1. Address Length IPv4: Uses 32-bit addresses, so we have around 4.3 billion unique addresses. IPv6: Uses 128-bit addresses, so it can provide a huge number of addresses—about 340 undecillion. 2. Address Format IPv4: Addresses are written with four numbers separated by dots. IPv6: Addresses are writRead more
1. Address Length
- IPv4: Uses 32-bit addresses, so we have around 4.3 billion unique addresses.
- IPv6: Uses 128-bit addresses, so it can provide a huge number of addresses—about 340 undecillion.
2. Address Format
- IPv4: Addresses are written with four numbers separated by dots.
- IPv6: Addresses are written with eight groups of four letters and numbers separated by colons.
3. Header Complexity
- IPv4: The header has many parts, which can make it a bit complicated.
- IPv6: The header is simpler and faster to process.
4. Address Configuration
- IPv4: You can set addresses manually or use DHCP to do it automatically.
- IPv6: Can automatically assign addresses by itself and also uses DHCPv6.
5. NAT (Network Address Translation)
- IPv4: Often uses NAT because there aren’t enough addresses for every device.
- IPv6: Doesn’t need NAT because it has plenty of addresses for all devices.
6. Security
- IPv4: Security features are optional.
- IPv6: Has built-in security features, making it more secure.
7. Broadcasting
- IPv4: Can send data to all devices on a network at once.
- IPv6: Doesn’t use broadcasting but has other methods to send data.
8. Fragmentation
- IPv4: Both the sender and routers can break down large data packets.
- IPv6: Only the sender breaks down data packets; routers don’t.
So, IPv6 is like an upgraded version of IPv4, with more addresses, simpler setup, and better security.
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Machine learning is a technology that enables computers to learn from data and make decisions or predictions without being explicitly programmed for each task. Here’s a quick look at its different types: 1. Supervised Learning Definition: The model is trained on a dataset where each example is labelRead more
Machine learning is a technology that enables computers to learn from data and make decisions or predictions without being explicitly programmed for each task. Here’s a quick look at its different types:
1. Supervised Learning
2. Unsupervised Learning
3. Semi-Supervised Learning
4. Reinforcement Learning
These types help solve different kinds of problems and make computers smarter.
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