That means if timeout tasks under the proposed strategy exist, then they must exist in any other scheduling strategy.
In the previous experiments, the frequent timeout occurrences (task failures) with corresponding performance degradation while running the I/O intensive TeraSort workload with large input data are observed in the Hadoop mobile clusters.
Therefore, task timeout parameters need to be configured adequately which need a priori knowledge about application execution time.
Finally, we propose a timeout handling algorithm when some task runs timeout.
So, if we use other scheduling strategies to make j finish on time, then there must be some other tasks that will be timeout.
On the other hand, this study identifies distinct problems in conducting Hadoop distributed analytics on the mobile clusters, which come in the form of longer job completion time or frequent task failure from task response timeout and node disconnection.
Using an operant delay-of-reward task (20 min daily sessions; timeout 25 sec), mice had a choice between either an immediate small amount of food (SS), or a larger amount of food after a delay (LL), which increased progressively across sessions (from 0 to 150 sec).
Meanwhile, we encounter a problem in plotting the same measurements from the TeraSort workload because the performance is extremely variable and unreliable due to an increasing number of task failures (from task response timeouts and intermittent node disconnections) and re-runs.
Subjects received a timeout message if they failed to complete the task within 400 ms. The entire session took approximately 40 minutes to complete.
While this process is undertaken, the master node will monitor the data that is returned to it and, once it has recieved all the replies (or a timeout is exceeded) it will remove the query tasks from the communication space.
