Openness in Distributed Systems

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Questions and Answers

What is the definition of openness in distributed systems?

Be able to interact with services from other open systems, irrespective of the underlying environment.

Which of the following are considered essential components of openness in distributed systems? (Select all that apply)

  • Systems should support portability of applications (correct)
  • Systems should be easily extensible (correct)
  • Systems should easily interoperate (correct)
  • Systems should conform to well-defined interfaces (correct)

What is the core challenge addressed by implementing policies for openness in distributed systems?

To define the level of consistency, allowed operations, QoS requirements, and secrecy levels for various aspects of the system.

What are two examples of mechanisms that can implement openness in distributed systems?

<p>Allowing dynamic setting of caching policies and supporting different levels of trust for mobile code.</p> Signup and view all the answers

Strict separation between policy and mechanism always leads to simpler management.

<p>False (B)</p> Signup and view all the answers

What is the key trade-off involved in deciding how to implement openness in distributed systems?

<p>The trade-off between simplifying management and reducing complexity versus maintaining flexibility.</p> Signup and view all the answers

Which three components are essential in ensuring the scalability of distributed systems?

<p>Number of users and/or processes (A), Maximum distance between nodes (B), Number of administrative domains (D)</p> Signup and view all the answers

Most systems today are primarily designed to address geographical and administrative scalability challenges.

<p>False (B)</p> Signup and view all the answers

What are three root causes of scalability problems in centralized solutions?

<p>The computational capacity, limited by the CPUs, the storage capacity, including the transfer rate between CPUs and disks, and the network between the user and the centralized service.</p> Signup and view all the answers

How can a simple queuing system be used to model a centralized service?

<p>By representing the service as a process that receives requests, queues them, and then processes them before sending a response.</p> Signup and view all the answers

What is the significance of the assumption that the queue in a simple queuing system has infinite capacity?

<p>This assumption ensures that the arrival rate of requests is not affected by the current queue length or processing status, simplifying analysis.</p> Signup and view all the answers

What is the key factor that limits the scalability of a centralized system?

<p>The utilization of the service, as a high utilization rate can lead to increased response times and potentially a grinding halt.</p> Signup and view all the answers

What is the primary advantage of a peer-to-peer (P2P) system compared to a client-server system in terms of scalability?

<p>P2P systems have a more distributed workload, leading to lower response time and potentially improved performance as the number of nodes increases.</p> Signup and view all the answers

Which of the following challenges contribute to the difficulty of achieving geographical scalability in distributed systems? (Select all that apply)

<p>The inherent unreliability of wide area networks (WANs) (A), The assumption of synchronous client-server interactions (B), The lack of support for multipoint communication (D)</p> Signup and view all the answers

What is the primary challenge associated with achieving administrative scalability in distributed systems?

<p>Managing conflicting policies concerning usage, payment, management, and security across different administrative domains.</p> Signup and view all the answers

Peer-to-peer networks are generally more susceptible to administrative scalability challenges than traditional client-server systems.

<p>False (B)</p> Signup and view all the answers

What is the fundamental principle behind the technique of hiding communication latencies to improve scalability?

<p>To use asynchronous communication with separate handlers for incoming responses, allowing the system to continue processing other tasks while waiting for responses.</p> Signup and view all the answers

The technique of hiding communication latencies can be effectively applied to all distributed systems.

<p>False (B)</p> Signup and view all the answers

How can the strategy of facilitating solution by moving computations to the client improve scalability of distributed systems?

<p>By offloading processing tasks from the server to the client, the server can focus on more demanding operations, potentially handling a larger number of requests.</p> Signup and view all the answers

What are two main techniques for partitioning data and computations across multiple machines to improve scalability?

<p>Move computations to clients using Java applets and decentralized naming services like the Domain Name System (DNS).</p> Signup and view all the answers

Explain the concept of replication and caching in the context of improving the scalability of distributed systems.

<p>Replication involves making copies of data available on different machines, while caching stores frequently accessed data locally to reduce the need for remote access, improving performance and scalability.</p> Signup and view all the answers

What is the primary challenge associated with using replication as a scaling technique?

<p>Maintaining consistency across multiple copies of data, as modifying one copy can lead to inconsistencies.</p> Signup and view all the answers

Tolerating inconsistencies in replicated data is always a viable approach to reduce the need for global synchronization.

<p>False (B)</p> Signup and view all the answers

What key design consideration should be taken into account when deciding whether to tolerate inconsistencies in replicated data?

<p>The specific requirements of the application and its ability to handle potential inconsistencies in data.</p> Signup and view all the answers

Flashcards

Open distributed systems

Systems that can interact with other systems regardless of their environment, following well-defined interfaces and allowing easy interoperability, portability, and extensibility.

Openness (in distributed systems)

The ability of different systems to seamlessly interact and exchange information.

Policies (in distributed systems)

Rules that govern how data is handled (cached, handled by downloaded code, or communicated).

Mechanisms (in distributed systems)

The technical ways of implementing the openness policies.

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Size scalability

The ability of a system to handle increasing amounts of data and users.

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Geographical scalability

The ability of a system to handle users located at different geographical locations.

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Administrative scalability

The system's ability to operate across different organizational or administrative domains.

