Chapter 18 - Distributed Coordination

Chapter 18: Distributed Coordination

Chapter 18 Distributed Coordination Event Ordering Mutual Exclusion Atomicity Concurrency Control Deadlock Handling Election Algorithms Reaching Agreement

Chapter Objectives To describe various methods for achieving mutual exclusion in a distributed system To explain how atomic transactions can be implemented in a distributed system To show how some of the concurrency-control schemes discussed in Chapter 6 can be modified for use in a distributed environment To present schemes for handling deadlock prevention, deadlock avoidance, and deadlock detection in a distributed system

Event Ordering Happened-before relation (denoted by  ) If A and B are events in the same process, and A was executed before B , then A  B If A is the event of sending a message by one process and B is the event of receiving that message by another process, then A  B If A  B and B  C then A  C

Relative Time for Three Concurrent Processes

Implementation of  Associate a timestamp with each system event Require that for every pair of events A and B, if A  B, then the timestamp of A is less than the timestamp of B Within each process Pi a logical clock , LCi is associated The logical clock can be implemented as a simple counter that is incremented between any two successive events executed within a process Logical clock is monotonically increasing A process advances its logical clock when it receives a message whose timestamp is greater than the current value of its logical clock If the timestamps of two events A and B are the same, then the events are concurrent We may use the process identity numbers to break ties and to create a total ordering

Distributed Mutual Exclusion (DME) Assumptions The system consists of n processes; each process P i resides at a different processor Each process has a critical section that requires mutual exclusion Requirement If P i is executing in its critical section, then no other process P j is executing in its critical section We present two algorithms to ensure the mutual exclusion execution of processes in their critical sections

DME: Centralized Approach One of the processes in the system is chosen to coordinate the entry to the critical section A process that wants to enter its critical section sends a request message to the coordinator The coordinator decides which process can enter the critical section next, and its sends that process a reply message When the process receives a reply message from the coordinator, it enters its critical section After exiting its critical section, the process sends a release message to the coordinator and proceeds with its execution This scheme requires three messages per critical-section entry: request reply release

DME: Fully Distributed Approach When process P i wants to enter its critical section, it generates a new timestamp, TS , and sends the message request ( P i , TS ) to all other processes in the system When process P j receives a request message, it may reply immediately or it may defer sending a reply back When process P i receives a reply message from all other processes in the system, it can enter its critical section After exiting its critical section, the process sends reply messages to all its deferred requests

DME: Fully Distributed Approach (Cont.) The decision whether process P j replies immediately to a request ( P i , TS ) message or defers its reply is based on three factors: If P j is in its critical section, then it defers its reply to P i If P j does not want to enter its critical section, then it sends a reply immediately to P i If P j wants to enter its critical section but has not yet entered it, then it compares its own request timestamp with the timestamp TS If its own request timestamp is greater than TS , then it sends a reply immediately to P i ( P i asked first) Otherwise, the reply is deferred

Desirable Behavior of Fully Distributed Approach Freedom from Deadlock is ensured Freedom from starvation is ensured, since entry to the critical section is scheduled according to the timestamp ordering The timestamp ordering ensures that processes are served in a first-come, first served order The number of messages per critical-section entry is 2 x ( n – 1) This is the minimum number of required messages per critical-section entry when processes act independently and concurrently

Three Undesirable Consequences The processes need to know the identity of all other processes in the system, which makes the dynamic addition and removal of processes more complex If one of the processes fails, then the entire scheme collapses This can be dealt with by continuously monitoring the state of all the processes in the system Processes that have not entered their critical section must pause frequently to assure other processes that they intend to enter the critical section This protocol is therefore suited for small, stable sets of cooperating processes

Token-Passing Approach Circulate a token among processes in system Token is special type of message Possession of token entitles holder to enter critical section Processes logically organized in a ring structure Unidirectional ring guarantees freedom from starvation Two types of failures Lost token – election must be called Failed processes – new logical ring established

Atomicity Either all the operations associated with a program unit are executed to completion, or none are performed Ensuring atomicity in a distributed system requires a transaction coordinator , which is responsible for the following: Starting the execution of the transaction Breaking the transaction into a number of subtransactions, and distribution these subtransactions to the appropriate sites for execution Coordinating the termination of the transaction, which may result in the transaction being committed at all sites or aborted at all sites

