Eric K. Butler

Evolution of storage management: transforming raw data into information

Exponential growth in storage requirements and an increasing number of heterogeneous devices and application policies are making enterprise storage management a nightmare for administrators. Back-of-the-envelope calculations, rules of thumb, and manual correlation of individual device data are too error prone for the day-to-day administrative tasks of resource provisioning, problem determination, performance management, and impact analysis. Storage management tools have evolved over the past several years from standardizing the data reported by storage subsystems to providing intelligent planners. In this paper, we describe that evolution in the context of the IBM Total Storage® Productivity Center (TPC)--a suite of tools to assist administrators in the day-to-day tasks of monitoring, configuring, provisioning, managing change, analyzing configuration, managing performance, and determining problems. We describe our ongoing research to develop ways to simplify and automate these tasks by applying advanced analytics on the performance statistics and raw configuration and event data collected by TPC using the popular Storage Management Initiative-Specification (SMI-S). In addition, we provide details of SMART (storage management analytics and reasoning technology) as a library that provides a collection of data-aggregation functions and optimization algorithms.

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.

Intelligent Information Lifecycle Management in Virtualized Storage Environments

Data or information lifecycle management (ILM) is the process of managing data over its lifecycle in a manner that balances cost and performance. The task is made difficult by datas continuously changing business value. If done well, it can lower costs through the increased use of cost-effective storage but also runs the risk of negatively impacting performance if data is inadvertently placed on the wrong device (e.g., low performance storage or on an over-utilized storage device). To address this challenge, we designed and developed the Intelligent Storage Tiering Manager (ISTM), an analytics-driven storage tiering tool that automates the process of load balancing data across and within different storage tiers in virtualized storage environments. Using administrator-generated policies, ISTM finds data with the specified performance profiles and automatically moves them to the appropriate storage tier. Application impact is minimized by limiting overall migration load and keeping data accessible during migration. Automation results in significantly less labor and errors while reducing task completion time from several days (and in some cases weeks) to a few hours. In this paper, we provide an overview of information lifecycle management (ILM), discuss existing solutions, and finally focus on the design and deployment of our ILM solution, ISTM, within a production data center.

Enterprise storage software lifecycle management system

Storage subsystems reside at the bottom-most layer of a contemporary data-center information technology (IT) stack. As with all other production software, the storage layers embedded software (or firmware ) must be constantly maintained in terms of upgrading it when new versions are released by hardware vendors. We study the problem of maintaining the currency of the storage software layer. We survey the typical processes that are used to keep storage firmware at recommended versions within a data center, and present an automated solution for this tedious and labor-intensive task. We present an approach to quantify the risk for continuing operations posed by stale firmware, a context-based approach for recommending target levels for devices with stale firmware, and an Upgrade Planner for rectifying the firmware of such devices. Our system has been deployed in the field using two approaches: as an operational tool used by hundreds of storage administrators within an outsourced IT context, and as an automated analytics component of an appliance-based approach within a maintenance services organization. We study the growth of the monitored infrastructure over two years. Finally, we show how our system drastically shrinks the time for an enterprise IT firmware upgrade cycle from days to minutes, and changes the nature of the complex task from a reactive to a proactive paradigm.