International Business Machines Corporation

Prasenjit Sarkar

Reshu Jain

Karan Gupta

Dinesh Kumar Subhraveti

Rui Zhang

Zhenhua Hu

Yonggang Hu

Himabindu Pucha

Ramani Ranjan Routray

Roger Lee Haskin

David Lynn Merbach

Mark James Seaman

Soumitra Sarkar

Eric Kevin Butler

Chung-hao Tan

Sumant Padbidri

Frank Bernhard Schmuck

Kaladhar Voruganti

Sandeep Gopisetty

Mohit Saxena

Vikram Harakere Krishnamurthy

Sandeep R Patil

Riyazahamad Moulasab Shiraguppi

Lawrence Yiumchee Chiu

Aameek Singh

Madhukar R Korupolu

David Gregory Van Hise

Mihail Corneliu Constantinescu

Bala Rajaraman

Glen Hattrup

Philip Anthony Richardson

Michael L Y Li

Kiyoung Yang

Shah Mohammed Rezaul Islam

Craig Anthony Laverone

Ahmed Mohammad Bashir

Shiva Chaitanya

Ashmi Ashokkumar Chokshi

Zhe Xu

Ali R Butt

Gandhi Sivakumar

David Carroll Challener

Brent Cameron Beardsley

Min Li

Renu Tewari

Kavita Chavda

Michael Anthony Ko

Prashant Pandey

Lu Nguyen

Jimmy Paul Strickland

Krishnakumar Rao Surugucchi

Rajagopal Ananthanarayanan

Patrick Cozzi

Gauri Sudhir Shah

Ashish Chaurasia

Omer Ahmed Zaki

Mansi Ajit Shah

Richard J Ayala, Jr

Comeliu Mihail Constantinescu

Shah Mohammed Islam

Victor S Wen

Nicholas Lloyd Armstrong-Crews

Patrick Randolph Eaton

Surugucchi Krishnakumar Rao

Kaladhar Vorunganti

Deepak R Kenchammana-Hoskote

Samer Al Kiswany

A method for multi-layer quality of service (QoS) management in a distributed computing environment includes receiving a workload and identifying a workload QoS class for the workload. The workload is scheduled to be run compute nodes. The workload QoS class is translated to a plurality of different resource layer QoS classes, and the plurality of QoS classes are communicated to a workload execution manager of the compute nodes. The workload execution manager extends the plurality of different resource layer QoS classes to respective resource layers involved in the running of the workload.

Multi-layer QOS management in a distributed computing environment

A system for multi-layer quality of service (QoS) management in a distributed computing environment includes a management node hosting a workload scheduler configured to receive a workload and identify a workload QoS class for the workload. The workload scheduler schedules running of the workload on compute nodes, translate the workload QoS class to a plurality of different resource layer QoS classes, and communicates the plurality of QoS classes to a workload execution manager of the compute nodes. The workload execution manager is configured to extend the plurality of different resource layer QoS classes to respective resource layers involved in the running of the workload.

Multi-layer QoS management in a distributed computing environment

A method for recovering data for a file system includes a journal-less recovery process that detects one or more inconsistencies for file system blocks upon a system failure based on one or more comparisons of information for the file system blocks.

Journal-less recovery for nested crash-consistent storage systems

A system for multi-layer quality of service (QoS) management in a distributed computing environment includes a management node hosting a workload scheduler operable to receive a workload and identify a workload QoS class for the workload. The system also includes a plurality of distributed compute nodes. A workload scheduler schedules running of the workload on the compute nodes, translates the workload QoS class to a storage level QoS class, and communicates the storage level QoS class to a workload execution manager of the compute nodes. The workload scheduler communicates the storage level QoS class to one or more storage managers where the storage managers manage storage resources, and the storage managers are operable to extend the storage level QoS class to the storage resources to support the workload QoS class.

A technique for multi-layer quality of service (QoS) management in a distributed computing environment includes receiving a workload to run in a distributed computing environment. A workload quality of service (QoS) class for the workload is identified, and the workload QoS class is translated to a storage level QoS class. The workload is scheduled to run on a compute node of the environment, and the storage level QoS class is communicated to a workload execution manager of the compute node. The storage level QoS class is communicated to one or more storage managers of the environment where the storage managers manage storage resources in the environment. The storage managers extend the storage level QoS class to the storage resources to support the workload QoS class.

Embodiments of the present invention relate to a new data center architecture that provides for efficient processing in distributed analytics applications. In one embodiment, a subnode of a distributed processing node is provided. The subnode includes at least one processor core operatively connected to a memory. A first interconnect operatively connects to the subnode. A second interconnect operatively connects the subnode to a storage. The storage includes a first storage unit and a second storage unit. The second storage unit has lower access time and latency than the first storage unit. A storage manager is provided that is operative to allocate data between the first and second storage units based on access patterns. The storage manager preferentially relocates non-sequentially accessed data to the second storage unit from the first storage unit.

