Chung-Hao Tan

Efficient and agile storage management in software defined environments

The IT industry is experiencing a disruptive trend for which the entire data center infrastructure is becoming software defined and programmable. IT resources are provisioned and optimized continuously according to a declarative and expressive specification of the workload requirements. The software defined environments facilitate agile IT deployment and responsive data center configurations that enable rapid creation and optimization of value-added services for clients. However, this fundamental shift introduces new challenges to existing data center management solutions. In this paper, we focus on the storage aspect of the IT infrastructure and investigate its unique challenges as well as opportunities in the emerging software defined environments. Current state-of-the-art software defined storage (SDS) solutions are discussed, followed by our novel framework to advance the existing SDS solutions. In addition, we study the interactions among SDS, software defined compute (SDC), and software defined networking (SDN) to demonstrate the necessity of a holistic orchestration and to show that joint optimization can significantly improve the effectiveness and efficiency of the overall software defined environments.

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.

Building end-to-end management analytics for enterprise data centers

The complexity of modern data centers has evolved significantly in recent years. One typically is comprised of a large number and types of middleware and applications that are hosted in a heterogeneous pool of both physical and virtual servers, connected by a complex web of virtual and physical networks. Therefore, to manage everything in a data center, system administrators usually need a plethora of management tools since one tool often manages only one type of devices. The boundaries between the different management tools can limit productivity of system administrators on their daily tasks as each tool only offers a partial view of the entire managed environment. As a result, advanced analytics such as impact analysis and problem determination are generally not achievable using the traditional management tools as they require a holistic view of the entire data center. In this paper, we describe an integrated management system for applications, servers, network and storage devices called DataGraph. Our system integrates data across heterogeneous point products and agents for management and monitoring to enable the above mentioned management analytics capabilities. A common data model is introduced to federate data collected by the different tools in multiple database repositories so no modifications are needed to existing management tools. A common integrated web user interface is implemented to facilitate management tasks that would otherwise require invoking multiple tools. We deployed this tool in a lab environment and demonstrated these analytics capabilities through several case studies.

A data-driven storage recommendation service for multitenant storage management environments

Storage management aims to improve the data center performance by optimizing the underlying storage resources more efficiently. The advent of cloud computing technologies introduces a paradigm shift from conventional on-premise storage management solutions to multitenant storage management as a service models. The advantage of centralized multitenant storage management platforms lies in the integrated procedures and the unified platform of data collection, governance, and analytics to gauge the effectiveness and efficiency of tenant storage environments. In this paper, we introduce our data-driven storage optimization framework, which is a centralized storage data analytics module to provide recommendations for the storage administrator. We use three example use cases to illustrate the exploitation of individual data center operational data to obtain actionable insights for better storage management solutions.