Distributed system lamport's and vector algorithmpinki soni
Logical clocks are mechanisms for capturing chronological and causal relationships in distributed systems that lack a global clock. Some key logical clock algorithms are Lamport's timestamps and vector clocks. Lamport's timestamps assign monotonically increasing numbers to events, while vector clocks allow for partial ordering of events. The algorithms for Lamport's timestamps and vector clocks involve incrementing and propagating clock values to determine causal relationships between events in a distributed system.
Logical clocks assign sequence numbers to distributed system events to determine causality without a global clock. Lamport's algorithm uses logical clocks to impose a partial ordering on events. Vector clocks extend this to also detect concurrent events that are not causally related, providing a full happened-before relation between all events. Each process maintains a vector clock that is incremented after local events and updated when receiving messages from other processes.
The document discusses different system models for distributed systems. It describes two main types of models: architectural models and fundamental models. Architectural models consider the placement of components across a network and relationships between components. Variations of the client-server model are presented, including mobile code, mobile agents, network computers, and thin clients. Design requirements for distributed architectures including performance issues, quality of service, caching/replication, and dependability are also covered.
Coda (Constant Data Avaialabilty) is a distributed file system developed at Carnegie Mellon University . This presentation explains how it works and different aspects of it.
Distributed shared memory (DSM) provides processes with a shared address space across distributed memory systems. DSM exists only virtually through primitives like read and write operations. It gives the illusion of physically shared memory while allowing loosely coupled distributed systems to share memory. DSM refers to applying this shared memory paradigm using distributed memory systems connected by a communication network. Each node has CPUs, memory, and blocks of shared memory can be cached locally but migrated on demand between nodes to maintain consistency.
This document discusses resource management techniques in distributed systems. It covers three main scheduling techniques: task assignment approach, load balancing approach, and load sharing approach. It also outlines desirable features of good global scheduling algorithms such as having no a priori knowledge about processes, being dynamic in nature, having quick decision-making capability, balancing system performance and scheduling overhead, stability, scalability, fault tolerance, and fairness of service. Finally, it discusses policies for load estimation, process transfer, state information exchange, location, priority assignment, and migration limiting that distributed load balancing algorithms employ.
Synchronization in distributed computingSVijaylakshmi
Synchronization in distributed systems is achieved via clocks. The physical clocks are used to adjust the time of nodes. Each node in the system can share its local time with other nodes in the system. The time is set based on UTC (Universal Time Coordination).
This document discusses various topics related to synchronization in distributed systems, including distributed algorithms, logical clocks, global state, and leader election. It provides definitions and examples of key synchronization concepts such as coordination, synchronization, and determining global states. Examples of logical clock algorithms like Lamport clocks and vector clocks are provided. Challenges around clock synchronization and calculating global system states are also summarized.
This document discusses structured naming in distributed systems. It describes name spaces as labeled, directed graphs with leaf nodes representing named entities and directory nodes linking to other nodes. Name resolution starts at the root node and follows the directory tables at each node until reaching the target node. Name spaces can be hierarchical trees or directed acyclic graphs. The Domain Name System (DNS) implements a global, hierarchical name space as a rooted tree with domain names representing subtrees.
This document discusses interprocess communication (IPC) and message passing in distributed systems. It covers key topics such as:
- The two main approaches to IPC - shared memory and message passing
- Desirable features of message passing systems like simplicity, uniform semantics, efficiency, reliability, correctness, flexibility, security, and portability
- Issues in message passing IPC like message format, synchronization methods (blocking vs. non-blocking), and buffering strategies
Data Link layer design issues, Error Detection and Correction, Elementary Data Link protocols: Unrestricted simplex protocol, Simplex stop-and-wait protocol, Simplex protocol for a noisy channel; Sliding Window protocols: One-bit sliding window protocol, Protocol using Go back N, Example.
Data link protocol: Higher Level Data Link Control, Data link layer in the internet. Internetworking and Advanced Internetworking Switching and Bridging, Basic Internetworking (IP), Routing, The Global Internet, Routing among Mobile Devices
1. There are two main approaches to distributed mutual exclusion - token-based and non-token based. Token based approaches use a shared token to allow only one process access at a time, while non-token approaches use message passing to determine access order.
