DAMCREM: Dynamic Allocation Method of Computation REsource to Macro-Tasks for Fully Homomorphic Encryption Applications

Takuya Suzuki, Yu Ishimaki, H. Yamana
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引用次数: 1

Abstract

Smart computing aims to improve the quality of life by utilizing Internet-of-Things devices and cloud computing. Typically, this computing handles private and/or personal information so concealing such sensitive information is a challenge. Adopting fully homomorphic encryption (FHE) is one approach for handling such sensitive information safely; that is, we can calculate the encrypted data without decryption. However, the time and space complexity of the FHE operation is high. Thus, its computation takes a long time. In this study, we aim to shorten FHE execution time by adopting our new scheduling algorithm, which divides a task into several macro-tasks and then assigns a set of threads. We assume a cloud computing system that is equipped with a many-core CPU. Thus, we propose the dynamic allocation method of computation resource to macro-tasks (DAMCREM), which dynamically allocates a certain number of threads (selected from pre-defined candidates) to each macro-task of every given job. In the evaluation, we compared DAMCREM to naive methods that allocate a pre-defined number of threads to each macro-task. The result shows that the average latency and maximum latency of job execution is less than those of naive methods, even when the average interval of job arrival is short.
全同态加密应用中计算资源对宏任务的动态分配方法
智能计算旨在通过利用物联网设备和云计算来提高生活质量。通常,这种计算处理私有和/或个人信息,因此隐藏此类敏感信息是一项挑战。采用完全同态加密(FHE)是安全处理此类敏感信息的一种方法;也就是说,我们可以在不解密的情况下计算加密的数据。但是,FHE操作的时间和空间复杂度较高。因此,其计算时间较长。在本研究中,我们采用新的调度算法,将一个任务划分为几个宏任务,然后分配一组线程,以缩短FHE的执行时间。我们假设有一个配备了多核CPU的云计算系统。因此,我们提出了计算资源到宏任务的动态分配方法(DAMCREM),该方法从预定义的候选线程中选择一定数量的线程动态分配给给定作业的每个宏任务。在评估中,我们将DAMCREM与为每个宏任务分配预定义数量的线程的朴素方法进行了比较。结果表明,在作业到达的平均间隔较短的情况下,作业执行的平均延迟和最大延迟均小于朴素方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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