Automated planners for storage provisioning and disaster recovery

Introducing an application into a data center involves complex interrelated decision-making for the placement of data (where to store it) and resiliency in the event of a disaster (how to protect it). Automated planners can assist administrators in making intelligent placement and resiliency decisions when provisioning for both new and existing applications. Such planners take advantage of recent improvements in storage resource management and provide guided recommendations based on monitored performance data and storage models. For example, the IBM Provisioning Planner provides intelligent decision-making for the steps involved in allocating and assigning storage for workloads. It involves planning for the number, size, and location of volumes on the basis of workload performance requirements and hierarchical constraints, planning for the appropriate number of paths, and enabling access to volumes using zoning, masking, and mapping. The IBM Disaster Recovery (DR) Planner enables administrators to choose and deploy appropriate replication technologies spanning servers, the network, and storage volumes to provide resiliency to the provisioned application. The DR Planner begins with a list of high-level application DR requirements and creates an integrated plan that is optimized on criteria such as cost and solution homogeneity. The Planner deploys the selected plan using orchestrators that are responsible for failover and failback.

[1]  Eric Anderson,et al.  Quickly finding near-optimal storage designs , 2005, TOCS.

[2]  Dirk Beyer,et al.  Designing for Disasters , 2004, FAST.

[3]  Benny Rochwerger,et al.  Oceano-SLA based management of a computing utility , 2001, 2001 IEEE/IFIP International Symposium on Integrated Network Management Proceedings. Integrated Network Management VII. Integrated Management Strategies for the New Millennium (Cat. No.01EX470).

[4]  John Wilkes,et al.  Seneca: remote mirroring done write , 2003, USENIX Annual Technical Conference, General Track.

[5]  Julie Ward,et al.  Appia: Automatic Storage Area Network Fabric Design , 2002, FAST.

[6]  Joel L. Wolf,et al.  The placement optimization program: a practical solution to the disk file assignment problem , 1989, SIGMETRICS '89.

[7]  Kimberly Keeton,et al.  A framework for evaluating storage system dependability , 2004, International Conference on Dependable Systems and Networks, 2004.

[8]  A. Chervenak,et al.  Protecting File Systems : A Survey of Backup Techniques , 1998 .

[9]  Ram Swaminathan,et al.  Ergastulum: Quickly fi nding near-optimal storage system designs , 2001 .

[10]  Eric Anderson,et al.  Proceedings of the Fast 2002 Conference on File and Storage Technologies Hippodrome: Running Circles around Storage Administration , 2022 .

[11]  Arif Merchant,et al.  Minerva: An automated resource provisioning tool for large-scale storage systems , 2001, TOCS.

[12]  Mark Sheehan,et al.  Online survey results , 2003 .

[13]  Alain Azagury Point-in-Time Copy: Yesterday, Today and Tomorrow , 2002 .

[14]  Dirk Beyer,et al.  On the road to recovery: restoring data after disasters , 2006, EuroSys '06.

[15]  Joseph Hall,et al.  An Experimental Study of Data Migration Algorithms , 2001, WAE.

[16]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[17]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[18]  William H. Sanders,et al.  Designing Dependable Storage Solutions for Shared Application Environments , 2010, IEEE Trans. Dependable Secur. Comput..

[19]  Ronald L. Rivest,et al.  Introduction to Algorithms, Second Edition , 2001 .