automatically select a cloud service provider. A processor may receive one or more parameters associated with an entity and a cloud service dataset associated with a provider. A processor may generate a digital twin of the entity using the one or more parameters. A processor may simulate the digital twin of the entity and the cloud service dataset. A processor may identify, responsive to simulating the digital twin of the entity and the cloud service dataset, one or more predicted conditions of cloud service dataset on the entity. A processor may select the provider based, at least in part, on the one or more predicted conditions.

Methods, systems, and apparatus, including an apparatus for generating clusters of building blocks of compute nodes using an optical network. In one aspect, a method includes receiving request data specifying requested compute nodes for a computing workload. The request data specifies a target n-dimensional arrangement of the compute nodes. A selection is made, from a superpod that includes a set of building blocks that each include an m-dimensional arrangement of compute nodes, a subset of the building blocks that, when combined, match the target n-dimensional arrangement specified by the request data. The set of building blocks are connected to an optical network that includes one or more optical circuit switches. A workload cluster of compute nodes that includes the subset of the building blocks is generated. The generating includes configuring, for each dimension of the workload cluster, respective routing data for the one or more optical circuit switches.

The present disclosure discloses a workflow scheduling method and system based on a multi-target particle swarm algorithm, and a storage medium. The method comprises the following steps that first, the difference between the frequency reduction characteristic and the execution time of each server in a cluster is considered; a multi-target comprehensive evaluation model covering workflow execution overhead, execution time and cluster load balance is constructed on the basis of a traditional model; second, a multi-target particle swarm algorithm is provided for workflow scheduling, and an efficient solving method is provided. The method alleviates the defects of premature convergence and low species diversity of the particle swarm algorithm, reduces the execution overhead and execution time of the workflow on the cluster server, and better balances the load of the cluster server.

Disclosed herein are an apparatus and a method for providing a virtual multi-cloud service. The apparatus for providing a virtual multi-cloud service includes one or more processors and execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to create a virtual machine cluster requested by an operator using an external cloud computing infrastructure, install a container orchestration tool in the virtual machine cluster using container-related information registered by the operator in advance, and create a virtual cloud requested by the operator using the container orchestration tool.

An arrangement includes a plurality of clusters and an interface through which a distributed federation database is accessible, wherein each of the clusters includes a cluster interface; a cluster local memory configured to store local cluster resources; and a federation controller. The federation controller is configured to: receive a first notification from the distributed federation database, wherein the first notification indicates a change relating to a federation resource in the distributed federation database; analyze the first notification; modify the local resource based on the analysis; and update a status of the federation resource in the distributed federation database when the local resource has been stored.