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Understanding Computing Devices, Systems, and Networks
Computing Devices: A computing device is a physical artifact capable of running a program. Examples of these devices include computers, tablets, servers, routers, and smart sensors. These devices are designed to receive inputs, process these inputs, and produce outputs based on the processed data.
Computing Systems: A computing system refers to a collection of computing devices and programs that work collaboratively to achieve a specific objective. This collaboration enhances the capabilities beyond what a single device could achieve independently.
Computer Networks: A computer network is an ensemble of interconnected computing devices that can send and receive data. Networks allow multiple devices to communicate and share resources efficiently. The bandwidth of a network refers to the maximum rate at which data can be transferred across the network in a given period of time.
Networking Concepts
Path and Routing:
Path: In networking, a path is defined as a sequence of directly connected devices that begins with a sender and ends with a receiver.
Routing: This is the process of finding a viable path from a sender to a receiver within a network. If a disruption occurs along the path, routing algorithms find an alternative path, showcasing the fault-tolerant nature of internet connections.
Internet Infrastructure
Components and Connectivity:
The internet is a vast physical network composed of fiber optic cables, radio transmitters, and various forms of cabling. This infrastructure supports the global connectivity of devices and networks.
Protocols and Internet Functionality:
Protocol: A set of agreed-upon rules that dictate how data is transmitted across a network. Protocols ensure reliable and standardized communication between devices.
Internet Protocol (IP): IP is crucial for addressing and routing. It assigns unique addresses (IP addresses) to each device on the internet, facilitating precise data routing.
Scalability and Evolution of Internet Protocols:
Scalability: This refers to the ability of a network or system to expand and adapt to increased demands. The internet's design allows it to scale up efficiently, accommodating more devices and data traffic without losing performance.
Evolution of Protocols: Internet protocols have evolved to support the growing scale and complexity of global networks. This evolution is necessary to handle the increasing number of devices and the vast amount of data transmitted over the internet.
Transition from IPv4 to IPv6 and Networking Protocols
As technology advances and the number of devices connected to the internet increases, the protocols and infrastructure that support network communication must evolve. Here's an overview of the transition from IPv4 to IPv6, the roles of TCP and UDP, and the concepts of fault tolerance and redundancy in network systems.
IPv4 vs. IPv6
IPv4 (Internet Protocol version 4):
Address Length: Uses 32 bits for IP addresses, allowing for approximately 4.29 billion unique addresses (2^32).
Limitation: The explosive growth of the internet and connected devices has nearly exhausted the address capacity of IPv4, leading to the need for a more robust system.
IPv6 (Internet Protocol version 6):
Address Length: Utilizes 128 bits, which significantly increases the number of possible IP addresses to about 3.4 x 10^38 (2^128). This expansion addresses the limitations of IPv4 and supports an expansive future growth of internet-connected devices.
Benefits: Besides providing a larger address space, IPv6 includes enhancements for security, routing efficiency, and auto-configuration.
Know for AP Exam: 2^96 times as many addresses can be represented with IPv6.
TCP and UDP Protocols
TCP (Transmission Control Protocol):
Functionality: TCP is a connection-oriented protocol that ensures reliable data transmission between computers on a network. It breaks data into packets, manages the packet sequencing, and retransmits any packets that are lost during transmission.
Use Cases: Ideal for applications where accuracy is more critical than speed, such as web browsing, email, and file transfers.
UDP (User Datagram Protocol):
Functionality: Unlike TCP, UDP is connectionless and does not guarantee the delivery of packets, meaning it does not resend lost packets. This makes it faster but less reliable.
Use Cases: Suitable for applications where speed is more critical than reliability, such as streaming audio/video or gaming, where missing a few packets might not disrupt the user experience.
Network Fault Tolerance and Redundancy
Fault Tolerance:
The internet is designed to be fault tolerant, meaning it can sustain operations even if part of the system fails. For example, if a route between two points on the internet is disrupted, the data will automatically be rerouted through an alternative path.
Redundancy:
Redundancy involves creating multiple pathways and duplicate systems to ensure reliability and availability. In networking, having multiple routes between sender and receiver ensures that the failure of a single path does not interrupt data transmission.
The Internet vs. The World Wide Web
The Internet: Refers to the global network of physical hardware — such as computers, routers, and cables — that allows billions of devices to connect and communicate.
The World Wide Web: Is a service built on top of the internet. It uses the HTTP protocol to transmit data and is made up of interconnected web pages and web applications.
Understanding Sequential vs. Parallel Computing
When examining computational models, it's crucial to distinguish between sequential and parallel computing, as each has different implications for processing speed and efficiency. Here's a breakdown of these concepts along with a method to calculate the "speedup" achieved through parallel processing.
Sequential Computing
Definition:
Sequential computing is a process where operations are performed one after another. Each step must be completed before the next begins, resulting in the total processing time being the sum of all individual steps.
Example Calculation:
Suppose a task involves the following sequential steps with their respective times (in seconds): 4, 1, 6, 2, 3, 1, 5, 1.
Total processing time = 4 + 1 + 6 + 2 + 3 + 1 + 5 + 1 = 23 seconds.
Parallel Computing
Definition:
Parallel computing involves dividing a task into smaller parts that can be executed simultaneously on different processors or across a network of computers. The total time required to complete the task in parallel is determined by the longest sub-task.
Example Calculation of Parallel Processing Time:
If the same task is split where the longest sub-task takes 9 seconds, then the parallel processing time is 9 seconds.
Mixed Model Calculation:
If parallel computing involves some steps that must be executed sequentially alongside parallel tasks, the total time is the sum of all sequential tasks plus the time taken for the longest parallel task.
Example:
Sequential tasks: 2, 1
Parallel tasks: 4, 3, 1
Total parallel processing time = 2 + 1 + max(4, 3, 1) = 3 + 4 = 7 seconds.
Speedup Calculation
Speedup is a measure of how much faster a parallel system is compared to a sequential system for the same task.
Formula: Speedup = Sequential run time / Parallel run time
Example of Speedup Calculation:
Sequential time: 23 seconds
Parallel time: 9 seconds
Speedup = 23/9 = 2.56
This result means the parallel solution is approximately 2.56 times faster than the sequential solution.
Understanding Distributed Computing
Distributed computing is a powerful approach used to tackle large and complex problems that exceed the capabilities of a single computer, particularly in terms of processing power and memory capacity. Let’s explore how distributed computing compares to parallel computing and why it's essential for solving certain types of problems.
Distributed Computing Defined
Distributed Computing:
Concept: In distributed computing, a problem is divided across multiple computing devices (nodes). These nodes can be located in the same physical location or spread across different geographical areas, interconnected via a network.
Advantages:
Scalability: By adding more nodes to the system, distributed computing environments can scale to handle tasks of almost any size and complexity.
Flexibility: Systems can be expanded or upgraded incrementally, which is more cost-effective than scaling up a single supercomputer.
Fault Tolerance: The failure of a single node does not necessarily prevent the entire system from continuing its operation. Redundant data storage and functional backups can keep the system running smoothly despite individual component failures.
Parallel vs. Distributed Computing
While both distributed and parallel computing involve running processes concurrently, they differ mainly in their structure and use cases:
Parallel Computing: Utilizes multiple processor cores within the same computer to perform concurrent processing of tasks. This is effective for tasks that require significant processing power but are limited by the physical and memory constraints of a single machine.
Distributed Computing: Spreads tasks across multiple, often heterogeneous, machines which may not share a physical connection but are linked through a network. This model is suitable for very large data sets and computationally intensive tasks that no single machine could handle efficiently.
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