Hewlett-packard Development Company, L.p.

Vmware, Inc.

Future Wei Technologies, Inc.

Netapp, Inc.

Xiaoyun Zhu

Sharad Singhal

Zhikui Wang

Pradeep Padala

Martin Arlitt

Parthasarathy Ranganathan

Rean Griffith

Anne Holler

Dirk Manfred Beyer

Xue Liu

Cipriano A Santos

Lei Lu

Jerry Rolia

Christos Karamanolis

Vanish Talwar

Aashish Parikh

Vijayaraghavan Soundararajan

Puneet Zaroo

Sahan Bamunavita Gamage

Parth Shah

Magnus Karlsson

Scott Alan Rhine

Kalyan Saladi

Adarsh Jagadeeshwaran

Madhuri Yechuri

Shankari Kalyanaraman

Pinar Yanardag Delul

Cullen Edwin Bash

Puneet Sharma

Jing Ye

Li Zhou

Arif Abdulhusein Merchant

Sandeep Madhav Uttamchandani

Wei Zhang

Manish Marwah

Wei Xu

Ira Cohen

Yue Chen

Sujata Banerjee

Mustafa Uysal

Ram Swaminathan

Jerome Alexander Rolia

Priya Mahadevan

Mayank Rawat

Christopher H Stewart

Alex Xin Zhang

Deng Liu

Niraj Tolia

Clifford A McCarthy

Terence Patrick Kelly

Julie Ward Drew

Akhil Sahai

Troy Don Miller

Artur Andrzejak

Nathaniel Morris

Harlan Crowder

Daniel Chen

Kenneth Salem

Ramya Raghavendra

Tom Turicchi

David Lee

Jinzhong Zhang

Huichao Zhao

Sid Askary

Cj Hersh

Shu Zhang

Jianzhe Tai

Brandon Heller

Ciprianos Santos

Identifying a likely cause of a problem in an application can include: injecting a series of a set of predetermined faults into the application; sampling a subset of a set of training features from the application during each predetermined fault injected into the application and labeling each subset with an identifier of the corresponding predetermined fault; and training a classifier to identify the likely cause by associating a set of real-time features sampled from the application that pertain to the problem to one or more of the predetermined faults in response to the training features.

Diagnosing application problems by learning from fault injections

A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.

Crowd-sourced operational metric analysis of virtual appliances

An apparatus and associated method are provided for application deployment assessment. In use, a plurality of deployment parameters associated with one or more applications, and a workload profile are received. Further, an application deployment specification is generated, based on the workload profile and the deployment parameters. Still yet, a type of one or more orchestrators on one or more systems is identified. The application deployment specification is processed, based on the identified type of the one or more orchestrators on the one or more systems. Further, the one or more processors execute the instructions to deploy, via an application program interface (API), the one or more applications to the one or more orchestrators on at least one of the one or more systems, and at least one workload generator to at least one of the one or more systems, utilizing the processed application deployment specification. Operational data is collected from one or more monitoring agents on the one or more systems. One or more statistics are generated for assessing the deployment of the one or more applications, based on the operational data.

Apparatus and method for application deployment assessment

An automatic scaling system and method for reducing state space in reinforced learning for automatic scaling of a multi-tier application uses a state decision tree that is updated with new states of the multi-tier application. When a new state of the multi-tier application is received, the new state is placed in an existing node of the state decision tree only if a first attribute of the new state is same as a first attribute of any state contained in the existing node and a second attribute of the new state is sufficiently similar to a second attribute of each existing state contained in the existing node based on a similarity measurement of the second attribute of each state contained in the existing node with the second attribute of the new state.

System and method for reducing state space in reinforced learning by using decision tree classification

A management server and method for performing resource management operations in a distributed computer system utilizes interference scores for clients executing different workloads to create an interference affinity-type rule for at least some of the clients contending for a resource based on the interference scores for that resource. The interference affinity-type rule can then be used to recommend a target host computer to place a client.

