1 / 7

Cloud Based Framework for Rich Mobile Application

Cloud Based Framework for Rich Mobile Application. Roberto Fonseca, Andrew Williams and Krishna Sharma Project Champion: Reza Rahimi. Mobile Offloading/Cyber Foraging.

gita
Download Presentation

Cloud Based Framework for Rich Mobile Application

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Cloud Based Framework for Rich Mobile Application Roberto Fonseca, Andrew Williams and Krishna Sharma Project Champion: Reza Rahimi

  2. Mobile Offloading/Cyber Foraging • Although the computing ability of mobile devices has greatly improved over the last few years it is still not enough to satisfy the current demand needs • Offloading resource intense components of an application to a server. • The server calculates and returns the result to the client. • Computation time is saved by the client, however transmission time needs to be taken into account before time or power savings may be realized.

  3. Our Solution using Open Cloud Computing • Used open source project "Java OCR" • Created 3 main components of Java OCR          -- Convert to Gray-scale          -- Filter image          -- Scan image • Components are distributed between client and server  • Used a cloud server (Microsoft Azure) • Used RESTful webservices on the cloud • Decision component done manually to check timing of different configurations

  4. End to end testing involves two parts: 1. (Laptop/Android phone) to Local Server 2. (Laptop/Android phone) to Microsoft Azure Cloud Testing Setup

  5. Consisted of a combination  of parameters Testing the OCR process on a small paragraph and multiple paragraphs Different Resolutions 200, 300, 600 DPI Small file sizes and large file sizes Measured the RTT, Filter Time, Grayscale Time and Scan time and estimated Peak Energy usages on client device. Testing Setup

  6. Findings • Testing performed on a 1.4 ghz cpu laptop to simulate a generic mobile device running Java. • Not much different between client and cloud server total run times except on larger files that require more computation power. • Split execution between device and Cloud server resulted in the slowest time.  This is because for this particular application the intermediate steps of image processing require sending very large files • Execution using a local server for offloading large files resulted in times that were 2-3 times faster than executing on the client only. • Offloading provides good results in specific cases • Processing large files on Cloud Only or Local Server Only

More Related