Live Prefetching for Mobile Computation Offloading
Seung-Woo Ko, Kaibin Huang, Seong-Lyun Kim, Hyukjin Chae

TL;DR
This paper introduces live prefetching, a real-time prediction-based technique for mobile computation offloading that reduces energy consumption and network load compared to traditional offline prefetching.
Contribution
It proposes a novel live prefetching method integrating task prediction with cloud computing, optimizing data fetching policies for energy efficiency under various channel conditions.
Findings
Optimal threshold-based prefetching policy for slow fading channels.
Close-to-optimal policies for fast fading channels.
Theoretically proven energy savings over non-predictive fetching.
Abstract
The conventional designs of mobile computation offloading fetch user-specific data to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching is proposed in this paper that seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Age of Information Optimization · Green IT and Sustainability
