Nowadays, many enterprises provide cloud services based on their own Hadoop clusters. Because the resources of a Hadoop cluster are limited, the Hadoop cluster must select some specific tasks to allocate limited resources in order to get the maximal profit. In this paper, we study the maximal profit problem for a given candidate task set. We describe the candidate task set with a valid sequence and propose a sequence-based scheduling strategy. In order to improve the efficiency of finding a valid sequence, we design some pruning strategies and give the corresponding scheduling algorithm. Finally, we propose a timeout handling algorithm when some task runs timeout. Experiments show that the total profit of the proposed algorithm is very close to the ideal maxima and is obviously bigger than related scheduling algorithms under different experimental settings.
This paper proposed a sliding window based correlation model which could conveniently and efficiently correlate events between different security systems. This model works in a best-effort way to find all relationship between events and enhance event's reliability in the time window. When the correlation is ending, the original event is either eliminated from event sequence or gotten an increased reliability.
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