International Business Machines Corporation

Vmware, Inc.

National Security Agency

Globalfoundries Inc.

The United States of America As Represented by the National Security Agency

The United States of America As Represented by National Security Agency

Sandeep Madhav Uttamchandani

Seshashayee Sankarshana Murthy

Nagapramod S Mandagere

Pin Zhou

Li Zhou

Ramani Ranjan Routray

Kavita Chavda

Deng Liu

Sandeep Gopisetty

Aameek Singh

Elizabeth Suzanne Richards

Kaladhar Voruganti

David Allan Pease

Mickey Iqbal

Li Yin

Linda Marie Duyanovich

Kristal T Pollack

Mayank Rawat

Juan C Gomez

Fei Meng

John Davis Palmer

Edward Gustav Chron

Wei Zhang

Thomas A Phelan

Sandip Agarwala

Samdeep Nayak

Chung-hao Tan

Guillermo Alvarez

Ramkumar Vadivelu

Eben Merriam Haber

Cheng Zhang

David D Weck

Brian Cho

Joseph P McCloskey

Tara L Matthews

Sudarshan M Srinivasan

Joseph C Czap, Jr

Prasenjit Sakar

Akshat Verma

Xiaoyun Zhu

Mark Davis Smith

Mark Andrew Smith

Madhukar R Korupolu

Upendra Sharma

Sanjay Vasudev Acharya

Yin Wang

Mark James Seaman

Gauri Sudhir Shah

Norman Jerome Pass

Stefan Jaquet

Richard Ayala

Yizheng Chen

Jianzhe Tai

Yea-Cheng Wang

Eric W Davis Rozier

Kaladhar Vorugami

Sundarshan Srinivasan

Techniques for utilizing flash storage as an extension of hard disk (HDD) storage are provided. In one embodiment, a computer system stores a subset of blocks of a logical file in a first physical file, associated with a first data structure that represents a filesystem object, on flash storage and a subset of blocks, associated with a second data structure that represents a filesystem object comprising tiering configuration information that includes an identifier of the first physical file, in a second physical file on HDD storage. The computer system processes an I/O request directed to the logical file by directing it to either the physical file on the flash storage or the HDD storage by verifying that the tiering configuration information exists in the data structure and determining whether the one or more blocks are part of the first subset of blocks or the second subset of blocks.

Techniques for implementing hybrid flash/HDD-based virtual disk files

A distributed shared log storage system employs an adapter that translates Application Programming Interfaces (APIs) for a big data application to APIs of the distributed shared log storage system. An instance of an adapter is configured for different big data applications in accordance with a profile thereof, so that the big data applications can take on a variety of added characteristics to enhance the application and/or to improve the performance of the application. Included in the added characteristics are global or local ordering of operations, replication of operations according to different replication models, making the operations atomic and caching.

Distributed shared log storage system having an adapter for heterogenous big data workloads

A method and system for optimizing power consumption of a data center by dynamic workload adjustment. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Optimizing power consumption by dynamic workload adjustment

Embodiments are provided for managing performance of a computer system. Both implicit and explicit recommendations for processing of tasks are provided. System performance is tracked and evaluation based upon the actions associated with the task. Future recommendations of the same or other tasks are provided based upon implicit feedback pertaining to system performance, and explicit feedback solicited from a system administrator.

Dynamic recommendation framework for information technology management

A distributed shared log storage system employs an adapter that translates APIs for a big data application to APIs of the distributed shared log storage system. The adapter is configured for different big data applications in accordance with a profile thereof, so that storage performance using the distributed shared log storage system can be comparable to the storage performance of the profiled big data application. An over-utilized adapter instance is detected and the workload assigned to the over-utilized adapter instance is either moved to a different adapter instance that can handle the workload or split among two or more adapter instances.

The present invention proactively identifies hotspots in a cloud computing environment through cloud resource usage models that use workload parameters as inputs. In some embodiments the cloud resource usage models are based upon performance data from cloud resources and time series based workload trend models. Hotspots may occur and can be detected at any layer of the cloud computing environment, including the server, storage, and network level. In a typical embodiment, parameters for a workload are identified in the cloud computing environment and inputted into a cloud resource usage model. The model is run with the inputted workload parameters to identify potential hotspots, and resources are then provisioned for the workload so as to avoid these hotspots.

Proactive identification of hotspots in a cloud computing environment

Embodiments of the present invention provide an approach for adapting an information extraction middleware for a clustered computing environment (e.g., a cloud environment) by creating and managing a set of statistical models generated from performance statistics of operating devices within the clustered computing environment. This approach takes into account the required accuracy in modeling, including computation cost of modeling, to pick the best modeling solution at a given point in time. When higher accuracy is desired (e.g., nearing workload saturation), the approach adapts to use an appropriate modeling algorithm. Adapting statistical models to the data characteristics ensures optimal accuracy with minimal computation time and resources for modeling. This approach provides intelligent selective refinement of models using accuracy-based and operating probability-based triggers to optimize the clustered computing environment, i.e., maximize accuracy and minimize computation time.

Optimizing storage cloud environments through adaptive statistical modeling

A method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions. The programming instructions are operable to determine one or more optimal mappings between a server layer and a storage layer through a network layer based on performance metrics of one or more ports of at least one of the server layer, the storage layer and the network layer.

System and method for a storage area network virtualization optimization

A method and system for optimizing power consumption of a data center by dynamic workload adjustment. At least one candidate workload solution for the data center is generated. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of the at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Techniques for automatically allocating space in a flash storage-based cache are provided. In one embodiment, a computer system collects I/O trace logs for a plurality of virtual machines or a plurality of virtual disks and determines cache utility models for the plurality of virtual machines or the plurality of virtual disks based on the I/O trace logs. The cache utility model for each virtual machine or each virtual disk defines an expected utility of allocating space in the flash storage-based cache to the virtual machine or the virtual disk over a range of different cache allocation sizes. The computer system then calculates target cache allocation sizes for the plurality of virtual machines or the plurality of virtual disks based on the cache utility models and allocates space in the flash storage-based cache based on the target cache allocation sizes.

Automated space management for server flash cache

Methods are presented for caching I/O data in a solid state drive (SSD) locally attached to a host computer supporting the running of a virtual machine (VM). Portions of the SSD are allocated as cache storage for VMs running on the host computer. A mapping relationship is maintained between unique identifiers for VMs running on the host computer and one or more process identifiers (PIDs) associated with processes running in the host computer that correspond to each of the VM's execution on the host computer. When an I/O request is received, a PID associated with I/O request is determined and a unique identifier for the VM is extracted from the mapping relationship based on the determined PID. A portion of the SSD corresponding to the unique identifier of the VM that is used as a cache for the VM can then be accessed in order to handle the I/O request.

Method and system for VM-granular I/O caching

A method, system, and article are provided for managing performance of a computer system. Both implicit and explicit recommendations for processing of tasks are provided. System performance is tracked and evaluated based upon the actions associated with the task. Future recommendations of the same or other tasks are provided based upon implicit feedback pertaining to system performance, and explicit feedback solicited from a system administrator.

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

Growing community of inventors

San Jose, CA, United States of America

Sandeep Madhav Uttamchandani