Netapp, Inc.

Peritus.ai, Inc.

Vipul Mathur

Kaladhar Voruganti

Lakshmi Narayanan Bairavasundaram

Gokul Soundararajan

Rukma Ameet Talwadker

Sai Rama Krishna Susarla

Neeraja Yadwadkar

Priya Sehgal

Darren Sawyer

Sai Rama Krishna Susaria

Garth Richard Goodson

Kiran Nenmeli Srinivasan

Shankar Pasupathy

Jayanta Basak

Mohan Reddy

Siddhartha Nandi

Srinivasan Narayanamurthy

Amin Mousavi

Goutham Shantigodu Tholpadi

Lakshmi Narayanan Bairavasunda

Karthik Gooli

Prakash Jagatheesan

It is detected that a metric associated with a first workload has breached a first threshold. It is determined that the first workload and a second workload access the same storage resources, wherein the storage resources are associated with a storage server. It is determined that the metric is impacted by the first workload and the second workload accessing the same storage resources. A candidate solution is identifier. An estimated impact of a residual workload is determined based, at least in part, on the candidate solution. A level of caching of at least one of the first workload or the second workload is adjusted based, at least in part, on the estimated impact of the residual workload.

Dynamic instantiation and management of virtual caching appliances

A system operation management server computer (server) and related methods are disclosed. The server is programmed to learn the features of and relations among computer devices from various types of data related to the computer devices and build a knowledge graph (KG) to represent IT infrastructure. The server is also programmed to manage a collection of issue resolution rules each mapping the states of certain computer devices that characterize a known issue and a known resolution of the known issue. In response to receiving a support bundle that contains data related to a target computer system that has encountered an unknown issue the server is programmed to determine which issue resolution rules are applicable to the support bundle based on the KG and transmit a recommendation for resolving the unknown issue.

Constructing and utilizing a knowledge graph for information technology infrastructure

Dynamic installation and management of virtual caching appliances

Described herein is a system and method for dynamically managing service-level objectives (SLOs) for workloads of a cluster storage system. Proposed states/solutions of the cluster may be produced and evaluated to select one that achieves the SLOs for each workload. A planner engine may produce a state tree comprising nodes, each node representing a proposed state/solution. New nodes may be added to the state tree based on new solution types that are permitted, or nodes may be removed based on a received time constraint for executing a proposed solution or a client certification of a solution. The planner engine may call an evaluation engine to evaluate proposed states, the evaluation engine using an evaluation function that considers SLO, cost, and optimization goal characteristics to produce a single evaluation value for each proposed state. The planner engine may call a modeler engine that is trained using machine learning techniques.

Proposed storage system solution selection for service level objective management

Modeler for predicting storage metrics

A change in workload characteristics detected at one tier of a multi-tiered cache is communicated to another tier of the multi-tiered cache. Multiple caching elements exist at different tiers, and at least one tier includes a cache element that is dynamically resizable. The communicated change in workload characteristics causes the receiving tier to adjust at least one aspect of cache performance in the multi-tiered cache. In one aspect, at least one dynamically resizable element in the multi-tiered cache is resized responsive to the change in workload characteristics.

Controlling a dynamically instantiated cache

Embodiments of the systems and techniques described here can leverage several insights into the nature of workload access patterns and the working-set behavior to reduce the memory overheads. As a result, various embodiments make it feasible to maintain running estimates of a workload's cacheability in current storage systems with limited resources. For example, some embodiments provide for a method comprising estimating cacheability of a workload based on a first working-set size estimate generated from the workload over a first monitoring interval. Then, based on the cacheability of the workload, a workload cache size can be determined. A cache then can be dynamically allocated (e.g., change, possibly frequently, the cache allocation for the workload when the current allocation and the desired workload cache size differ), within a storage system for example, in accordance with the workload cache size.

Systems and methods for tracking working-set estimates with a limited resource budget

It is detected that a metric associated with a first workload has breached a first threshold. It is determined that the first workload and a second workload access the same storage resources, wherein the storage resources are associated with a storage server. It is determined that the metric is impacted by the first workload and the second workload accessing the same storage resources. In response to a determination that the metric is impacted by the first workload and the second workload accessing the same storage resources, a first virtual cache appliance is instantiated and one of the first workload or the second workload is routed through the virtual cache appliance. Routing one of the first workload or the second workload through the first virtual cache appliance causes the first virtual cache appliance to cache data associated with the storage resources.

Managing service level objectives for storage workloads

Evaluating proposed storage solutions

Technology for operating a cache sizing system is disclosed. In various embodiments, the technology monitors input/output (IO) accesses to a storage system within a monitor period; tracks an access map for storage addresses within the storage system during the monitor period; and counts a particular access condition of the IO accesses based on the access map during the monitor period. When sizing a cache of the storage system that enables the storage system to provide a specified level of service, the counting is for computing a working set size (WSS) estimate of the storage system.

Methods and systems for determining a cache size for a storage system

Collaborative management of shared resources is implemented by a storage server receiving, from a first resource manager, notification of a violation for a service provided by the storage server or device coupled to the storage server. The storage server further receives, from each of a plurality of resource managers, an estimated cost of taking a corrective action to mitigate the violation and selects a corrective action proposed by one of the plurality of resource managers based upon the estimated cost. The storage server directs the resource manager that proposed the selected corrective action to perform the selected corrective action.

Collaborative management of shared resources

The techniques introduced here provide for efficient management of storage resources in a modern, dynamic data center through the use of virtual storage appliances. Virtual storage appliances perform storage operations and execute in or as a virtual machine on a hypervisor. A storage management system monitors a storage system to determine whether the storage system is satisfying a service level objective for an application. The storage management system then manages (e.g., instantiates, shuts down, or reconfigures) a virtual storage appliance on a physical server. The virtual storage appliance uses resources of the physical server to meet the storage related needs of the application that the storage system cannot provide. This automatic and dynamic management of virtual storage appliances by the storage management system allows storage systems to quickly react to changing storage needs of applications without requiring expensive excess storage capacity.

Dynamic storage management using virtual storage appliances

A network server analyzes a change in the network, including performing a machine-learning analysis of an extrapolation space. The server accesses observed data from multiple counters that each record samples for a metric in the network. The server performs a CART (classification and regression tree) analysis of the observed data to select the counters whose metrics affect a target network performance, such as latency. The server estimates an extrapolation space based on the observed data for the selected counters. The server then performs a machine-learning analysis of the extrapolation space based on a kriging model of the selected counters.

Growing community of inventors

Bengaluru, India

Vipul Mathur