Automated planners for storage provisioning and disaster recovery S. Gopisetty E. Butler S. Jaquet M. Korupolu T. K. Nayak R. Routray M. Seaman A. Singh C.-H. Tan S. Uttamchandani A. Verma 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. Introduction With the continued growth toward petascale storage requirements, enterprise storage area networks (SANs) are increasing in size and complexity. Customers are continually purchasing and adding new storage subsystems, fabric switches, and servers. Management of these complex interconnected environments is a challenging task and is turning out to be the primary cost component in most data centers, often significantly more than hardware costs. With the increasing size and device types, several common tasks that were hitherto performed manually as an art, such as provisioning storage for workloads and disaster recovery (DR) planning, have become increasingly complex for administrators. Manual planning tends to be slow, expensive, and error prone and does not scale. Furthermore, until recently there were no suitable SAN resource management tools that were capable of collecting all of the necessary configuration and performance numbers from the various storage components into one place to facilitate automated planning and analysis. Hence, much of the planning work was done either by expert consultants or by extensive over-provisioning, both of which are expensive and intolerable as storage demands and costs rise. Recent developments in SAN monitoring tools have greatly advanced the state of the art. Products such as the IBM TotalStorage* Productivity Center (TPC) [1] and EMC ControlCenter** [2] provide a good foundation by monitoring SAN components and gathering the configuration and dynamic performance information in one common place with centralized control. By leveraging these advances in SAN management tools, we present two advanced planning tools that assist administrators in one of their most important, yet highly complex, tasks of application provisioning. Introducing a new application into a data center SAN is often a multiple-week activity requiring significant manual effort. A lot of complexity is introduced by ÓCopyright 2008 by International Business Machines Corporation. Copying in printed form for private use is permitted without payment of royalty provided that (1) each reproduction is done without alteration and (2) the Journal reference and IBM copyright notice are included on the first page. The title and abstract, but no other portions, of this paper may be copied by any means or distributed royalty free without further permission by computer-based and other information-service systems. Permission to republish any other portion of this paper must be obtained from the Editor. IBM J. RES. & DEV. VOL. 52 NO. 4/5 JULY/SEPTEMBER 2008 S. GOPISETTY ET AL. 1 0018-8646/08/$5.00 ª 2008 IBM