کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
425536 685775 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Key based data analytics across data centers considering bi-level resource provision in cloud computing
ترجمه فارسی عنوان
تجزیه و تحلیل داده های کلیدی در سراسر مراکز داده با توجه به ارائه دو سطح منابع در محاسبات ابر
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی


• A key based mechanism is proposed to place reducer for data analysis across data centers.
• The problem is formalized as a bi-level programming.
• A unified genetic algorithm is proposed to facilitate the placement.
• The algorithm can outperform the baseline and state-of-the-art algorithms by 49% and 40%, respectively.

Due to the distribution characteristic of the data source, such as astronomy and sales, or the legal prohibition, it is not always practical to store the world-wide data in only one data center (DC). Hadoop is a commonly accepted framework for big data analytics. But it can only deal with data within one DC. The distribution of data necessitates the study of Hadoop across DCs. In this situation, though, we can place mappers in the local DCs, where to place reducers is a great challenge, since each reducer needs to process almost all map output across all involved DCs. In this paper, a novel architecture and a key based scheme are proposed which can respect the locality principle of traditional Hadoop as much as possible while realizing deployment of reducers with lower costs. Considering both the DC level and the server level resource provision, bi-level programming is used to formalize the problem and it is solved by a tailored two level group genetic algorithm (TLGGA). The final results, which may be dispersed in several DCs, can be aggregated to a designative DC or the DC with the minimum transfer and storage cost. Extensive simulations demonstrate the effectiveness of TLGGA. It can outperform both the baseline and the state-of-the-art mechanisms by 49% and 40%, respectively.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 62, September 2016, Pages 40–50
نویسندگان
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