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MapReduce is a popular programming model for executing time-consuming analytical queries as a batch of tasks on large scale data clusters.
The crowd of incentivized users can either parallelize and speedup the completion of a batch of tasks [16], or solve problems beyond the reach of any individual even if given sufficient time to do so [17].
SpeQuloS is concerned with providing quality of service (QoS) for a given batch of tasks through a computational credit exchange system, in which credits accrued from previous DG processing by one institution (the donor) on another institution's behalf (the requester), can be exchanged back for cloud resources at the requester's site when the donor requires them.
Therefore, after the first batch of map tasks finish, all the reduce tasks would be running and fetching remote data across network.
It operates on batches of unmapped tasks and can preemptively remap tasks to processors.
The greedy partitioning scheme could also be beneficial on larger many-core processors, where the master may become a bottleneck, making off-line creation of batches of PSC tasks essential.
The final method is a reformulation that introduces a new integer variable representing the total number of batches of a task.
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