Mark James Seaman

GPFS-SNC: an enterprise storage framework for virtual-machine clouds

In a typical cloud computing environment, the users are provided with storage and compute capacity in the form of virtual machines. The underlying infrastructure for these services typically comprises large distributed clusters of commodity machines and direct-attached storage in concert with a server virtualization layer. The focus of this paper is on an enterprise storage framework that supports the timely and resource-efficient deployment of virtual machines in such a cloud environment. The proposed framework makes use of innovations in the General Parallel File System-Shared Nothing Clusters (GPFS®-SNC) file system, supports optimal allocation of resources to virtual machines in a hypervisor-agnostic fashion, achieves low latency when provisioning for new virtual machines, and adapts to the input-output needs of each virtual-machine instance in order to achieve high performance for all types of applications.

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.

CARP: Handling Silent Data Errors and Site Failures in an Integrated Program and Storage Replication Mechanism

This paper presents CARP, an integrated program and storage replication solution. CARP extends program replication systems which do not currently address storage errors, builds upon a record-and-replay scheme that handles nondeterminism in program execution, and uses a scheme based on recorded program state and I/O logs to enable efficient detection of silent data errors and efficient recovery from such errors. CARP is designed to be transparent to applications with minimal run-time impact and is general enough to be implemented on commodity machines. We implemented CARP as a prototype on the Linux operating system and conducted extensive sensitivity analysis of its overhead with different application profiles and system parameters. In particular, we evaluated CARP with standard unmodified email, database, and web server benchmarks and showed that it imposes acceptable overhead while providing sub-second program state recovery times on detecting a silent data error.