Beyond Map-Reduce: LATNODE - A New Programming Paradigm for Big Data Systems

被引：0
|
作者：
Sheng, Chai Yit [1 ]
Keong, Phang Keat [1 ]
机构：
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia
来源：
INFORMATION SCIENCE AND APPLICATIONS 2017, ICISA 2017 | 2017年 / 424卷
关键词：
Big data; Map-Reduce; Trees; Lambda calculus; Functional programming; Programming paradigm;
D O I：
10.1007/978-981-10-4154-9_9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Compute Aggregate model used to model Map Reduce does not allow for dynamic node reordering once a job has started, assumes homogenous nodes and a balanced tree layout. We introduce heterogeneous nodes into the tree structure, thereby causing unbalanced trees. Finally, we present a new programming abstraction to allow for dynamic tree balancing.
引用
收藏
页码：69 / 75
页数：7
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