GROUP COMMUNICATION: Multicast Communication-Causal Ordering of message and Causal Order Multicast Protocol

GROUP COMMUNICATION: Multicast Communication-Causal Ordering of message and Causal Order Multicast Protocol.

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Group Communication: Multicast Communication & Causal Ordering of Messages

1. Multicast Communication

Multicast communication refers to the process of sending a message from one sender to multiple receivers in a group. It is commonly used in distributed systems, network communication, and parallel computing to efficiently distribute data.

Types of Multicast Communication

• Unreliable Multicast: No guarantee that all receivers get the message.
• Reliable Multicast: Ensures all receivers get the message.
• Ordered Multicast: Ensures messages follow a specific order.

2. Causal Ordering of Messages

Causal ordering ensures that messages are delivered to all receivers in an order that preserves cause-effect relationships.

Example of Causal Ordering

• Suppose Process P1 sends message M1, and Process P2 sends M2 after receiving M1.
• Causal ordering ensures that all processes receive M1 before M2 to maintain logical consistency.

Mathematical Representation (Happens-Before Relation)

• If M1 → M2 (M1 happens before M2), then all processes must receive M1 before M2.
• This is often denoted as Lamport’s Happens-Before Relation (→).

3. Causal Order Multicast Protocol

The Causal Order Multicast Protocol ensures that all messages are delivered in a way that respects causal dependencies.

Key Components of the Protocol

1. Vector Clocks – Each process maintains a vector timestamp to track causal relationships.
2. Message Buffering – Messages are buffered until all causally preceding messages arrive.
3. Dependency Tracking – Each message carries its timestamp to enforce correct ordering.

Steps in Causal Order Multicast

1. Sender Process: Assigns a vector timestamp to the message.
2. Multicast Transmission: The message is sent to all processes.
3. Receiver Process:
   • Checks the vector timestamp.
   • If causally dependent messages are missing, it buffers the message.
   • Delivers the message when dependencies are met.

Example Scenario

• P1 sends M1 → [1,0,0]
• P2 receives M1 and sends M2 → [1,1,0]
• P3 must receive M1 before M2 to maintain causality.

4. Applications of Causal Order Multicast

• Distributed Databases: Ensures consistent updates across replicas.
• Collaborative Applications: Chat systems, document editing tools.
• Event-Based Systems: Financial transactions, stock market updates.

Conclusion

Causal order multicast is essential in distributed systems to ensure consistent and predictable message delivery. The use of vector clocks and message buffering allows systems to maintain logical consistency, preventing misordering of messages that could lead to inconsistencies.

GROUP COMMUNICATION: Multicast Communication-Causal Ordering of message and Causal Order Multicast Protocol

Message Ordering and Group Communication

Communication in Distributed Systems

Group Communication

Here’s a clear explanation of Group Communication, particularly focused on Multicast Communication, Causal Ordering, and Causal Order Multicast Protocol — key concepts in Distributed Systems (important for B.Tech, GATE, and Computer Science interviews):

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GROUP COMMUNICATION IN DISTRIBUTED SYSTEMS

What Is Group Communication?

Group communication allows a process to send messages to multiple recipients (a group) in a distributed system, instead of point-to-point.

Unicast vs Multicast vs Broadcast

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MULTICAST COMMUNICATION

Multicast

Multicast is the core mechanism to send a message from one process to a selected group of processes.

Example: Sending an update to all replicas of a database server.

Multicast can have different levels of message ordering guarantees:

• Unordered Multicast

• FIFO-ordered Multicast

• Causally-ordered Multicast

• Totally-ordered Multicast

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CAUSAL ORDERING OF MESSAGES

What is Causal Ordering?

Causal ordering ensures that if one message causally affects another, then all processes deliver them in that order.

If M1 → M2 (M1 causally affects M2), then M1 must be delivered before M2 by every process.

Defined using Lamport’s “happened-before” (→) relation.

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Why Causal Order Matters:

• Ensures consistency in chat systems, collaborative apps, replicated data.

• Prevents anomalies like delivering a reply before the question.

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CAUSAL ORDER MULTICAST PROTOCOL

Goal:

Ensure causally related messages are delivered in the same causal order at all receivers.

How It Works:

Uses Vector Clocks (a logical clock array) to track causality between events.

Protocol Steps:

1. Each process maintains a vector clock V[i].

2. Before sending a message:

   • Increment own entry: V[i] = V[i] + 1

   • Attach full vector clock to the message

3. When receiving a message M from process P:

   • Buffer the message until the following condition is met:

     pgsql

     CopyEdit

     ∀ j ≠ P: M.V[j] ≤ Local.V[j]
and
M.V[P] == Local.V[P] + 1


   • This ensures all messages that causally precede M have been delivered first.

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Example:

Imagine 3 processes: P1, P2, P3

• P1 sends M1 → P2

• P2 replies with M2 → P1 and P3

If causal ordering is not preserved, P3 may receive M2 before M1 → violates logic

Causal Order Multicast ensures M1 is delivered before M2 at P3.

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Limitations

• Extra Overhead due to vector clocks

• Doesn’t ensure total ordering (only causal)

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Related Terms:

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Summary:

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GROUP COMMUNICATION: Multicast Communication-Causal Ordering of message and Causal Order Multicast Protocol