Abstract: A storage system sizing service that includes: translating one or more specifications characterizing user requirements into workload parameters; generating a plurality of performance model results by providing the workload parameters as respective inputs to respective performance models, wherein the respective performance models correspond to respective distinct configurations of a plurality of storage systems; and selecting, in dependence upon the performance model results, one or more distinctly configured storage system configurations from among the plurality of storage systems.

Abstract: A storage system determines source addresses, and destination addresses in a storage system, for network traffic. The storage system determines a hash algorithm, from a plurality of hash algorithms. The hash algorithm is to be used across the source addresses for load-balancing the network traffic to the destination addresses. The storage system determines that the hash algorithm more closely meets one or more load-balancing criteria than at least one other hash algorithm, of the plurality of hash algorithms. The storage system distributes the network traffic from the source addresses to the destination addresses in the storage system, with load-balancing according to the determined hash algorithm.

Abstract: A storage system determines source addresses, and destination addresses in a storage system, for network traffic. The storage system determines a hash algorithm, from a plurality of hash algorithms. The hash algorithm is to be used across the source addresses for load-balancing the network traffic to the destination addresses. The storage system determines that the hash algorithm more closely meets one or more load-balancing criteria than at least one other hash algorithm, of the plurality of hash algorithms. The storage system distributes the network traffic from the source addresses to the destination addresses in the storage system, with load-balancing according to the determined hash algorithm.

Abstract: A storage system that has blades and fabric modules connects to a customer legacy network that has a first, active switch and a second, passive switch. A first link aggregation group (LAG) is configured active and includes ports of the first, active switch that connect via links to the first and second fabric modules of the storage system. A second LAG is configured passive and includes ports of the second, passive switch that connect via links to the first and second fabric modules. A multi-chassis link aggregation group (MLAG, MCLAG or MC-LAG) is configured and includes ports of the first and second fabric modules that connect via links to the first and second switches.

Abstract: Automatically creating a data analytics pipeline, including: receiving, from a user, information describing a plurality of components to include in a data analytics pipeline; receiving, from the user, information describing a manner in which the plurality of components in the data analytics pipeline should be connected; and creating the data analytics pipeline in dependence upon the information describing the plurality of components to include in the data analytics pipeline and the information describing the manner in which the plurality of components in the data analytics pipeline should be connected.