Reducing data movement costs using energy-efficient, active computation on ssd

ABSTRACT Modern scientific discovery often involves running complex application simulations on supercomputers, followed by a sequence of data analysis tasks on smaller clusters. This offline approach suffers from significant data movement costs such as redundant I/O, storage bandwidth bottleneck, and wasted CPU cycles, all of which contribute to increased energy consumption and delayed end-toend performance. Technology projections for an exascale machine indicate that energy-efficiency will become the primary design metric. It is estimated that the energy cost of data movement will soon rival the cost of computation. Consequently, we can no longer ignore the data movement costs in data analysis. To address these challenges, we advocate executing data analysis tasks on emerging storage devices, such as SSDs. Typically, in extreme-scale systems, SSDs serve only as a temporary storage system for the simulation output data. In our approach, Active Flash, we propose to conduct in-situ data analysis on the SSD controller without degrading the performance of the simulation job. By migrating analysis tasks closer to where the data resides, it helps reduce the data movement cost. We present detailed energy and performance models for both active flash and offline strategies, and study them using extreme-scale application simulations, commonly used data analytics kernels, and supercomputer system configurations. Our evaluation suggests that active flash is a promising approach to alleviate the storage bandwidth bottleneck, reduce the data movement cost, and improve the overall energy efficiency

Replica selection in the Globus Data Grid.

The Globus Data Grid architecture provides a scalable infrastructure for the management of storage resources and data that are distributed across Grid environments. These services are designed to support a variety of scientific applications, ranging from high-energy physics to computational genomics, that require access to large amounts of data (terabytes or even petabytes) with varied quality of service requirements. By layering on a set of core services, such as data transport, security, and replica cataloging, one can construct various higher-level services. In this paper, we discuss the design and implementation of a high-level replica selection service that uses information regarding replica location and user preferences to guide selection from among storage replica alternatives. We first present a basic replica selection service design, then show how dynamic information collected using Globus information service capabilities concerning storage system properties can help improve and optimize the selection process. We demonstrate the use of Condor's ClassAds resource description and matchmaking mechanism as an efficient tool for representing and matching storage resource capabilities and policies against application requirements