Abstract: In one aspect, a computerized method for detecting reconnaissance and infiltration in data lakes and cloud warehouses, comprising: monitoring a SaaS data store or a cloud-native data store from inside the data store; examining the attack and automatically identifies how far the attack has progressed in the attack lifecycle; identifying the target and scope of the attack evaluates how far the attackers have penetrated the system and what is their target; and establishing the value of the asset subject to the attackers' attack and maps the impact of the attack on the CIA (confidentiality, integrity and availability) triad.

Abstract: System and computer-implemented method for managing on-prem hyperconverged systems automatically detects a condition for additional resources in an on-prem hyperconverged system with a cluster of active host computers and at least one dark capacity host computer that is not part of the cluster. The resources of at least one dark capacity host computer in the on-prem hyperconverged system is used to address the condition for additional resources after the dark capacity host computer has been added to the cluster.

Abstract: In a computer-implemented method for modifying a state of a virtual machine, information between two states of a virtual machine is compared, wherein the two states include a current state of the virtual machine and previous state of the virtual machine. The previous state of the virtual machine is included within a snapshot of the virtual machine at the previous state. Information that is different between the two states is identified. The information that is different between the two states is presented, wherein the information that is different is selectable for copying between the two states.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: A number of hosts in a logical cluster is adjusted up or down in an elastic manner by tracking membership of hosts in the cluster using a first data structure and tracking membership of hosts in a spare pool using a second data structure, and upon determining that a triggering condition for adding another host is met and that all hosts in the cluster are being used, selecting a host from the spare pool, and programmatically adding an identifier of the selected host to the first data structure and programmatically deleting the identifier of the selected host from the second data structure.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: A number of hosts in a logical cluster is adjusted up or down in an elastic manner by tracking membership of hosts in the cluster using a first data structure and tracking membership of hosts in a spare pool using a second data structure, and upon determining that a triggering condition for adding another host is met and that all hosts in the cluster are being used, selecting a host from the spare pool, and programmatically adding an identifier of the selected host to the first data structure and programmatically deleting the identifier of the selected host from the second data structure.

Abstract: The subject matter described herein provides virtual computing instance (VCI) component protection against networking failures in a datacenter cluster. Networking routes at the host level, VCI level, and application level are monitored for connectivity. Failures are communicated to a primary host or to a datacenter virtualization infrastructure that initiates policy-based remediation, such as moving affected VCIs to another host in the cluster that has all the necessary networking routes functional.

Abstract: A number of hosts in a logical cluster is adjusted up or down in an elastic manner by tracking membership of hosts in the cluster using a first data structure and tracking membership of hosts in a spare pool using a second data structure, and upon determining that a triggering condition for adding another host is met and that all hosts in the cluster are being used, selecting a host from the spare pool, and programmatically adding an identifier of the selected host to the first data structure and programmatically deleting the identifier of the selected host from the second data structure.

Abstract: The subject matter described herein provides virtual computing instance (VCI) component protection against networking failures in a datacenter cluster. Networking routes at the host level, VCI level, and application level are monitored for connectivity. Failures are communicated to a primary host or to a datacenter virtualization infrastructure that initiates policy-based remediation, such as moving affected VCIs to another host in the cluster that has all the necessary networking routes functional.

Abstract: A number of hosts in a logical cluster is adjusted up or down in an elastic manner by tracking membership of hosts in the cluster using a first data structure and tracking membership of hosts in a spare pool using a second data structure, and upon determining that a triggering condition for adding another host is met and that all hosts in the cluster are being used, selecting a host from the spare pool, and programmatically adding an identifier of the selected host to the first data structure and programmatically deleting the identifier of the selected host from the second data structure.