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First of all, the data sizes between waves and particles are largely different.
For instance, Fig. 3 presents some of the data sizes of various M2M uses cases.
And in Fig. 2, when the node's redundancy level is growing, the data sizes and energy consumption are also increased.
The data sizes are denoted as v i and v i ′, respectively, in which v i ′ > v i.
This storage and distribution step requires significant time and resources and may not even be possible in an online/distance learning scenario due to the data sizes involved.
The data sizes and access patterns of today's large I/O-intensive workloads require architectures whose processing power scales with increased storage capacity.
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Data transfer times are accounted for with a model based on the data size and the cloud data center internal bandwidth (assumed to be fixed for all nodes).
This operation pattern reduces the data size.
where N is the data size.
Volume The data size characterizes the volume of big data.
D represents the data size of each data link.
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