Thèse de doctorat: Algorithmes de classification répartis sur le cloud

tel-00744768, version 1 - 23 Oct 2012
36 CHAPTER 2. PRESENTATION OF CLOUD COMPUTING
considerations. More specifically, the users need not dive into general parallel applications
design, neither do they need to have any knowledge about the hardware
actually running the computations: MapReduce indeed provides a scale-agnostic
([64]) interface.
Secondly, the framework helps the users to figure out what the parallel algorithm
is actually running. As stated in [81], “concurrent programs are notoriously
difficult to reason about”. Because of its elementary design, a MapReduce execution
has Leslie Lamport’s sequential consistency property 2 , which means the
MapReduce result is the same as the one that would have been produced by some
reordered sequential execution of the program. This property helps to understand
the MapReduce execution as it can therefore be rethought as a sequential run
speeded-up.
As previously stated, the MapReduce framework also provides a strong resilience
to failures. A reliable commodity hardware usually has a Mean-Time Between
Failures (MTBF) of 3 years. In a typical 10,000 server cluster, this implies that
ten servers are failing every day. Thus, failures in large-scale data centers are a
frequent event and a framework like MapReduce is designed to be resilient to
single point of failures 3 . Such a resilience is hard to achieve since the computing
units are dying silently, i.e. without notifying of their failure. MapReduce holds a
complex monitoring system to ping all the processing units and to ensure all the
tasks will be successfully completed, as detailed in [47].
The initial problems MapReduce has been devised to deal with are related to text
mining. These problems involve gigantic amounts of data that do not fit in the
memory of the processing units dedicated to the computation, as explained in
[48]. In such situations, providing the processing units with a continuous flow
of data without starving these units out would require an aggregated bandwidth
very hard to achieve. MapReduce has provided a new solution to this problem by
co-locating the data storage and the processing system. Instead of pushing the
data chunks to the processing units in charge of them, MapReduce pushes the
tasks to the processing units whose storage holds the data chunks to be processed.
Such a design requires a distributed file system that can locate where the data are
stored, but removes a lot of stress on communication devices.
2. Provided that the map and reduce operators are deterministic functions of their input values.
3. With the noticeable exception of the single orchestration machine referred to as master.