Ссылка на решение в MathCAD
Suppose that a certain communications protocol involves a per-packet overhead of 50 bytes for headers and framing.We send 1 million bytes of data using this protocol; however, one data byte is corrupted and the entire packet containing it is thus lost. Give the total number of overhead+loss bytes for packet data sizes of 1000, 10,000, and 20,000 bytes. Which size is optimal?
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)
Оптимальный вариант - размер пакета10000 B
Suppose that a certain communications protocol involves a per-packet overhead of 50 bytes for headers and framing.We send 1 million bytes of data using this protocol; however, one data byte is corrupted and the entire packet containing it is thus lost. Give the total number of overhead+loss bytes for packet data sizes of 1000, 10,000, and 20,000 bytes. Which size is optimal?
Оптимальный вариант - размер пакета10000 B