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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2017
عنوان انگلیسی مقاله:
Towards Max-Min Fair Resource Allocation for Stream Big Data Analytics in Shared Clouds
ترجمه فارسی عنوان مقاله:
به سمت تخصیص منابع MAX-MIN برای تحلیل جریان داده های بزرگ در ابرهای به اشتراک گذاشته شده
منبع:
IEEE - This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TBDATA.2016.2638860, IEEE Transactions on Big Data
نویسنده:
Yuxuan Jiang ,Zhe Huang, Danny H.K. Tsang,
چکیده انگلیسی:
Distributed stream big data analytics platforms have emerged to tackle the continuously generated data streams. In stream
big data analytics, the data processing workflow is abstracted as a directed graph referred to as a topology. Data are read from the
storage and processed tuple by tuple, and these processing results are updated dynamically. The performance of a topology is
evaluated by its throughput. This paper proposes an efficient resource allocation scheme for a heterogeneous shared stream big data
analytics cluster shared by multiple topologies, in order to achieve max-min fairness in the utilities of the throughput for all the
topologies. We first formulate a novel model resource allocation problem, which is a mixed 0-1 integer program. The NP-hardness of
the problem is rigorously proven. To tackle this problem, we transform the non-convex constraint to several linear constraints using
linearization and reformulation techniques. Based on the analysis of the problem-specific structure and characteristics, we propose an
approach that iteratively solves the continuous problem with a fixed set of discrete variables optimally, and updates the discrete
variables heuristically. Simulations show that our proposed resource allocation scheme remarkably improves the max-min fairness in
utilities of the topology throughput, and is low in computational complexity.
Index Terms: Stream big data analytics | optimization
قیمت: رایگان
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