Abstract-Increasingly, mobile devices are becoming the preferred platform for computation for many users. Unfortunately, the resource limitations, in battery life, computation power and storage, restricts the richness of applications that can be run on such devices. To alleviate these concerns, a popular approach that has gained currency in recent years is computation offloading, where a portion of an application is run off-site, leveraging the far greater resources of the cloud. 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 that an application accesses to be distributed among several servers. This paper describes algorithmic approaches for performing fine-grained, multi-site 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 is based on a novel partitioning algorithm, and a novel data representation. We demonstrate that our partitioning algorithm outperforms existing multi-site offloading algorithms, and that our data representation provides for more efficient, fine-grained offloading than prior approaches.
However, as the number of surrounding devices and cloud computing and storage increases, it is more common that an application can be executed on multiple servers REF .
Techniques for Fine-Grained, Multi-site Computation Offloading
2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Computer Science