2. A common token based algorithm uses a centralized coordinator process that grants access to the requesting process. Ring-based algorithms pass a token around a logical ring, allowing the process holding it to enter the critical section.
3. Lamport's non-token algorithm uses message passing of requests and timestamps to build identical request queues at each process, allowing the process at the head of the queue to enter the critical section. The Ricart-Agrawala
Message and Stream Oriented CommunicationDilum Bandara
Message and Stream Oriented Communication in distributed systems. Persistent vs. Transient Communication. Event queues, Pub/sub networks, MPI, Stream-based communication, Multicast communication
program partitioning and scheduling IN Advanced Computer ArchitecturePankaj Kumar Jain
Advanced Computer Architecture,Program Partitioning and Scheduling,Program Partitioning & Scheduling,Latency,Levels of Parallelism,Loop-level Parallelism,Subprogram-level Parallelism,Job or Program-Level Parallelism,Communication Latency,Grain Packing and Scheduling,Program Graphs and Packing
The document discusses various algorithms for achieving distributed mutual exclusion and process synchronization in distributed systems. It covers centralized, token ring, Ricart-Agrawala, Lamport, and decentralized algorithms. It also discusses election algorithms for selecting a coordinator process, including the Bully algorithm. The key techniques discussed are using logical clocks, message passing, and quorums to achieve mutual exclusion without a single point of failure.
File Replication : High availability is a desirable feature of a good distributed file system and file replication is the primary mechanism for improving file availability. Replication is a key strategy for improving reliability, fault tolerance and availability. Therefore duplicating files on multiple machines improves availability and performance.
Replicated file : A replicated file is a file that has multiple copies, with each copy located on a separate file server. Each copy of the set of copies that comprises a replicated file is referred to as replica of the replicated file.
Replication is often confused with caching, probably because they both deal with multiple copies of data. The two concepts has the following basic differences:
A replica is associated with server, whereas a cached copy is associated with a client.
The existence of cached copy is primarily dependent on the locality in file access patterns, whereas the existence of a replica normally depends on availability and performance requirements.
Satynarayanana [1992] distinguishes a replicated copy from a cached copy by calling the first-class replicas and second-class replicas respectively
This document provides teaching material on distributed systems replication from the book "Distributed Systems: Concepts and Design". It includes slides on replication concepts such as performance enhancement through replication, fault tolerance, and availability. The slides cover replication transparency, consistency requirements, system models, group communication, fault-tolerant and highly available services, and consistency criteria like linearizability.
SDN( Software Defined Network) and NFV(Network Function Virtualization) for I...Sagar Rai
Software, Software Defined Network, Network Function Virtualization, SDN, NFV, Internet of things, Basics of Internet of things, Network Basics, Virtualization, Limitation of Conventional Network, Open flow, Basics of conventional network,
Distributed deadlock occurs when processes are blocked while waiting for resources held by other processes in a distributed system without a central coordinator. There are four conditions for deadlock: mutual exclusion, hold and wait, non-preemption, and circular wait. Deadlock can be addressed by ignoring it, detecting and resolving occurrences, preventing conditions through constraints, or avoiding it through careful resource allocation. Detection methods include centralized coordination of resource graphs or distributed probe messages to identify resource waiting cycles. Prevention strategies impose timestamp or age-based priority to resource requests to eliminate cycles.
This document discusses consistency models in distributed systems with replication. It describes reasons for replication including reliability and performance. Various consistency models are covered, including: strict consistency where reads always return the most recent write; sequential consistency where operations appear in a consistent order across processes; weak consistency which enforces consistency on groups of operations; and release consistency which separates acquiring and releasing locks to selectively guard shared data. Client-centric models like eventual consistency are also discussed, where updates gradually propagate to all replicas.
The document discusses several algorithms for process synchronization and mutual exclusion in distributed systems:
- Token ring algorithm passes an exclusive token around a logical ring of processes to control access to shared resources.
