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Adapting MapReduce for realtime apps


As much as MapReduce is popular, so much is the discussion to make it even better from a generalized approach to higher performance oriented approach. We will be discussing a few frameworks which have tried to adapt MapReduce further for higher performance orientation.

The first post in this series tries will discuss AMREF, an Adaptive MapReduce Framework designed for real time data intensive applications. (published in the paper Fan Zhang, Junwei Cao, Xiaolong Song, Hong Cai, Cheng Wu: AMREF: An Adaptive MapReduce Framework forReal Time Applications. GCC 2010: 157-162.)


It is always a tricky question on how many splitters, mappers and reducers must be there for an optimal configuration. Faced with the same challenge, the authors felt it is normally difficult to optimally predefine the number in order to maximize the operation performance. The perennial dilemma according to them is how to balance between full utilization of the nodes and the waiting period for an incoming event.


Splitter, as per the authors, should take the additional responsibility to see which mapper is faster or slower and accordingly the files need to be allocated to each mapper. For a faster mapper, the files will be relatively more than other mappers.

As per the design proposed by authors, the ‘Adaptive splitter’ would in stage 1 distribute
the input file evenly to the mappers. Next in stage 2, different mappers with different processing capacity would have different length of input files. Thereafter, in stage 3, a new input file is distributed to the three mappers according to their processing capacity.



In the mapping stage, ‘Adaptive mapper’ design increases or decreases the mappers based on the run time application. An adaptive mapper is added dynamically if it is observed that there is an overburden on the other mappers, or an unbalanced workload between mappers and reducers. Similarly the design proposed to decrease adaptively a mapper when the utilization is less.

For the ‘Adaptive Reducers’, when the output of mappers are too fast for the number of reducers, an adaptive reducer is added in parallel to produces output. Another variant could use a sequential reducer as an adaptive addition where the input to the reducer would be the output of the earlier reducers.


The authors used feedback control and stochastic control in their experiments with this design approach. In a positive feedback loop, if the utilization of the 95% servers or above in splitting stage surpassed 90%, another splitting server node was added to optimize the workload. Similarly, if the utilization of the 95% servers or above in splitting stage was lower than 20%, they adaptively decreased one splitting node. Similar rules were applied to map and reduce stage.


Another interesting technique which was employed included Stochastic control. In this technique, they relied on prediction based on the incoming data, including the incoming time, the amounts, and traffic spikes to adjust the network for moderating the mutation of incoming data

As reported in their conclusion, they found Kalman filter prediction to be much more effective than Smooth filter prediction. Kalman filter named after Rudolf (Rudy) E. Kálmán has "common application for guidance, navigation and control of vehicles, particularly aircraft and spacecraft. Furthermore, the Kalman filter is a widely applied concept in time series".  We will be covering about Kalman filter in out subsequent posts because of the huge interest and discussion that it has been generating in the circles of late.

Overall, the Adaptive MapReduce approach presented by the authors offers interesting options to the application designer. As claimed, it could have an impact in real time applications though the real test would come in the commercial implementations subjected to huge data sets on real time.

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