کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4950548 1440647 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Uploading multiply deferrable big data to the cloud platform using cost-effective online algorithms
ترجمه فارسی عنوان
آپلود داده های بزرگ بازپرداخت بزرگ را به پلت فرم ابر با استفاده از الگوریتم های آنلاین مقرون به صرفه ارسال کنید
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
Cloud computing consists of processing big data and provides convenient, on-demand network access to a shared pool of configurable computing resources. Cloud data center costs have become a hot topic in recent years. To minimize bandwidth costs, a better solution for uploading multiply deferrable big data to a cloud computing platform for processing using a MapReduce framework was studied. The multiply deferrable big data, which have its own delay window sizes, are produced by local cloud users, and the bandwidth charging model in this paper is the Max contract pricing scheme adopted by Internet service providers (ISPs). A basic single-ISP case was analyzed. We then extended the study to the cloud scene. The Multi-Heuristic Smoothing Algorithm for the single case was designed, and we proved that the worst-case competitive ratio of the Multi-Heuristic Smoothing Algorithm falls between 2(1−(1−1/Dmax)Dmax) and 2, where Dmax is the maximum delay window size. In addition, the Multi-Dynamic Self-Adaption Algorithm (MDSA) was designed to optimize the cloud scene based on the Multi-Heuristic Smoothing Algorithm. The simulation experiments demonstrated that the total cost was reduced by 12% when the Multi-Dynamic Self-Adaption Algorithm was adopted.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 67, February 2017, Pages 276-285
نویسندگان
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