Two-Phase Commit Protocol (2PC) Assumes fail-stop model Execution of the protocol is initiated by the coordinator after the last step of the transaction has been reached When the protocol is initiated, the transaction may still be executing at some of the local sites The protocol involves all the local sites at which the transaction executed Example: Let T be a transaction initiated at site S i and let the transaction coordinator at S i be C i

Phase 1: Obtaining a Decision C i adds <prepare T > record to the log C i sends <prepare T > message to all sites When a site receives a <prepare T > message, the transaction manager determines if it can commit the transaction If no: add <no T > record to the log and respond to C i with <abort T > If yes: add <ready T > record to the log force all log records for T onto stable storage send <ready T > message to C i

Phase 1 (Cont.) Coordinator collects responses All respond “ready”, decision is commit At least one response is “abort”, decision is abort At least one participant fails to respond within time out period, decision is abort

Phase 2: Recording Decision in the Database Coordinator adds a decision record <abort T > or <commit T > to its log and forces record onto stable storage Once that record reaches stable storage it is irrevocable (even if failures occur) Coordinator sends a message to each participant informing it of the decision (commit or abort) Participants take appropriate action locally

Failure Handling in 2PC – Site Failure The log contains a <commit T > record In this case, the site executes redo ( T ) The log contains an <abort T > record In this case, the site executes undo ( T ) The contains a <ready T > record; consult C i If C i is down, site sends query-status T message to the other sites The log contains no control records concerning T In this case, the site executes undo ( T )

Failure Handling in 2PC – Coordinator C i Failure If an active site contains a <commit T > record in its log, the T must be committed If an active site contains an <abort T > record in its log, then T must be aborted If some active site does not contain the record <ready T > in its log then the failed coordinator C i cannot have decided to commit T Rather than wait for C i to recover, it is preferable to abort T All active sites have a <ready T > record in their logs, but no additional control records In this case we must wait for the coordinator to recover Blocking problem – T is blocked pending the recovery of site S i

Concurrency Control Modify the centralized concurrency schemes to accommodate the distribution of transactions Transaction manager coordinates execution of transactions (or subtransactions) that access data at local sites Local transaction only executes at that site Global transaction executes at several sites

Locking Protocols Can use the two-phase locking protocol in a distributed environment by changing how the lock manager is implemented Nonreplicated scheme – each site maintains a local lock manager which administers lock and unlock requests for those data items that are stored in that site Simple implementation involves two message transfers for handling lock requests, and one message transfer for handling unlock requests Deadlock handling is more complex

Single-Coordinator Approach A single lock manager resides in a single chosen site, all lock and unlock requests are made a that site Simple implementation Simple deadlock handling Possibility of bottleneck Vulnerable to loss of concurrency controller if single site fails Multiple-coordinator approach distributes lock-manager function over several sites

Majority Protocol Avoids drawbacks of central control by dealing with replicated data in a decentralized manner More complicated to implement Deadlock-handling algorithms must be modified; possible for deadlock to occur in locking only one data item

Biased Protocol Similar to majority protocol, but requests for shared locks prioritized over requests for exclusive locks Less overhead on read operations than in majority protocol; but has additional overhead on writes Like majority protocol, deadlock handling is complex

Primary Copy One of the sites at which a replica resides is designated as the primary site Request to lock a data item is made at the primary site of that data item Concurrency control for replicated data handled in a manner similar to that of unreplicated data Simple implementation, but if primary site fails, the data item is unavailable, even though other sites may have a replica

Timestamping Generate unique timestamps in distributed scheme: Each site generates a unique local timestamp The global unique timestamp is obtained by concatenation of the unique local timestamp with the unique site identifier Use a logical clock defined within each site to ensure the fair generation of timestamps Timestamp-ordering scheme – combine the centralized concurrency control timestamp scheme with the 2PC protocol to obtain a protocol that ensures serializability with no cascading rollbacks

Generation of Unique Timestamps

Deadlock Prevention Resource-ordering deadlock-prevention – define a global ordering among the system resources Assign a unique number to all system resources A process may request a resource with unique number i only if it is not holding a resource with a unique number grater than i Simple to implement; requires little overhead Banker’s algorithm – designate one of the processes in the system as the process that maintains the information necessary to carry out the Banker’s algorithm Also implemented easily, but may require too much overhead

