Title

A2Cloud: Practical Application-to-Cloud Matching To Empower Scientific Computing on a Budget

Poster Number

19B

Lead Author Major

Computer Engineering

Lead Author Status

Senior

Second Author Major

Computer Engineering

Second Author Status

Senior

Format

Poster Presentation

Faculty Mentor Name

Vivek K. Pallipuram

Faculty Mentor Email

vpallipuramkrishnamani@pacific.edu

Faculty Mentor Department

Electrical and Computer Engineering

Additional Faculty Mentor Name

David Mueller

Additional Faculty Mentor Email

dmueller@pacific.edu

Additional Faculty Mentor Department

Electrical and Computer Engineering

Additional Faculty Mentor Name

Chadi El Kari

Additional Faculty Mentor Email

celkari@pacific.edu

Additional Faculty Mentor Department

Computer Science

Graduate Student Mentor Name

David De La Vega

Graduate Student Mentor Email

d_delavega@u.pacific.edu

Graduate Student Mentor Department

Electrical and Computer Engineering

Abstract/Artist Statement

Primarily undergraduate universities and small businesses have long been at a disadvantage when it comes to scientific computing resources. On-premise computing clusters have a high barrier to entry and are often not justifiable for these users. Hours on supercomputing resources of big research labs are hard to come by and maintain. Modern cloud computing provides an attainable alternative, however, due to the large quantity of cloud solutions it is a challenge to select the most effective one. For that reason, we present a model that matches scientific applications to cloud instances for high application performance. Our model constructs two vectors: the application vector, which characterizes a program, and the probabilistic cloud vector which characterizes a cloud instance. The application vector components are application-specific constants such as the number of floating-point operations, memory usage, and disk usage. The cloud vector components are independent random variables that correspond to the application vector components such as floating-point operations per second, memory bandwidth, and disk bandwidth. The model then performs an inner-product of the two vectors to produce an Application-to-Cloud (A2Cloud) score, which quantifies the Application-to-Cloud match. We encapsulate the A2Cloud model in a user-friendly A2Cloud framework that inputs a test application and outputs cloud instance recommendations. We demonstrate the model and framework by conducting 162 application executions across nice cloud instances. Our tests yield an average A2Cloud matching rate of 6 for every 9 application-instance pairs with a mean absolute difference of +/- 1.08 ranks.

Location

DeRosa University Center, Ballroom

Start Date

28-4-2018 1:00 PM

End Date

28-4-2018 3:00 PM

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Apr 28th, 1:00 PM Apr 28th, 3:00 PM

A2Cloud: Practical Application-to-Cloud Matching To Empower Scientific Computing on a Budget

DeRosa University Center, Ballroom

Primarily undergraduate universities and small businesses have long been at a disadvantage when it comes to scientific computing resources. On-premise computing clusters have a high barrier to entry and are often not justifiable for these users. Hours on supercomputing resources of big research labs are hard to come by and maintain. Modern cloud computing provides an attainable alternative, however, due to the large quantity of cloud solutions it is a challenge to select the most effective one. For that reason, we present a model that matches scientific applications to cloud instances for high application performance. Our model constructs two vectors: the application vector, which characterizes a program, and the probabilistic cloud vector which characterizes a cloud instance. The application vector components are application-specific constants such as the number of floating-point operations, memory usage, and disk usage. The cloud vector components are independent random variables that correspond to the application vector components such as floating-point operations per second, memory bandwidth, and disk bandwidth. The model then performs an inner-product of the two vectors to produce an Application-to-Cloud (A2Cloud) score, which quantifies the Application-to-Cloud match. We encapsulate the A2Cloud model in a user-friendly A2Cloud framework that inputs a test application and outputs cloud instance recommendations. We demonstrate the model and framework by conducting 162 application executions across nice cloud instances. Our tests yield an average A2Cloud matching rate of 6 for every 9 application-instance pairs with a mean absolute difference of +/- 1.08 ranks.