- Ricart-Agrawala algorithm uses message ordering and timestamps to elect a process to enter the critical section.
- Lamport's mutual exclusion algorithm uses request queues and timestamps to ensure only one process is in the critical section at a time.
- Election algorithms like the Bully and Ring algorithms elect a coordinator process to handle synchronization and recovery from failures.
Physical clocks use quartz crystals or atomic vibrations to keep time, but they drift over time. Clock synchronization protocols like NTP and SNTP allow networked devices to regularly adjust their clocks to account for drift by requesting the time from authoritative time servers. They apply algorithms like Cristian's to compensate for network latency, setting the local clock to the average of the reported server time and round-trip delay time to minimize errors from network variability.
The document discusses the Sun Network Filesystem (NFS) architecture. NFS provides transparent remote access to filesystems across operating systems and machine architectures using remote procedure calls. It has design goals of machine and OS independence, crash recovery, and transparent access. The Virtual File System interface defines filesystem operations. NFS uses a stateless server model that authenticates users on each request to check access permissions. Pathname translation is done iteratively on the client side.
Logical clocks are mechanisms for capturing chronological and causal relationships in distributed systems. Lamport introduced logical clocks using timestamps assigned to events to define a "happens before" relation between events. Vector clocks extend logical timestamps to capture causality more accurately by maintaining a vector of timestamps, with one entry per process. Matrix clocks further extend this idea by maintaining a matrix to represent processes' knowledge of other processes' logical clocks.
1) Logical clocks are used to provide a total ordering of events in an asynchronous distributed system where there is no global clock. Each process maintains a local logical clock that is incremented for compute and send events. The clock is updated based on timestamp values piggybacked in received messages.
2) The happened-before relation defines a partial ordering of events based on causality. Logical clocks extend this to a total ordering but the ordering is arbitrary and may not match the perceived order of users.
3) While logical clocks provide total ordering, the timestamps do not preclude concurrent events from having the same value. Vector clocks were developed to address this limitation of Lamport clocks.
This document discusses various topics related to synchronization in distributed systems, including distributed algorithms, logical clocks, global state, and leader election. It provides definitions and examples of key synchronization concepts such as coordination, synchronization, and determining global states. Examples of logical clock algorithms like Lamport clocks and vector clocks are provided. Challenges around clock synchronization and calculating global system states are also summarized.
This document discusses structured naming in distributed systems. It describes name spaces as labeled, directed graphs with leaf nodes representing named entities and directory nodes linking to other nodes. Name resolution starts at the root node and follows the directory tables at each node until reaching the target node. Name spaces can be hierarchical trees or directed acyclic graphs. The Domain Name System (DNS) implements a global, hierarchical name space as a rooted tree with domain names representing subtrees.
This document discusses interprocess communication (IPC) and message passing in distributed systems. It covers key topics such as:
- The two main approaches to IPC - shared memory and message passing
- Desirable features of message passing systems like simplicity, uniform semantics, efficiency, reliability, correctness, flexibility, security, and portability
- Issues in message passing IPC like message format, synchronization methods (blocking vs. non-blocking), and buffering strategies
Data Link layer design issues, Error Detection and Correction, Elementary Data Link protocols: Unrestricted simplex protocol, Simplex stop-and-wait protocol, Simplex protocol for a noisy channel; Sliding Window protocols: One-bit sliding window protocol, Protocol using Go back N, Example.
Data link protocol: Higher Level Data Link Control, Data link layer in the internet. Internetworking and Advanced Internetworking Switching and Bridging, Basic Internetworking (IP), Routing, The Global Internet, Routing among Mobile Devices
1. There are two main approaches to distributed mutual exclusion - token-based and non-token based. Token based approaches use a shared token to allow only one process access at a time, while non-token approaches use message passing to determine access order.
2. A common token based algorithm uses a centralized coordinator process that grants access to the requesting process. Ring-based algorithms pass a token around a logical ring, allowing the process holding it to enter the critical section.
