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IB DP Computer Science Study Notes

C.3.1 Introduction to Distributed Computing

Distributed computing is a model where computational processes are spread across multiple machines, which communicate and coordinate their actions by passing messages to one another. This section delves into the key areas of mobile, ubiquitous, peer-to-peer, and grid computing, explaining their unique attributes and roles within distributed systems.

Mobile Computing

Mobile computing represents the convergence of wireless communication technologies and portable computing devices that facilitate connectivity on the move.

Definitions and Fundamental Principles

  • Mobile Communication: The exchange of information across a wireless medium that does not depend on a fixed physical link.
  • Mobile Hardware: Handheld devices like smartphones, tablets, and wearable gadgets that provide computing capabilities.
  • Mobile Software: Applications and operating systems specifically designed for mobile devices, often focusing on touch-based input and wireless connectivity.

Major Features and Applications

  • Portability and Accessibility: Devices can be used in various environments, providing constant access to information and resources.
  • Context-Awareness: Mobile devices can adjust their operations based on location, orientation, or user activity.
  • Diverse Applications: Ranging from personal productivity, entertainment, to business and healthcare services.

Role in Distributed Systems

  • Data Synchronisation: Keeping data consistent across the distributed network and mobile devices.
  • Resource Sharing: Offering services and information among multiple users and devices.

Ubiquitous Computing

Ubiquitous computing, also known as pervasive computing, integrates computation into the environment, enabling people to interact with information-processing devices more naturally and casually than they do with traditional desktop computers.

Definitions and Fundamental Principles

  • Integration into Daily Life: Devices that are part of everyday objects with the capability to communicate information.
  • Context-Aware Computing: Systems that can sense their environment and adapt their operations accordingly.

Major Features and Applications

  • Smart Environments: Rooms and buildings that can sense their inhabitants and respond to their needs.
  • Invisible Technology: Devices that are not perceived as computers but as part of the environment.
  • Applications: Home automation systems, smart appliances, and intelligent transportation systems.

Role in Distributed Systems

  • Enhanced User Experience: Providing a seamless experience as users engage with multiple interconnected devices.
  • Resource Efficiency: Utilising the available resources of the environment to optimise performance and energy usage.

Peer-to-Peer Networks

Peer-to-peer networks are decentralised networks where each participant (peer) has equivalent capabilities and responsibilities, differing from traditional client-server models.

Definitions and Fundamental Principles

  • Decentralisation: Each node, or peer, acts as both a client and a server, sharing the role of data provider and consumer.
  • Direct Interconnectivity: Peers connect and communicate directly without the need for central coordination.

Major Features and Applications

  • Redundancy and Reliability: P2P networks are robust against failures because they do not rely on a central point of control.
  • Scalability: Can easily expand by adding more peers without the need for significant infrastructure changes.
  • Applications: File sharing, cryptocurrency networks, and distributed data processing.

Role in Distributed Systems

  • Load Distribution: Sharing the workload among peers to utilise the full potential of the network.
  • Fault Tolerance: Enhancing reliability as the failure of one peer does not incapacitate the network.

Grid Computing

Grid computing is a technology that pools together the computational power of various computers to work on complex tasks that can't be handled by a single machine.

Definitions and Fundamental Principles

  • Resource Sharing: Leveraging the collective power of a network of computers to work on a single problem.
  • Collaborative Processing: Multiple systems working in tandem to process large datasets or complex problems.

Major Features and Applications

  • High Throughput: Ability to process a significant number of tasks over a given period.
  • Complex Problem Solving: Tackling scientific, analytical, or data-intensive challenges that require high computational power.
  • Applications: Climate modelling, pharmaceutical research, and astronomical data analysis.

Role in Distributed Systems

  • Distributed Problem Solving: Enabling the tackling of problems that are beyond the scope of individual machines.
  • Resource Optimisation: Efficiently utilising disparate resources for optimal computational performance.

Interoperability and Open Standards

Interoperability in distributed computing is the ability of different systems, platforms, and applications to work together (inter-operate), while open standards are freely available and implementable specifications ensuring such interoperability.

Importance of Interoperability

  • Seamless Communication: Ensuring different systems can exchange and process information without interruption.
  • Operational Flexibility: Providing users with a broad choice of products and services that work well together.

Open Standards

  • Ensuring Accessibility: Open standards help prevent vendor lock-in, allowing for greater user autonomy in terms of hardware and software choices.
  • Promoting Innovation: By providing a common platform for developers, open standards encourage diverse contributions and innovation within the technology ecosystem.

