Nowadays, companies are
faced with the task of processing huge quantum of data. As the traditional
database systems cannot handle this task in a cost-efficient manner, companies
have built customized data processing frameworks. Cloud computing has emerged
as a promising approach to rent a large IT infrastructure on a short-term
pay-per-usage basis. This paper attempts to schedule tasks on compute nodes so
that data sent from one node to the other has to traverse as few network
switches as possible. The challenges and opportunities for efficient parallel
data processing in cloud environments have been demonstrated and Nephele, the
first data processing framework, has been presented to exploit the dynamic
resource provisioning offered by the IaaS clouds. The overall utilisation of
resources has been improved by assigning specific virtual machine types to
specific tasks of a processing job and by automatically allocating or
deallocating virtual machines in the course of a job execution. This has led to
substantial reduction in the cost of parallel data processing.
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