Cisco Technology, Inc.

Yathiraj B Udupi

Debojyoti Dutta

Aparupa Das Gupta

Xinyuan Huang

Kai Zhang

Johnu George

Rahul Ramakrishna

Marc Solanas Tarre

Madhav V Marathe

Raghunath Nambiar

Rishabh Jain

Lewis Wiley Tucker

Manoj Sharma

Wei Sun

Ralf Rantzau

Komei Shimamura

Ganesh Rajan

Seth Mason

Amit Agarwal

Timothy Okwii

Sarvesh Ranjan

Olivia Zhang

Embodiments include receiving an indication of a data storage module to be associated with a tenant of a distributed storage system, allocating a partition of a disk for data of the tenant, creating a first association between the data storage module and the disk partition, creating a second association between the data storage module and the tenant, and creating rules for the data storage module based on one or more policies configured for the tenant. Embodiments further include receiving an indication of a type of subscription model selected for the tenant, and selecting the disk partition to be allocated based, at least in part, on the subscription model selected for the tenant. More specific embodiments include generating a storage map indicating the first association between the data storage module and the disk partition and indicating the second association between the data storage module and the tenant.

Tenant-level sharding of disks with tenant-specific storage modules to enable policies per tenant in a distributed storage system

A method for ranking detected anomalies is disclosed. The method includes generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule. The method further includes ranking nodes of the graph based on the edge weights. The method further includes ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies.

Intelligent anomaly identification and alerting system based on smart ranking of anomalies

In one embodiment, a method for serverless computing comprises: receiving a task definition, wherein the task definition comprises a first task and a second task chained to the first task; adding the first task and the second task to a task queue; executing the first task from the task queue using hardware computing resources in a first serverless environment associated with a first serverless environment provider; and executing the second task from the task queue using hardware computing resources in a second serverless environment selected based on a condition on an output of the first task.

Serverless computing and task scheduling

In one embodiment, a server in a network reports one or more symptoms of a monitored device that is malfunctioning to a user interface via a particular chatbot session. The server receives, via the particular chatbot session, a triage request to enter a triage mode regarding the one or more reported symptoms. The server predicts a corrective action using the one or more reported symptoms as input to a machine learning model. The machine learning model is trained using a history of observed symptoms in the network, a history of corrective actions initiated via chatbot sessions and associated with the observed symptoms, and a history of feedback regarding the corrective actions received via the chatbot sessions. The server provides the predicted corrective action to the user interface via the particular chatbot session as a suggested corrective action, in response to the received triage request.

STAB: smart triaging assistant bot for intelligent troubleshooting

The present disclosure describes a method for cloud resource placement optimization. A resources monitor monitors state information associated with cloud resources and physical hosts in the federated cloud having a plurality of clouds managed by a plurality of cloud providers. A rebalance trigger triggers a rebalancing request to initiate cloud resource placement optimization based on one or more conditions. A cloud resource placement optimizer determines an optimized placement of cloud resources on physical hosts across the plurality of clouds in the federated cloud based on (1) costs including migration costs, (2) the state information, and (3) constraints, wherein each physical host is identified in the constraints-driven optimization solver by an identifier of a respective cloud provider and an identifier of the physical host. A migrations enforcer determines an ordered migration plan and transmits requests to place or migrate cloud resources according to the ordered migration plan.

Cloud resource placement optimization and migration execution in federated clouds

In an example, there is disclosed a logging server computing apparatus, having: a processor; a memory; and a logging engine to: analyze a network; build an entity-state matrix M from an entity vector e and a state vector s; determine that there is a strong correlation between an entity eand a state s; and report the strong correlation.

Entity-state relationship correlation

Systems and methods are described for allocating resources in a cloud computing environment. The method includes receiving a computing request, the request for use of at least one virtual machine and a portion of memory. In response to the request, a plurality of hosts is identified and a cost function is formulated using at least a portion of those hosts. Based on the cost function, at least one host that is capable of hosting the virtual machine and memory is selected.

Optimizing placement of virtual machines

In one embodiment, a device in a network aggregates values for a set of key performance indicators (KPIs) for a system the network to form a plurality of KPI states. The device associates a plurality of observed performance metric values from the system with the KPI states. The device constructs a machine learning-based decision tree. Internal vertices of the decision tree represent conditions for the plurality of observed performance metric values and leaves of the tree represent the KPI states. The device predicts a KPI state by using the machine learning-based decision tree to analyze live performance metric values streamed from the system. The device generates a proactive alert based on the predicted KPI state.

Probabilistic and proactive alerting in streaming data environments

In one embodiment, a device in a network receives a first plurality of measurements for network metrics captured during a first time period. The device determines a first set of correlations between the network metrics using the first plurality of measurements captured during the first time period. The device receives a second plurality of measurements for the network metrics captured during a second time period. The device determines a second set of correlations between the network metrics using the second plurality of measurements captured during the second time period. The device identifies a difference between the first and second sets of correlations between the network metrics as a network anomaly.

Multi-dimensional system anomaly detection

The present disclosure describes a distributed, advertisement-based, solution for scheduling virtual resources in cloud infrastructures such as the OpenStack. The scheduling algorithm distributes the scheduling requirements and host state feasibility checks to the individual hosts in the datacenter, which can periodically send a summarized advertisement to the scheduler controller listing the number of instances of different type(s) of virtual resources that a particular host can support. The scheduler controller, thus no longer has to compute and maintain individual host states, and the scheduling problem is reduced to selecting the feasible advertisements that satisfy a given request. The solution can be extended to a scenario of multiple scheduler controllers using the same distributed, advertisement-based, approach.

Periodic advertisements of host capabilities in virtual cloud computing infrastructure

The present disclosure describes a method for virtual machine placement optimization based on generalized organizational scenarios. The method involves defining a variable matrix (wherein each entry of the variable matrix indicate whether a particular virtual machine is to be placed on a particular host server), a first set of variables (wherein each variable of the first set of variables indicate whether a particular host server has at least one virtual machine to be placed thereon), a second set of variables (wherein the second set of variables indicates for all possible pairs of host servers whether two particular host servers both have at least one virtual machine to be placed thereon). The method further involves determining a set of virtual machine to host server allocations by solving a constraints optimization problem over the first set of variables and the second set of variables based on a generalized organizational scenario.

Virtual machine placement optimization with generalized organizational scenarios

Embodiments include obtaining at least one system metric of a distributed storage system, generating one or more recovery parameters based on the at least one system metric, identifying at least one policy associated with data stored in a storage node of a plurality of storage nodes in the distributed storage system, and generating a recovery plan for the data based on the one or more recovery parameters and the at least one policy. In more specific embodiments, the recovery plan includes a recovery order for recovering the data. Further embodiments include initiating a recovery process to copy replicas of the data from a second storage node to a new storage node, wherein the replicas of the data are copied according to the recovery order indicated in the recovery plan.

Growing community of inventors

San Jose, CA, United States of America

Yathiraj B Udupi