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Centralized service

A system where all requests are handled by a single, central server.

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Queuing system

A model of a centralized service, where requests are put into a queue for processing.

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Arrival rate (λ)

The rate at which requests are submitted to a system.

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Processing capacity (μ)

The rate at which a system can process requests.

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Utilization (U)

The fraction of time a system is busy processing requests.

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Response time

The amount of time it takes to process a request.

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Asynchronous communication

Communication where a request is sent without waiting for an immediate response.

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Client-side computation

Processing data and executing operations on the client's machine instead of the server.

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Data replication

Creating multiple copies of data on different machines.

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Data consistency

Ensuring that all copies of data are identical.

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Global synchronization

A method of ensuring data consistency by coordinating updates across all copies.

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P2P (peer-to-peer)

A distributed system where clients directly interact and share resources with each other.

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BitTorrent

A peer-to-peer file-sharing system.

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Skype

A peer-to-peer telephony application.

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Spotify

A peer-to-peer audio streaming platform.

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Administrative scalability challenges

The issue of managing and controlling a distributed system across different administrative domains with potentially varying policies.

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Computational grids

A collection of computers coordinating to solve large tasks, typically shared resources.

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Shared equipment

Examples of shared resources requiring distributed management policies.

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Study Notes

Openness of Distributed Systems

  • Systems should interact with other open systems regardless of their underlying setups.
  • Systems need well-defined interfaces.
  • Systems should be able to interoperate easily.
  • Systems need to support application portability.
  • Systems should be easily extensible.

Policies versus Mechanisms (Implementing Openness)

  • Policies consider consistency levels for client-cached data.
  • Policies determine operations for downloaded code.
  • Policies adjust quality of service (QoS) requirements based on bandwidth.
  • Policies define security levels for communication.
  • Mechanisms allow dynamic caching policy settings.
  • Mechanisms support varying levels of trust for mobile code.
  • Mechanisms offer adjustable QoS parameters per data stream.
  • Mechanisms offer different encryption algorithms.

On Strict Separation

  • Stricter policy/mechanism separation necessitates more configuration parameters and complex management.
  • Hardcoding policies simplifies management, but reduces flexibility.

Scale in Distributed Systems

  • Many assume "scalability" without clarifying how their systems scale.
  • At least three components for scalable systems (size, geographic, administrative).
  • Size scalability considers user/process numbers.
  • Geographic scalability considers maximum distance between nodes.
  • Administrative scalability looks at the number of administrative domains.
  • Most systems typically only focus on size scalability initially, but geographic and administrative scalability remain important.

Size Scalability

  • Centralized solutions face scalability limits due to CPU, storage (and transfer rate to/from disk), and network constraints.

Formal Analysis (Centralized Service)

  • A conceptual model for a centralized service, presented as a queuing system.
  • Queue assumes infinite capacity (arrival rate unaffected by queue length, processing is independent).
  • Key parameters include arrival rate (A), processing capacity (μ) , or rate at which requests are processed.
  • Fraction of time the system has k requests is mathematically expressed, with λ and μ.

Formal Analysis (Utilization)

  • Utilization (U) measures the fraction of time a service is busy.
  • Mathematically expressed as U=1-P0.
  • where P0 = fraction of time with no requests.

Formal Analysis (Average Number of Requests)

  • Average number of requests in the system ñ can be calculated. ñ = ∑kâ‹…pk .

Formal Analysis (Average Throughput)

  • Average throughput (X) of the service is mathematically expressed. λ, μ is used.

Formal Analysis (Response Time)

  • Response time (R) is the total time taken to process a request after submission.
  • Mathematically expressed with service time S.
  • If service utilization (U) approaches 1, the system's response time increases significantly.

Client-Server vs. P2P: Example

  • Example showing how minimum distribution time differs between client-server and P2P systems.

Problems with Geographical Scalability

  • Systems often assume synchronous communications, which fail with wider area networks.
  • WAN links are likely unreliable, making reliable video streaming difficult.
  • Multi-point communication is often absent in many systems.

Problems with Administrative Scalability

  • Conflicting policies (usage, payment, management, security) across different domains in computational grids cause problems.
  • Managing shared resources, like radio telescopes used as large-scale sensor networks, is difficult.
  • Scaling in peer-to-peer networks (e.g., file sharing, telephony) present a contrasting example of successful sharing and administration.

Techniques for Scaling

  • Hiding communication latencies: using asynchronous communication and separate handlers for incoming responses.
  • Moving computations to clients: an alternative to processing at the server (e.g., Java applets).
  • Partitioning data and computations: dividing data among multiple machines (e.g., DNS and WWW).

Replication and Caching

  • Replication creates copies of data across different machines (e.g. files, DBs).
  • Replication introduces inconsistencies: managing multiple copies requires global synchronization.
  • Web sites that have replicated and cached contents, browsers, and server caching are examples of use.

Scaling: The Problem with Replication

  • Applying replication is usually easy, except for that replication often introduces inconsistencies.
  • Maintaining consistent copies across multiple servers (or clients) requires careful consideration of global synchronization
  • Global synchronization makes large-scale deployments difficult; inconsistencies are acceptable if the application's requirements allow them.

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