9,130 research outputs found
High-Performance Cloud Computing: A View of Scientific Applications
Scientific computing often requires the availability of a massive number of
computers for performing large scale experiments. Traditionally, these needs
have been addressed by using high-performance computing solutions and installed
facilities such as clusters and super computers, which are difficult to setup,
maintain, and operate. Cloud computing provides scientists with a completely
new model of utilizing the computing infrastructure. Compute resources, storage
resources, as well as applications, can be dynamically provisioned (and
integrated within the existing infrastructure) on a pay per use basis. These
resources can be released when they are no more needed. Such services are often
offered within the context of a Service Level Agreement (SLA), which ensure the
desired Quality of Service (QoS). Aneka, an enterprise Cloud computing
solution, harnesses the power of compute resources by relying on private and
public Clouds and delivers to users the desired QoS. Its flexible and service
based infrastructure supports multiple programming paradigms that make Aneka
address a variety of different scenarios: from finance applications to
computational science. As examples of scientific computing in the Cloud, we
present a preliminary case study on using Aneka for the classification of gene
expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape
dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter
Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft
DESIGN AND DELIVERY OF ELECTRONIC SERVICES: IMPLICATIONS FOR CUSTOMER VALUE IN ELECTRONIC FOOD RETAILING
Electronic food retailers can satisfy their customers more effectively if they understand how this particular market works. As in other service segments, the emergence of electronic business-to-customer services in the retail food industry poses questions for managers about the design of new food retailing services and the redesign of existing services for delivery through electronic channels. Important topics include characteristics of electronic service offerings, the typical operational configurations used to deliver electronic services, and the ways in which they relate to the effectiveness of electronic service delivery. We address this issue by developing a product-process matrix for understanding and analyzing electronic retailing services in general. We tailor the matrix to food retailing in particular. The product-process matrix allows electronic food retailers to determine in advance what features they need in a web site to serve their chosen market effectively.Consumer/Household Economics, Marketing, Research and Development/Tech Change/Emerging Technologies,
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
Ariadne: Analysis for Machine Learning Program
Machine learning has transformed domains like vision and translation, and is
now increasingly used in science, where the correctness of such code is vital.
Python is popular for machine learning, in part because of its wealth of
machine learning libraries, and is felt to make development faster; however,
this dynamic language has less support for error detection at code creation
time than tools like Eclipse. This is especially problematic for machine
learning: given its statistical nature, code with subtle errors may run and
produce results that look plausible but are meaningless. This can vitiate
scientific results. We report on Ariadne: applying a static framework, WALA, to
machine learning code that uses TensorFlow. We have created static analysis for
Python, a type system for tracking tensors---Tensorflow's core data
structures---and a data flow analysis to track their usage. We report on how it
was built and present some early results
- …