3. Lamport's non-token algorithm uses message passing of requests and timestamps to build identical request queues at each process, allowing the process at the head of the queue to enter the critical section. The Ricart-Agrawala
Message and Stream Oriented CommunicationDilum Bandara
Message and Stream Oriented Communication in distributed systems. Persistent vs. Transient Communication. Event queues, Pub/sub networks, MPI, Stream-based communication, Multicast communication
program partitioning and scheduling IN Advanced Computer ArchitecturePankaj Kumar Jain
Advanced Computer Architecture,Program Partitioning and Scheduling,Program Partitioning & Scheduling,Latency,Levels of Parallelism,Loop-level Parallelism,Subprogram-level Parallelism,Job or Program-Level Parallelism,Communication Latency,Grain Packing and Scheduling,Program Graphs and Packing
The document discusses various algorithms for achieving distributed mutual exclusion and process synchronization in distributed systems. It covers centralized, token ring, Ricart-Agrawala, Lamport, and decentralized algorithms. It also discusses election algorithms for selecting a coordinator process, including the Bully algorithm. The key techniques discussed are using logical clocks, message passing, and quorums to achieve mutual exclusion without a single point of failure.
File Replication : High availability is a desirable feature of a good distributed file system and file replication is the primary mechanism for improving file availability. Replication is a key strategy for improving reliability, fault tolerance and availability. Therefore duplicating files on multiple machines improves availability and performance.
Replicated file : A replicated file is a file that has multiple copies, with each copy located on a separate file server. Each copy of the set of copies that comprises a replicated file is referred to as replica of the replicated file.
Replication is often confused with caching, probably because they both deal with multiple copies of data. The two concepts has the following basic differences:
A replica is associated with server, whereas a cached copy is associated with a client.
The existence of cached copy is primarily dependent on the locality in file access patterns, whereas the existence of a replica normally depends on availability and performance requirements.
Satynarayanana [1992] distinguishes a replicated copy from a cached copy by calling the first-class replicas and second-class replicas respectively
This document provides teaching material on distributed systems replication from the book "Distributed Systems: Concepts and Design". It includes slides on replication concepts such as performance enhancement through replication, fault tolerance, and availability. The slides cover replication transparency, consistency requirements, system models, group communication, fault-tolerant and highly available services, and consistency criteria like linearizability.
SDN( Software Defined Network) and NFV(Network Function Virtualization) for I...Sagar Rai
Software, Software Defined Network, Network Function Virtualization, SDN, NFV, Internet of things, Basics of Internet of things, Network Basics, Virtualization, Limitation of Conventional Network, Open flow, Basics of conventional network,
Distributed deadlock occurs when processes are blocked while waiting for resources held by other processes in a distributed system without a central coordinator. There are four conditions for deadlock: mutual exclusion, hold and wait, non-preemption, and circular wait. Deadlock can be addressed by ignoring it, detecting and resolving occurrences, preventing conditions through constraints, or avoiding it through careful resource allocation. Detection methods include centralized coordination of resource graphs or distributed probe messages to identify resource waiting cycles. Prevention strategies impose timestamp or age-based priority to resource requests to eliminate cycles.
This document discusses consistency models in distributed systems with replication. It describes reasons for replication including reliability and performance. Various consistency models are covered, including: strict consistency where reads always return the most recent write; sequential consistency where operations appear in a consistent order across processes; weak consistency which enforces consistency on groups of operations; and release consistency which separates acquiring and releasing locks to selectively guard shared data. Client-centric models like eventual consistency are also discussed, where updates gradually propagate to all replicas.
The document discusses several algorithms for process synchronization and mutual exclusion in distributed systems:
- Token ring algorithm passes an exclusive token around a logical ring of processes to control access to shared resources.
- Ricart-Agrawala algorithm uses message ordering and timestamps to elect a process to enter the critical section.
- Lamport's mutual exclusion algorithm uses request queues and timestamps to ensure only one process is in the critical section at a time.
- Election algorithms like the Bully and Ring algorithms elect a coordinator process to handle synchronization and recovery from failures.
