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Book Sections Year : 2017

The HARNESS Platform: A Hardware-and Network-Enhanced Software System for Cloud Computing

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Abstract

HARNESS is a next generation cloud-computing platform that offers commodity and specialized resources in support of large-scale data processing applications. We focus primarily on application domains that are currently not well supported by today's cloud providers, including the areas of scientific computing, business-analytics, and online machine learning. These applications often require acceleration of critical operations using devices such as FPGAs, GPGPUs, network middleboxes, and SSDs. We explain the architectural principles that underlie the HARNESS platform, including the separation of agnostic and cognizant resource management that allows the platform to be resilient to heterogeneity while leveraging its use. We describe a prototype implementation of the platform, which was evaluated using two testbeds: (1) a heterogeneous compute and storage cluster that includes FPGAs and SSDs and (2) Grid'5000, a large-scale distributed testbed that spans France. We evaluate the HARNESS cloud-computing platform with two applications: Reverse-Time Migration, a scientific computing application from the geosciences domain, and AdPredictor, a machine learning algorithm used in the Bing search engine.
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Dates and versions

hal-01507344 , version 1 (13-04-2017)

Identifiers

  • HAL Id : hal-01507344 , version 1

Cite

Jose G F Coutinho, Mark Stillwell, Katerina Argyraki, George Ioannidis, Anca Iordache, et al.. The HARNESS Platform: A Hardware-and Network-Enhanced Software System for Cloud Computing. Ivan Mistrik; Rami Bahsoon; Nour Ali; Maritta Heisel; Bruce Maxim. Software Architecture for Big Data and the Cloud, Morgan Kaufmann, 2017, 9780128054673. ⟨hal-01507344⟩
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