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A Fog Computing architecture consists of edge nodes that generate and possibly pre-process (sensor) data, fog nodes that do some processing quickly and do any actuations that may be needed, and cloud nodes that may perform further detailed analysis for long-term and archival purposes. Processing of a batch of input data is distributed into sub-computations which are executed at the different nodes of the architecture. In many applications, the computations are expected to preserve the order in which the batches arrive at the sources. In this paper, we discuss mechanisms for performing the computations at a node in correct order, by storing some batches temporarily and/or dropping some batches. The former option causes a delay in processing and the latter option affects Quality of Service (QoS). We bring out the trade-offs between processing delay and storage capabilities of the nodes, and also between QoS and the storage capabilities.