Physical clocks use quartz crystals or atomic vibrations to keep time, but they drift over time. Clock synchronization protocols like NTP and SNTP allow networked devices to regularly adjust their clocks to account for drift by requesting the time from authoritative time servers. They apply algorithms like Cristian's to compensate for network latency, setting the local clock to the average of the reported server time and round-trip delay time to minimize errors from network variability.
The document discusses the Sun Network Filesystem (NFS) architecture. NFS provides transparent remote access to filesystems across operating systems and machine architectures using remote procedure calls. It has design goals of machine and OS independence, crash recovery, and transparent access. The Virtual File System interface defines filesystem operations. NFS uses a stateless server model that authenticates users on each request to check access permissions. Pathname translation is done iteratively on the client side.
Logical clocks are mechanisms for capturing chronological and causal relationships in distributed systems. Lamport introduced logical clocks using timestamps assigned to events to define a "happens before" relation between events. Vector clocks extend logical timestamps to capture causality more accurately by maintaining a vector of timestamps, with one entry per process. Matrix clocks further extend this idea by maintaining a matrix to represent processes' knowledge of other processes' logical clocks.
1) Logical clocks are used to provide a total ordering of events in an asynchronous distributed system where there is no global clock. Each process maintains a local logical clock that is incremented for compute and send events. The clock is updated based on timestamp values piggybacked in received messages.
2) The happened-before relation defines a partial ordering of events based on causality. Logical clocks extend this to a total ordering but the ordering is arbitrary and may not match the perceived order of users.
3) While logical clocks provide total ordering, the timestamps do not preclude concurrent events from having the same value. Vector clocks were developed to address this limitation of Lamport clocks.
Chapter 5 discusses synchronization in distributed systems. Synchronization mechanisms are needed to enforce correct interaction between processes that share resources and run concurrently. Clock synchronization and event ordering are important synchronization techniques. Clock synchronization aims to keep clocks across distributed nodes close together despite unpredictable delays. It can be achieved through centralized or distributed algorithms. Event ordering ensures a total order of all events in a distributed system through happened-before relations and logical clocks.
The document discusses synchronization in distributed systems. It covers clock synchronization algorithms to ensure processes agree on time ordering of events. Logical clocks like Lamport timestamps are used when exact time is not needed, only causal ordering. Distributed mutual exclusion algorithms are discussed to allow only one process access to shared resources at a time, including token-based and permission-based approaches. The latter includes both a centralized and distributed algorithm.
This document summarizes key concepts around synchronization in distributed systems including clock synchronization, logical clocks, election algorithms, and mutual exclusion. It discusses how physical clocks become unsynchronized over time, introduces Lamport timestamps and logical clocks, describes the Bully and Ring election algorithms, and covers a centralized mutual exclusion algorithm that uses a coordinator to grant processes permission to enter a critical region.
This document discusses different types of clocks used in distributed systems, including physical clocks and logical clocks. Physical clocks are tied to real time but may drift over time. Logical clocks like Lamport's logical clock and vector clocks are derived from potential causality between events and can order events without reference to real time. The document covers clock synchronization algorithms like Cristian's algorithm and Berkeley algorithm for internal synchronization. It also discusses external synchronization using protocols like NTP. Logical clocks like Lamport's clock and vector clocks can order events based on potential causality between them using logical timestamps.
clock synchronization in Distributed System Harshita Ved
The document discusses various techniques for synchronizing clocks in distributed real-time systems. It begins by explaining that real-time systems require results within a certain time frame and interactions with the physical world. The challenges of distributed systems are then presented, where individual node clocks may run at different speeds and it is difficult to determine which event occurred first. Several clock synchronization algorithms are outlined, including using a global clock, averaging individual clocks, having an external time source, and assigning timestamps to messages. The Cristian and Berkeley algorithms are then described in more detail as centralized synchronization approaches where one node coordinates keeping all clocks aligned.
A distributed system is a network that consists of autonomous computers that are connected using a distribution middleware. They help in sharing different resources and capabilities to provide users with a single and integrated coherent network.