Significance in Distributed Systems

  • Universal Access: Open standards and interoperability together enable different types of devices and systems to connect and communicate, which is the backbone of distributed computing.
  • Future-proofing Technology: Open standards ensure that systems can evolve and adapt over time, maintaining compatibility with new technologies and legacy systems alike.

In conclusion, understanding the intricacies of distributed computing is essential for IB Computer Science students. This exploration of mobile, ubiquitous, peer-to-peer, and grid computing, along with interoperability and open standards, provides a comprehensive look at the building blocks of modern distributed systems.


Grid computing significantly contributes to scientific research by providing vast amounts of computational power necessary to process complex data and run simulations that individual computers cannot handle. This is especially crucial in fields like climate modelling, physics, and bioinformatics. Notable examples include the Large Hadron Collider's data analysis, the [email protected] project which analyses radio signals for signs of extraterrestrial intelligence, and the [email protected] project which models protein folding. These projects rely on grid computing to parse through petabytes of data and perform quadrillions of calculations, thereby accelerating scientific discovery and innovation.

Distributed computing is a broad concept that covers any computing structure where processing is spread across multiple nodes, which may be geographically dispersed. In contrast, cloud computing is a model of distributed computing that provides on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Cloud computing services are typically delivered over the internet and offer scalable and flexible resource allocation, whereas distributed computing may not necessarily be tied to such service models and can exist without the need for an internet connection.

Mobile computing plays a pivotal role in the development of smart cities by providing the infrastructure necessary for the collection, transmission, and processing of data in real-time. With the widespread use of mobile devices, urban planners can gather data about traffic patterns, public transport usage, and community services engagement. This data can then inform decisions on infrastructure development, traffic management, and public services provision, making urban environments more efficient, sustainable, and responsive to the needs of their inhabitants. Mobile computing technologies are the backbone of smart city applications such as intelligent transportation systems, smart grids, and urban informatics.

Peer-to-peer (P2P) networks face several security challenges, notably the risk of distributing malicious software, vulnerability to data breaches, and the difficulty in ensuring the trustworthiness of peer nodes. Without centralised control, it can be hard to enforce security measures. To mitigate these risks, P2P networks can implement robust encryption for data transmission, use decentralised trust models like blockchain to verify the integrity of data, and employ reputation systems to help identify trustworthy nodes. Additionally, regular auditing and updating of the network protocols can help safeguard against new vulnerabilities.

Ubiquitous computing and the Internet of Things (IoT) are closely related concepts in the field of distributed computing. Ubiquitous computing refers to a computing paradigm where computation is embedded in every aspect of daily life. It is characterised by the presence of numerous, casually accessible, and often invisible computing devices. IoT is an extension of this idea, where physical devices are connected to the internet, allowing them to send and receive data. Essentially, IoT can be seen as a practical implementation of ubiquitous computing, where the 'things' in IoT are the devices that blend into the background of our environment, providing seamless computing services.

Practice Questions

Explain the concept of interoperability within distributed systems and why it is significant for the seamless integration of different computing platforms.

Interoperability refers to the ability of different computing systems and software applications to communicate, exchange data, and use the information that has been exchanged effectively. It is significant in distributed systems for several reasons. Firstly, it ensures that various applications and services can work together seamlessly, which is essential for the integrated and efficient functioning of distributed networks. Secondly, interoperability facilitates the sharing of resources, allowing different systems to leverage each other's strengths. Finally, it enhances the user experience by providing a smooth and consistent interface across various platforms and devices, which is crucial in a world where the diversity of devices and applications is continually growing.

Compare and contrast mobile computing and grid computing, focusing on their scalability and resource utilisation.

Mobile computing and grid computing are distinct in their approach to scalability and resource utilisation. Mobile computing achieves scalability primarily through the widespread distribution and individual connectivity of devices. Its resource utilisation is often limited by the individual capabilities of each mobile device, which are generally less powerful than traditional servers. On the other hand, grid computing attains scalability by pooling the computational resources of many dispersed computers to work on a single task. This collaborative approach allows for the tackling of complex problems that would be impossible for a single device, effectively utilising the collective resources for high-performance tasks. Grid computing thus excels in scalability and resource utilisation for intensive computational tasks compared to mobile computing.

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Written by: Alfie
Cambridge University - BA Maths

A Cambridge alumnus, Alfie is a qualified teacher, and specialises creating educational materials for Computer Science for high school students.

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