Abstract: Autonomous power control is provided on a storage system to enable service level maximum response time controls to be achieved, while also minimizing power consumption, by enabling service level minimum response times to be specified and enforced. A workload/CPU clock speed model is created for the storage system correlating a maximum number of IOPS that the storage system can process for different CPU clock speeds for each workload type. A service level agreement specifies a maximum storage system response time. A storage system minimum response time is also specified, that is used to identify a target CPU clock speed for the workload type being provided by a host. The CPU clock speed is lowered to reduce energy consumption by the storage system toward the target CPU clock speed, and storage system performance is monitored to ensure that the storage system complies with the storage system maximum response time.

Abstract: A Ransomware Activity Detection System (RADS) characterizes historic read/write IO activity on a storage volume, and also characterizes historic data characteristics of the storage volume, such as the percentage reducibility of the data held in the storage volume. The RADS monitors the storage volume to identify differences between current read/write IO activity and historic read/write IO activity, as well as difference between current data characteristics of the storage volume and historic data characteristics of the storage volume. When the RADS detects a significant difference in read/write IO activity on a storage volume, that is coupled with a significant changes to the data characteristics of the storage volume, the RADS protects the storage volume and generates an alert of the possible occurrence of a ransomware attack. Protection may occur prior in connection with any bulk read operation to proactively protect storage volumes against ransomware attacks.

Abstract: An intelligent predictive battery replacement system to increase disaster recovery stability uses training samples created using battery report indexes and vault condition recovery reports to train a linear regression model learning process to learn a recursion between battery post recovery charge and a set of time series battery operational parameters. Once trained, the learning process is used in a predictive manner to predict the post recovery charge state of batteries deployed in storage controllers, to provide a predictive per-battery risk assessment. The per-battery risk assessment identify batteries that may be scheduled to be replaced to increase disaster recovery stability of the storage systems.

Abstract: Proactive traffic shaping is used to generate and transmit proactive QFULL messages that are selectively sent to hosts that are generating large numbers of IO operations with lower service levels in instances where a compute node is experiencing high IO volume. By sending proactive QFULL messages only to hosts that are sending IO operations with lower service levels, it is possible to cause the lower priority IO operations to be directed by the hosts to other compute nodes within the storage system, to thereby balance IO operations between compute nodes and enable higher priority IO operations to be serviced with lower latency. When a low priority IO operation arrives, a determination is made as to the depth of the queue. If the number of IO operations in the queue is above a threshold, and IOS are trending upward, a QFULL message is sent to the low priority IO initiator.

Abstract: Proactive traffic shaping is used to generate and transmit proactive QFULL messages that are selectively sent to hosts that are generating large numbers of IO operations with lower service levels in instances where a compute node is experiencing high IO volume. By sending proactive QFULL messages only to hosts that are sending IO operations with lower service levels, it is possible to cause the lower priority IO operations to be directed by the hosts to other compute nodes within the storage system, to thereby balance IO operations between compute nodes and enable higher priority IO operations to be serviced with lower latency. When a low priority IO operation arrives, a determination is made as to the depth of the queue. If the number of IO operations in the queue is above a threshold, and IOS are trending upward, a QFULL message is sent to the low priority IO initiator.

Abstract: A Ransomware Activity Detection System (RADS) characterizes historic read/write IO activity on a storage volume, and also characterizes historic data characteristics of the storage volume, such as the percentage reducibility of the data held in the storage volume. The RADS monitors the storage volume to identify differences between current read/write IO activity and historic read/write IO activity, as well as difference between current data characteristics of the storage volume and historic data characteristics of the storage volume. When the RADS detects a significant difference in read/write IO activity on a storage volume, that is coupled with a significant changes to the data characteristics of the storage volume, the RADS protects the storage volume and generates an alert of the possible occurrence of a ransomware attack. Protection may occur prior in connection with any bulk read operation to proactively protect storage volumes against ransomware attacks.