Distributed Systems (3rd Edition) Chapter 06 discusses coordination through clock synchronization and logical clocks. It describes several clock synchronization algorithms like Network Time Protocol that aim to keep physical clocks precise and accurate. Logical clocks like Lamport's logical clocks attach timestamps to events in a way that preserves causality without a global clock. Vector clocks were developed to guarantee causal precedence between timestamps. Total-ordered multicast uses logical clocks to ensure all processes see messages in the same order.
This document discusses various synchronization issues in distributed systems including clock synchronization, event ordering, mutual exclusion, and deadlock. It describes how computer clocks are implemented and different clock synchronization algorithms like centralized and distributed algorithms. It explains logical clocks and happened-before relation for event ordering. Different approaches for mutual exclusion like centralized, distributed, and token-passing are outlined. The four conditions for deadlock and different strategies like avoidance, prevention, and detection and recovery are summarized. Resource allocation graphs and wait-for graphs are introduced for modeling deadlocks.
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Distributed systems face challenges with time ordering and synchronization due to a lack of a global clock. Logical clocks provide a way to determine event ordering without precise time information. Lamport's algorithm uses logical clocks and the "happens before" relation to ensure proper synchronization. Physical clocks also require synchronization methods like Cristian's algorithm to limit clock drift between distributed nodes. Mutual exclusion in distributed systems can be achieved through centralized coordination of access to critical sections.
The document discusses the concept of time and ordering of events in distributed systems. It examines how the concept of one event happening before another defines a partial ordering of events. It then presents a distributed algorithm for synchronizing logical clocks across processes to totally order all events. This total ordering can be used to solve synchronization problems. The algorithm is also specialized to synchronize physical clocks and derive a bound on how out of sync they can become.
This document summarizes rate monotonic scheduling and earliest deadline first (EDF) scheduling algorithms.
Rate monotonic scheduling prioritizes processes inversely proportional to their period, with shorter periods having higher priority. It is optimal for scheduling periodic processes if their utilization is below 100%. EDF is an optimal dynamic priority algorithm that prioritizes processes based on absolute deadline, with earlier deadlines having higher priority. It can schedule up to 100% processor utilization while meeting all deadlines. Thread scheduling involves scheduling user level threads to kernel level threads via lightweight processes, and scheduling kernel threads using system scheduling policies.
osama-quantum-computing and its uses and applicationsRachitdas2
This document provides an overview of quantum computing. It begins with introductions to quantum mechanics and the basic concept of a quantum computer. Qubits can represent superpositions of states allowing quantum computers to perform massive parallelism. Data is represented using qubit states and operations involve entanglement. Measurement causes superpositions to collapse probabilistically. While quantum mechanics is strange, quantum computing may enable solving problems like factoring exponentially faster than classical computers. The document questions the Church-Turing thesis in light of quantum computing's ability.
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4. At 1’o clock, The first process P0 send a message to P1. This message
reaches P1 only at 6 pm in the evening.
At 2’0 clock, the first process P0 send an another message to the
process p3. It reaches P3 at 3 pm.
The processPp3 also sends one message to process P2 at 4 ‘o clock.
This message reaches P1 at 5 pm.
5. The important thing about this situation is that , the message from P2
arrives at P1, before the first message from P0 reaches p1 i.e the
first message arrives only at 6 pm. ( late arrival)
This timing problem should be solved .
7. The process P0 gives one apple to P1.
Immediately P0 sends a message to P2 that P1 has one apple
So P2 sends a message to P1 to eat that apple.
But the real situation is, P1 not yet received that apple. So how does
he eat it?
8. This example illustrates a causality violation.
A causality violation occurs when a message ordering problem results
in one host taking an action based on information that another host
should have received, but not yet received.
In this case , P2 is trying to invoke a method ( eat( ) ) on P1, because
P2 wrongly assumes that P1 has an apple.
9. Each process in a system should have knowledge of other processes
running in the system to find the causality violations. i.e An array of
knowledge is needed by each process.
Lamport logical lock is not sufficient to solve this problem, because it
tracks only the total number of events in the system
So a new communication mechanism is needed to solve this causality
violation problem. What is that mechanism?
Vector clocks .
10. Vector Clocks are used in a distributed systems to determine whether
pairs of events are causally related or not.
