Norbert G. Vogl

Galapagos: model-driven discovery of end-to-end application-storage relationships in distributed systems

Modern business information systems are typically multitiered distributed systems comprising Web services, application services, databases, enterprise information systems, file systems, storage controllers, and other storage systems. In such environments, data is stored in different forms at multiple tiers, with each tier associated with some level of data abstraction. An information entity owned by an application generally maps to several data entities, logically associated across tiers and related to the application. Discovery of such relationships in a distributed system is a challenging problem, complicated by the widespread adoption of virtualization technologies and by the traditional tendency to manage each tier as an independent domain. In this paper, we present a system and methodology for model-driven discovery of end-to-end application-data relationships spanning multiple tiers, from the applications to the lowest levels of the storage hierarchy. The key to our methodology involves modeling how data is used and transformed by distributed software components. An important benefit of our system, which we call Galapagos, is the ability to reflect business decisions expressed at the application level to the level of storage.

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.

Capacity planning tools for web and grid environments

A key aspect in managing resources for customer sites is to predict and assess the load associated with a site in order to figure out how best to allocate resources for the site over time and to efficiently schedule tasks. The cost associated with the site and return on investment are also key parameters. This paper describes work we have done in developing tools for answering these critical questions. The tools use both analytical models and discrete event simulations to predict performance and analyze costs needed for handling a customer workload while satisfying the service level objectives. These tools provide capacity and load planning, performance simulation, and cost and financial analyses. Our tools have been used successfully by several major customers, and those experiences have shaped how the tools have evolved over time.

ITBVM: IT Business Value Modeler

Today, enterprise IT environments are complex as never before with individual applications, tiers, or technologies segregated into individual management domains. Typically, the value of business applications and the dependencies between business and IT objects and IT objects among each other is completely unknown or at least not up to date. Thus, ultimately, the business value of individual IT tasks is unknown. Hence it is very hard to perform global management services such as performance optimization in resource-constrained environments. This deficiency is even more deeply felt by an internal or external services provider called in to set up a new optimization or IT management framework.We propose a framework ITBVM for business-value driven IT optimization with particular emphasis on such enterprise environments. A key part is the use of discovery technologies to provide the link between business value and IT objects. As one instance of the framework, we show how discovery can improve a performance-optimization problem in an otherwise blackbox scenario. We validate these improvements through experiments in a controlled setup and through statistical interpretation of fine-grained dependency discovery in a large real enterprise environment.

Workload Monitoring in Hybrid Clouds

In this paper, we describe architecture and design of an innovative integrated monitoring solution for monitoring workloads in a hybrid cloud. Using off-the-shelf commercially available monitoring products and services, the solution described here allows for monitoring of a workload deployed across public and private clouds separated by enterprise firewall. The monitored data from endpoints distributed across the hybrid cloud is aggregated in a centralized monitoring system for analysis and for performing higher level management functions. Our solution is automated and can be integrated with other IT service management functions. The solution can leverage any endpoint monitoring functions and services a cloud service provider may offer. The monitoring framework described in this paper forms the basis for the hybrid cloud monitoring capability in IBMs hybrid cloud offering,IBM Service Management Extensions for Hybrid Cloud.