Modular architecture for extreme-scale distributed processing applications

A filesystem includes a pool of data blocks and a pool of index blocks. Non-overlapping subpools of data blocks, including a first subpool of data blocks and a second subpool of data blocks, are identified within the pool of data blocks. Likewise, non-overlapping subpools of index blocks, including a first subpool of index blocks and a second subpool of index blocks, are identified within the pool of index blocks. A first file group is mounted within a global namespace on a first mount point on the filesystem. A second file group is also mounted within the global namespace on a second mount point on the filesystem. The first subpool of data blocks and the first subpool of index blocks are allocated to the first mounted file group, and the second subpool of data blocks and the second subpool of index blocks are allocated to the second mounted file group.

Filesystem with isolated independent filesets

A method for storing and recovering data for a file system includes a write process that performs a write instruction for data that includes atomically updating corresponding data blocks and metadata-links on a storage medium. The metadata-links contain links from data blocks to parent metadata blocks and links from metadata blocks to the parent metadata blocks. A journal-less recovery process that detects one or more inconsistencies for file system blocks comprising data blocks or metadata blocks upon a system failure based on one or more comparisons of information for the file system blocks.

A method for replicating a program and data storage according to one embodiment comprises sending program replication data from a first program to a second program, the second program having an application program that is a replica of an application program of the first program; sending data storage requests from the first program to a first storage system; and replicating data stored in the first storage system in a second storage system. Additional methods, systems, and computer program products are disclosed.

Continuously available program replicas

A system for multi-layer quality of service (QoS) management in a distributed computing environment includes a management node hosting a workload scheduler operable to receive a workload and identify a workload QoS class for the workload. The system also includes a plurality of distributed compute nodes. A workload scheduler is operable to schedule running of the workload on the compute nodes. The workload scheduler is operable to translate the workload QoS class to a storage level QoS class and communicate the storage level QoS class to a workload execution manager of the compute nodes. The workload scheduler communicates the storage level QoS class to one or more storage managers where the storage managers manage storage resources, and the storage managers are operable to extend the storage level QoS class to the storage resources to support the workload QoS class.

A method for managing storage allocation includes adaptively determining, by a storage device processor, a region width across disk spaces for a group of storage devices that is inversely proportional to a number of nodes sharing a particular storage device. An adaptive storage device allocation region of the particular storage device is created based on the determined region width across the disk spaces for the group of storage devices.

Generalized storage allocation for multiple architectures

Embodiments of the present invention provide an approach for migrating virtual machines across network (e.g., WAN) separated data centers (e.g., storage clouds). Specifically, under embodiments of the present invention, a first storage system associated with a first data center is synchronized with a second storage system associated with a second data center via a storage system link. Then, a minimal state of a virtual machine is migrated from a first computer in the first data center to a second computer in the second data center via a WAN link. Using the minimal state, the virtual machine is stored in the second computer. Thereafter, the storage system link is terminated. In addition, as updated pages are received in memory of the first computer, they are migrated to the second computer via the WAN link. Once this migration is complete, the WAN link can be terminated. Therefore, embodiments of the present invention provide at least two forms of synchronization: computational synchronization and storage synchronization.

Migrating virtual machines across network separated data centers

A system for multi-layer quality of service (QoS) management in a distributed computing environment includes: a management node hosting a workload scheduler operable to receive a workload and identify a workload QoS class for the workload; and a plurality of distributed compute nodes, the workload scheduler operable to schedule running of the workload on the compute nodes. The workload scheduler is operable to: translate the workload QoS class to a storage level QoS class; communicate the storage level QoS class to a workload execution manager of the compute nodes; and communicate the storage level QoS class to one or more storage managers, the storage managers managing storage resources. The storage managers are operable to extend the storage level QoS class to the storage resources to support the workload QoS class.

A technique for multi-layer quality of service (QoS) management in a distributed computing environment includes: receiving a workload to run in a distributed computing environment; identifying a workload quality of service (QoS) class for the workload; translating the workload QoS class to a storage level QoS class; scheduling the workload to run on a compute node of the environment; communicating the storage level QoS class to a workload execution manager of the compute node; communicating the storage level QoS class to one or more storage managers of the environment, the storage managers managing storage resources in the environment; and extending, by the storage managers, the storage level QoS class to the storage resources to support the workload QoS class.

Growing community of inventors

San Jose, CA, United States of America

Prasenjit Sarkar