Reshu Jain

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.

GPFS-SNC: an enterprise cluster file system for big data

A new class of data-intensive applications commonly referred to as Big Data applications (e.g., customer sentiment analysis based on click-stream logs) involves processing massive amounts of data with a focus on semantically transforming the data. This class of applications is massively parallel and well suited for the MapReduce programming framework that allows users to perform large-scale data analyses such that the application execution layer handles the system architecture, data partitioning, and task scheduling. In this paper, we introduce GPFS-SNC (General Parallel File System for Shared Nothing Clusters), a scalable file system that operates over a cluster of commodity machines and direct-attached storage and meets the requirements of analytics and traditional applications that are typically used together in analytics solutions. The architecture extends an existing enterprise cluster file system to support these emerging classes of workloads by applying five innovative optimizations: 1) locality awareness to allow compute jobs to be scheduled on nodes where the data resides, 2) metablocks that allow large and small block sizes to co-exist in the same file system to meet the needs of different types of applications, 3) write affinity that allows applications to dictate the layout of files on different nodes in order to maximize both write and read bandwidth, 4) pipelined replication to maximize use of network bandwidth for data replication, and 5) distributed recovery to minimize the effect of failures on ongoing computation.

Getting your big data priorities straight: a demonstration of priority-based QoS using social-network-driven stock recommendation

As we come to terms with various big data challenges, one vital issue remains largely untouched. That is the optimal multiplexing and prioritization of different big data applications sharing the same underlying infrastructure, for example, a public cloud platform. Given these demanding applications and the necessary practice to avoid over-provisioning, resource contention between applications is inevitable. Priority must be given to important applications (or sub workloads in an application) in these circumstances. This demo highlights the compelling impact prioritization could make, using an example application that recommends promising combinations of stocks to purchase based on relevant Twitter sentiment. The application consists of a batch job and an interactive query, ran simultaneously. Our underlying solution provides a unique capability to identify and differentiate application workloads throughout a complex big data platform. Its current implementation is based on Apache Hadoop and the IBM GPFS distributed storage system. The demo showcases the superior interactive query performance achievable by prioritizing its workloads and thereby avoiding I/O bandwidth contention. The query time is 3.6 × better compared to no prioritization. Such a performance is within 0.3% of that of an idealistic system where the query runs without contention. The demo is conducted on around 3 months of Twitter data, pertinent to the S & P 100 index, with about 4 × 1012 potential stock combinations considered.

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.