Qubole, Inc.

Other

Hariharan Iyer

Joydeep Sen Sarma

Mayank Ahuja

Prakhar Jain

Ajaya Agrawal

The present invention is generally directed to systems and methods of providing automatic scaling pure-spot clusters. Such dusters may be dynamically rebalanced for further costs savings. In accordance with some methods of the present invention may include a method of utilizing a cluster in a big data cloud computing environment where instances may include reserved on-demand instances for a set price and on-demand spot instances that may be bid on by a user, the method including: creating one or more stable nodes, comprising spot instances with a bid price above a price for an equivalent on-demand instance; creating one or more volatile nodes, comprising spot instances with a bid price below a price for an equivalent on-demand instance; using one or more of the stable nodes as a master node; and using the volatile nodes as slave nodes.

Pure-spot and dynamically rebalanced auto-scaling clusters

The present invention is generally directed to systems and methods of provisioning, and using heterogeneous clusters in a cloud-based big data system, the heterogeneous clusters made up of primary instance types and different types of instances, the method including: determining if there are composition requirements of any heterogeneous cluster, the composition requirements defining instance types permitted for use; determining if any of the permitted different types of instances are required or advantageous for use; determining an amount of different types of instances to utilize, this determination based at least in part on an instance weight; provisioning the heterogeneous cluster comprising both primary instances and permitted different types of instances.

Heterogeneous auto-scaling big-data clusters in the cloud

The present invention is generally directed to systems and methods of provisioning and using heterogeneous clusters in a cloud-based big data system, the heterogeneous clusters made up of primary instance types and different types of instances, the method including: determining if there are composition requirements of any heterogeneous cluster, the composition requirements defining instance types permitted for use; determining if any of the permitted different types of instances are required or advantageous for use; determining an amount of different types of instances to utilize, this determination based at least in part on an instance weight; provisioning the heterogeneous cluster comprising both primary instances and permitted different types of instances.

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Bengaluru, India

Hariharan Iyer