This document presents a novel statistical cost model and algorithm for efficiently offloading applications to clouds. The model constructs an execution dependency tree to represent execution relationships among application modules. In contrast to fixed average costs, each module's cost is modeled as a random variable described by its cumulative distribution function statistically estimated through profiling. Using this model, the paper generalizes cost optimization functions and proposes an efficient offloading algorithm based on dynamic programming. Performance results show the model can estimate application execution time with a mean absolute percentage error as low as 5% for applications with sequential and branching dependencies.

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A Novel Statistical Cost Model and an Algorithm for Efficient Application Offloading to
Clouds
Abstract
This work presents a novel statistical cost model for applications that can be offloaded to
cloud computing environments. The model constructs a tree structure, referred to as the
execution dependency tree (EDT), to accurately represent various execution relations, or
dependencies (e.g., sequential, parallel and conditional branching) among the application
modules, along its different execution paths. Contrary to existing models that assume fixed
average offloading costs, each module’s cost is modelled as a random variable described by its
Cumulative Distribution Function (CDF) that is statistically estimated through application
profiling. Using this model, we generalize the offloading cost optimization functions to those
that use more user tailored statistical measures such as cost percentiles. We employ these
functions to propose an efficient offloading algorithm based on a dynamic programming
formulation. We also show that the proposed model can be used as an efficient tool for
application analysis by developers to gain insights on the applications’ statistical performance
under varying network conditions and users behaviours. Performance evaluation results show
that the achieved mean absolute percentage error between the model-based estimated cost and
the measured one for the application execution time can be as small as 5% for applications with
sequential and branching module dependencies.
System Specification
System Requirements:
Hardware Requirements:

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• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
Software Requirements:
• Operating system : - Windows 7. 32 bit
• Coding Language : C#.net 4.0
• Data Base : SQL Server 2008