Techniques for fine-grained, multi-site computation offloading

Kanad Sinha, Purdue University

Abstract

Mobile devices are increasingly becoming the preferred platform of computation for many users. Unfortunately, its resource limitations in terms of battery life, computation power and storage, restricts the richness of applications that can be run on such devices. Another trend becoming increasingly popular today is that of cloud computing, which allows access to a practically limitless pool of resources on demand. Notably, it is increasingly common that the data users desire to access and manipulate already lies in the cloud. The common approach to solving the problem of limited resources on mobile devices that has gained currency in recent years is computation offloading, where a portion of an applications is run off-site, leveraging the far greater resources of the cloud. Most prior work in this area has focused on a constrained form of the problem: a single mobile device offloading computation to a single server. However, with the increased popularity of cloud computing and storage, it is more common for the data accessed by an application to be distributed among several servers. This work describes approaches for performing fine-grained, multi-site computation offloading. This allows portions of an application to be offloaded in a data-centric manner, even if that data exists at multiple sites. Our approach, based on a novel partitioning algorithm and a program representation, is shown to outperform other partitioning algorithms and allow more efficient, fine-grained offloading than prior approaches.

Degree

M.S.E.C.E.

Advisors

Kulkarni, Purdue University.

Subject Area

Computer Engineering

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