The efficiency of MapReduce is closely related to its load balance. Existing works on MapReduce load balance focus on coarse-grained scheduling. This study concerns finegrained scheduling on MapReduce operations, with each operation representing one invocation of the Map or Reduce function. By default, MapReduce adopts the hash-based method to schedule Reduce operations, which often leads to po...

We introduce HadoopProv, a modified version of Hadoop that implements provenance capture and analysis in MapReduce jobs. It is designed to minimise provenance capture overheads by (i) treating provenance tracking in Map and Reduce phases separately, and (ii) deferring construction of the provenance graph to the query stage. Provenance graphs are later joined on matching intermediate keys of the...

Increasingly, organizations capture, transform and analyze enormous data sets. Prominent examples include internet companies and e-science. The Map-Reduce scalable dataflow paradigm has become popular for these applications. Its simple, explicit dataflow programming model is favored by some over the traditional high-level declarative approach: SQL. On the other hand, the extreme simplicity of M...

MapReduce is a parallel programming model and an associated implementation introduced by Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. The original MapReduce implementation b...

This paper describes two new matrix transform algorithms for the Max-Log-MAP decoding of turbo codes. In the proposed algorithms, the successive decoding procedures carried out in the conventional Max-Log-MAP algorithm are performed in parallel, and well formulated into a set of simple and regular matrix operations, which can therefore considerably speed up the decoding operations and reduce th...