Interference-aware client placement

A management server and method for performing resource management operations in a distributed computer system utilizes interference scores for clients executing different workloads, including a client to be placed in the distributed computer system, as utilization values of resources, which are assigned continuously variable weights to produce weighted resource utilization values. The weighted resource utilization values are used to generate overall selection scores for host computers of the distributed compute system, which are then used to recommend a target host computer among the host computers of the distributed computer system to place the client.

Interference-based client placement using dynamic weights

A system and method for managing resources in a distributed computer system that includes at least one resource pool for a set of virtual machines (VMs) utilizes a set of desired individual VM-level resource settings that corresponds to target resource allocations for observed performance of an application running in the distributed computer system. The set of desired individual VM-level resource settings are determined by constructing a model for the observed application performance as a function of current VM-level resource allocations and then inverting the function to compute the target resource allocations in order to meet at least one user-defined service level objective (SLO). The set of desired individual VM-level resource settings are used to determine final RP-level resource settings for a resource pool to which the application belongs and final VM-level resource settings for the VMs running under the resource pool, which are then selectively applied.

Performance-driven resource management in a distributed computer system

Techniques for dynamically managing the placement of blocks of a logical file between a flash storage tier and an HDD storage tier are provided. In one embodiment, a computer system can collect I/O statistics pertaining to the logical file, where a first subset of blocks of the logical file are stored on the flash storage tier and where a second subset of blocks of the logical file are stored on the HDD storage tier. The computer system can further generate a heat map for the logical file based on the I/O statistics, where the heat map indicates, for each block of the logical file, the number of times the block has been accessed. The computer system can then identify, using the heat map, one or more blocks of the logical file as being performance-critical blocks, and can move data between the flash and HDD storage tiers such that the performance-critical blocks are placed on the flash storage tier.

Techniques for dynamically relocating virtual disk file blocks between flash storage and HDD-based storage

A technique for controlling an output of a computing system having multiple adjustable inputs includes providing a set of adjustable inputs to the computing system, observing an output of the computing system while the system is in operation, and selecting a subset of adjustable inputs from the set of adjustable inputs based on the observation of the output. The inputs in the selected subset are then adjusted to achieve a desired output of the computing system.

Control of a computing system having adjustable inputs

A method for controlling resource allocation is provided. The method includes determining a service metric associated with a first application, wherein the first application is associated with one or more virtual machines. The method further includes comparing the service metric to an application specific service level goal associated with the first application and modifying a resource allocation associated with the first application at one or more of the virtual machines.

Dynamic feedback control of resources in computing environments

A resource consumption of a resource compartment for a time interval is predicted. A resource entitlement for the resource compartment for the time interval is determined based on the predicted resource consumption.

Predictive control for resource entitlement

Embodiments of the present invention pertain to dynamically resizing a virtual machine container. According to one embodiment, an optimal utilization is determined based on a desired performance for a multi-tiered application and transaction mix information that describes a mix of transactions that result from executing the multi-tiered application. Automatic modification of an amount of a physical resource a virtual machine container is entitled to for executing a particular application component associated with the multi-tiered application is enabled where the amount is automatically modified based on the optimal utilization.

Dynamically resizing a virtual machine container

A system for controlling power consumption of a network includes at least one terminal to receive a plurality of requests to route data from a plurality of data sources to a plurality of data sinks, where the data sources and the data sinks are connected to each other through a plurality of network nodes forming the network, and a network configuration unit. The network configuration unit includes a selection module configured to select a configuration of the network nodes that allows the network to have a lowest overall power consumption of the network among a plurality of configurations of the network, and an output module configured to output a plurality of instruction signals to the network nodes to perform the network configuration. A network path for transmitting a network flow is selected that does not allow the network flow to be split and flow through another network path.

System for controlling power consumption of a network

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Cupertino, CA, United States of America

Xiaoyun Zhu