Answer:
1. Processor communication -- this involves the following tasks:
<em>a. exchange of data between processor and I/O module</em>
<em>b. command decoding - I/O module accepts commands sent from the processor. E.g., the I/O module for a disk drive may accept the following commands from the processor: READ SECTOR, WRITE SECTOR, SEEK track, etc. </em>
<em>c. status reporting – The device must be able to report its status to the processor, e.g., disk drive busy, ready etc. Status reporting may also involve reporting various errors. </em>
<em>d. Address recognition – Each I/O device has a unique address and the I/O module must recognize this address. </em>
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2. Device communication – The I/O module must be able to perform device communication such as status reporting.
3. Control & timing – The I/O module must be able to co-ordinate the flow of data between the internal resources (such as processor, memory) and external devices.
4. Data buffering – This is necessary as there is a speed mismatch between speed of data transfer between processor and memory and external devices. Data coming from the main memory are sent to an I/O module in a rapid burst. The data is buffered in the I/O module and then sent to the peripheral device at its rate.
5. Error detection – The I/O module must also be able to detect errors and report them to the processor. These errors may be mechanical errors (such as paper jam in a printer), or changes in the bit pattern of transmitted data. A common way of detecting such errors is by using parity bits.
Answer:
Due to unreachable DHCP server
Explanation:
As we know that Dynamic Host Configuration Protocol, in short DHCP is the network protocol which is used to central and automatic management of IP address with in the network.
So due to unreachable DHCP server a student could not able to connect with the internet.When the network connection break it means that DHCP server is offline.
So the answer is Due to unreachable DHCP server .
Hexadecimal it describes locations in memory
Answer:
Explanation:
Hadoop clusters can boost the processing speed of many big data analytics jobs, given their ability to break down large computational tasks into smaller tasks that can be run in a parallel, distributed fashion.