Using Vector Clocks, timestamps are generated for each event in the
system, and their causal relationship is determined by comparing those
timestamps.
Vector clock of a system of N processes is an array of N logical clocks,
i.e one clock per process.
So for N given processes, there will be a vector/ array of size N.
[ 1….N]
11. As with Lamport logical time, each process maintains its own
view of the local time and updates it using the timestamps
placed by the sender onto messages.
But with vector logical time, the time contains more information
i.e it contains a vector representing the state of each process
running in the distributed system.
In other words, this vector not only contains the event count for
the process itself, it also contains the last-known event counts on
each and every other process .
12. Initially, all the clocks are set to zero.
Every time, an Internal event occurs in a process, the value of the
processes' logical clock in the vector is incremented by 1
Two rules
Rule 1: ( Local update rule) Every time a process sends a message, the
value of the processes' logical clock in the vector is incremented by 1 and
and then send a copy of its own vector.
Rule 2: (message rule) Every time, a process receives a message, the
value of the processes' logical clock in the vector is incremented by 1.
Moreover, each element is updated by taking the maximum of the value
in its own vector clock and the value in the vector in the received message
.
13. As a result, when a process receives a message, it merges its own time
vector and the timestamp sent with the message -- it selects the higher
of the values for each element.
This ensures that the sender has information that is at least as up-to-
date as the receiver.
15. Assume, N = 3. i.e 3 processes are running p1, p2 and p3.
Apply rule 1 ( Local update rule)
The event a occurs in p1. So it increments its own logical clock in the
vector by 1. ( 1,0,0)
The event d occurs in p2. So it increments its own logical clock in
the vector by 1. ( 0,1,0)
The event g occurs in p3. So it increments its own logical clock in
the vector by 1. ( 0,0,1)
16. Now , event b occurs in process p1. So again apply rule 1 (local update
rule). Process p1 increments its clock in the vector by 1. ( 2,0,0).
17. The event b of process p1 sends a message to the event e in process
p2. Also sends a copy of its own vector to p2( By piggybacking. i.e
sends vector elements on the shoulder of message).
Now what will be the status of the vector clocks ?.
18. Apply rule 2
( Every time, a process receives a message, the value of the processes'
logical clock in the vector is incremented by 1. Moreover, each element is
updated by taking the maximum of the value in its own vector clock and
the value in the vector in the received message (for every element) ) .
First, Increment VC of p2 by 1 as per the rule. So ( 0,1,0) becomes
( 0,2,0) .
Vector clock values in the received message are ( 2, 0, 0). ( Already
sent by p1)
Take maximum of these two vector clock values
Time stamps
-----------
0, 2, 0 0 and 2 . maximum is 2
2, 0, 0 2 and 0 . maximum is 2
-----------
2, 2, 0
----------
20. Now, many events occur in all the processes
Event h occurs in process p3. So VCs become ( 0, 0, 2)
Event c occurs in process p1. Now, VCs become ( 3, 0, 0)
Event f occurs in process p2. Now, VCs become ( 2, 3, 0)
Event i occurs in process p3.
23. Now what will be the status of the vector clocks now ?.
Apply rule 2
first, Increment VC of p3 by 1 as per the rule
So ( 0, 0, 2) becomes ( 0, 0, 3 )
Vector clock values in the received message are ( Already sent by p2)
( 2, 3, 0).
Take maximum of these two vector clock values
Time stamps
------------
0, 0, 3 take maximum values
2, 3, 0
-----------
2, 3, 3
----------
25. Vector clocks easily captures the causality between inter-process
communications. Partial order (→) between a and i.
26. If two events are not comparable, we say these events are concurrent. But
Lamport logical clock mechanism can not find whether the events are
concurrent or not. This problem is solved by the vector clock mechanism
Using vector clock, timestamps are created for each event in the
distributed system and their relationship is determined by comparing
those timestamps. Whether they are concurrent or in a casual relationship.
Vector clock timestamps precisely capture happens-before relation
Vector Clocks guarantee strong clock consistency as they are an
extension of Lamport Timestamps.
.