21,655 research outputs found
Efficient Task Replication for Fast Response Times in Parallel Computation
One typical use case of large-scale distributed computing in data centers is
to decompose a computation job into many independent tasks and run them in
parallel on different machines, sometimes known as the "embarrassingly
parallel" computation. For this type of computation, one challenge is that the
time to execute a task for each machine is inherently variable, and the overall
response time is constrained by the execution time of the slowest machine. To
address this issue, system designers introduce task replication, which sends
the same task to multiple machines, and obtains result from the machine that
finishes first. While task replication reduces response time, it usually
increases resource usage. In this work, we propose a theoretical framework to
analyze the trade-off between response time and resource usage. We show that,
while in general, there is a tension between response time and resource usage,
there exist scenarios where replicating tasks judiciously reduces completion
time and resource usage simultaneously. Given the execution time distribution
for machines, we investigate the conditions for a scheduling policy to achieve
optimal performance trade-off, and propose efficient algorithms to search for
optimal or near-optimal scheduling policies. Our analysis gives insights on
when and why replication helps, which can be used to guide scheduler design in
large-scale distributed computing systems.Comment: Extended version of the 2-page paper accepted to ACM SIGMETRICS 201
Observations on Factors Affecting Performance of MapReduce based Apriori on Hadoop Cluster
Designing fast and scalable algorithm for mining frequent itemsets is always
being a most eminent and promising problem of data mining. Apriori is one of
the most broadly used and popular algorithm of frequent itemset mining.
Designing efficient algorithms on MapReduce framework to process and analyze
big datasets is contemporary research nowadays. In this paper, we have focused
on the performance of MapReduce based Apriori on homogeneous as well as on
heterogeneous Hadoop cluster. We have investigated a number of factors that
significantly affects the execution time of MapReduce based Apriori running on
homogeneous and heterogeneous Hadoop Cluster. Factors are specific to both
algorithmic and non-algorithmic improvements. Considered factors specific to
algorithmic improvements are filtered transactions and data structures.
Experimental results show that how an appropriate data structure and filtered
transactions technique drastically reduce the execution time. The
non-algorithmic factors include speculative execution, nodes with poor
performance, data locality & distribution of data blocks, and parallelism
control with input split size. We have applied strategies against these factors
and fine tuned the relevant parameters in our particular application.
Experimental results show that if cluster specific parameters are taken care of
then there is a significant reduction in execution time. Also we have discussed
the issues regarding MapReduce implementation of Apriori which may
significantly influence the performance.Comment: 8 pages, 8 figures, International Conference on Computing,
Communication and Automation (ICCCA2016
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