Abstract: A storage space management system for a thin provisioned virtual environment may comprise an over-allocation computation engine to compute an over-allocation metric for a virtual datastore. The over-allocation metric may be computed based on virtual storage space allocated to corresponding virtual machines and actual physical storage allocated to the virtual datastore. Further, the over-allocation metric may indicate extent of over-allocation of the actual physical storage to the virtual datastore. An available space computation engine may determine an available space metric for the virtual machines based on available datastore space and available physical storage space. An analysis engine may obtain a time value indicating time left within which storage space available for the virtual machines would be utilized.

Abstract: A storage space management system for a thin provisioned virtual environment may comprise an over-allocation computation engine to compute an over-allocation metric for a virtual datastore. The over-allocation metric may be computed based on virtual storage space allocated to corresponding virtual machines and actual physical storage allocated to the virtual datastore. Further, the over-allocation metric may indicate extent of over-allocation of the actual physical storage to the virtual datastore. An available space computation engine may determine an available space metric for the virtual machines based on available datastore space and available physical storage space. An analysis engine may obtain a time value indicating time left within which storage space available for the virtual machines would be utilized.

Abstract: In an example implementation, a network topology map with storage paths between servers and storage volumes of storage arrays in a storage network through network switches may be generated. A network switch may be identified from the network switches in the network topology map as a bottleneck by monitoring a performance parameter for each of the network switches. The performance parameter is indicative of I/O load at a port of a respective network switch. Storage volumes in the network topology map and connected to the bottlenecked network switch may be identified, and storage volume I/O metrics associated with each of the servers with respect to the identified storage volumes may be aggregated. Based on the aggregated storage volume I/O metrics, at least one of the servers may be determined as a candidate for a root-cause of the bottleneck.