手持式裝置上考慮耗時與耗電的運算量卸載至協同處理器與雲端處理

Time-and-Energy Aware Computation Offloading in Handheld Devices to Coprocessors and Clouds

黃霆鈞(Ting-Jun Huang)

指導教授 ： 林盈達

國立交通大學/資訊學院/網路工程研究所/碩士(2011年)

https://doi.org/10.6842/NCTU.2011.00240

摘要

基於省電與成本考量，手持裝置上的硬體元件通常只適合特定的應用，無法做為一般性的運算使用。但隨著智慧型手持裝置(如手機、平板)的普及，這些裝置上所運行的軟體愈趨多元，導致裝置上的耗時與耗電大幅提高，帶動了手持裝置上運算量卸載的研究，而運算量的卸載並非永遠可以省時與省電。我們實作了一個以環境因子為基礎的卸載決策架構，它以收集而來的環境因子預測在每種運作環境下的耗時與耗電，再依使用者所傾向的使用模式做出決策。我們選定兩種評測程式做為卸載決策成果的評估，分別為矩陣相乘與病毒掃描。在矩陣相乘的運算中，此方法決策錯誤率低於30%，而且成功的讓裝置在運算過程中節省約20~300%的運算時間，與50~130%的電量消耗。在病毒掃描的測試中，採用的是clamAV的掃毒程式，此方法決策錯誤率趨近於零。令人意外的是，在測試中發現當掃描較大容量的檔案時(例：大於2MB)，將檔案傳送到雲端進行掃描的方案完全不適用，反而會導致160~250%左右的效能損耗。

關鍵字

運算量卸載 ； 協同處理器 ； 雲端運算 ； Android

並列摘要

The hardware components in handheld devices are usually adapted to specific application and are hard to do general computations. However, with the popular of smart handheld devices, e.g. smart phone and pads, more kinds of software can run on these devices and raising the time and energy consumption. Therefore, some research discussed with computation offloading, but offloading does not promise the time and energy saving can always be achieved. Thus, we implement an offloading decision framework based on environment factors. The framework collects factors for estimating time and energy usage in every computing environment, and then makes decision according to user's preference. We pick two program, matrix multiplication and virus scanning, to evaluate the decision framework. In matrix multiplication computation, the false decision rate is below 30%, and can save 20~300% computing time and 50~130% energy consumption. In virus scanning, we choose the scanner from clamAV, the false decision rate is nearly zero. Surprisingly, we find a large file, e.g. larger than 2MB, is not suitable for offloading to cloud for scanning, which will suffered 160~250% performance decrease.

並列關鍵字

Computation Offloading ； Coprocessors ； Cloud Computing ； Android

參考文獻

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