Microsoft Technology Licensing, LLC

Abhinit Kumar

David Allen Dion

Lokesh Srinivas Koppolu

Amitabh Prakash Tamhane

Elden Christensen

Andrea D'Amato

Nicholas Maliwacki

Suresh Chandra Pippari

Gor Nishanov

Daniel Upton

Embodiments are directed to communicating between computing nodes in a cluster of nodes. In one scenario, a computer system receives a data packet from a worker node including the worker node's current workload identifiers and health status, where the data packet includes an associated version number. The computer system determines that the version number in the received data packet is different than a previously received data packet and evaluates the worker node's current workload configuration to determine whether workload changes are to be made on the worker node. Then, upon determining that workload changes are to be made on the worker node, the computer system selects a subset of workload changes to apply to the worker node, generates an indication of the selected subset of workload changes to the worker node and sends the generated indication of workload changes to the worker node.

Scalable fault resilient communications within distributed clusters

Technologies for cluster systems that are natively geo-site-aware. Such a cluster system makes use of this awareness to determine the subsets of nodes located at various geo-sites at physical configuration, to optimize workload placement based on the geo-sites, to make failover and failback decisions based on the geo-sites, and to assign voting and prune nodes for quorum management based on the geo-sites. Such capabilities result in cluster systems that are more resilient and more efficient in terms of resource usage than cluster systems without such native geo-site awareness.

Optimal storage and workload placement, and high resiliency, in geo-distributed cluster systems

Embodiments provide workload processing for clustered systems. In an illustrative, non-limiting embodiment, a computer-implemented method may include identifying a server as an active node of a cluster; assigning a workload to the server in response to the identification; determining, after the assignment, that the server is no longer an active node of the cluster; calculating, in response to the determination, a probability that the server is capable of continuing to execute the workload; and deciding, based upon the probability, whether to allow the workload to remain assigned to the server.

Providing higher workload resiliency in clustered systems based on health heuristics

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Abhinit Kumar