Timestamped Deadlock-Prevention Scheme Each process P i is assigned a unique priority number Priority numbers are used to decide whether a process P i should wait for a process P j ; otherwise P i is rolled back The scheme prevents deadlocks For every edge P i  P j in the wait-for graph, P i has a higher priority than P j Thus a cycle cannot exist

Wait-Die Scheme Based on a nonpreemptive technique If P i requests a resource currently held by P j , P i is allowed to wait only if it has a smaller timestamp than does P j ( P i is older than P j ) Otherwise, P i is rolled back (dies) Example: Suppose that processes P 1 , P 2 , and P 3 have timestamps 5, 10, and 15 respectively if P 1 request a resource held by P 2 , then P 1 will wait If P 3 requests a resource held by P 2 , then P 3 will be rolled back

Would-Wait Scheme Based on a preemptive technique; counterpart to the wait-die system If P i requests a resource currently held by P j , P i is allowed to wait only if it has a larger timestamp than does P j ( P i is younger than P j ). Otherwise P j is rolled back ( P j is wounded by P i ) Example: Suppose that processes P 1 , P 2, and P 3 have timestamps 5, 10, and 15 respectively If P 1 requests a resource held by P 2 , then the resource will be preempted from P 2 and P 2 will be rolled back If P 3 requests a resource held by P 2 , then P 3 will wait

Deadlock Detection Use wait-for graphs Local wait-for graphs at each local site . The nodes of the graph correspond to all the processes that are currently either holding or requesting any of the resources local to that site May also use a global wait-for graph. This graph is the union of all local wait-for graphs.

Deadlock Detection – Centralized Approach Each site keeps a local wait-for graph A global wait-for graph is maintained in a single coordination process. There are three different options (points in time) when the wait-for graph may be constructed: 1. Whenever a new edge is inserted or removed in one of the local wait-for graphs 2. Periodically, when a number of changes have occurred in a wait-for graph 3. Whenever the coordinator needs to invoke the cycle-detection algorithm Unnecessary rollbacks may occur as a result of false cycles

Detection Algorithm Based on Option 3 Append unique identifiers (timestamps) to requests form different sites When process P i , at site A , requests a resource from process P j , at site B , a request message with timestamp TS is sent The edge P i  P j with the label TS is inserted in the local wait-for of A . The edge is inserted in the local wait-for graph of B only if B has received the request message and cannot immediately grant the requested resource

The Algorithm 1. The controller sends an initiating message to each site in the system 2. On receiving this message, a site sends its local wait-for graph to the coordinator 3. When the controller has received a reply from each site, it constructs a graph as follows: (a) The constructed graph contains a vertex for every process in the system (b) The graph has an edge Pi  Pj if and only if there is an edge Pi  Pj in one of the wait-for graphs, or an edge Pi  Pj with some label TS appears in more than one wait-for graph If the constructed graph contains a cycle  deadlock

Local and Global Wait-For Graphs

Fully Distributed Approach All controllers share equally the responsibility for detecting deadlock Every site constructs a wait-for graph that represents a part of the total graph We add one additional node P ex to each local wait-for graph If a local wait-for graph contains a cycle that does not involve node P ex , then the system is in a deadlock state A cycle involving P ex implies the possibility of a deadlock To ascertain whether a deadlock does exist, a distributed deadlock-detection algorithm must be invoked

Augmented Local Wait-For Graphs

Augmented Local Wait-For Graph in Site S2

Election Algorithms Determine where a new copy of the coordinator should be restarted Assume that a unique priority number is associated with each active process in the system, and assume that the priority number of process P i is i Assume a one-to-one correspondence between processes and sites The coordinator is always the process with the largest priority number. When a coordinator fails, the algorithm must elect that active process with the largest priority number Two algorithms, the bully algorithm and a ring algorithm, can be used to elect a new coordinator in case of failures

Bully Algorithm Applicable to systems where every process can send a message to every other process in the system If process P i sends a request that is not answered by the coordinator within a time interval T , assume that the coordinator has failed; P i tries to elect itself as the new coordinator P i sends an election message to every process with a higher priority number, P i then waits for any of these processes to answer within T

Bully Algorithm (Cont.) If no response within T , assume that all processes with numbers greater than i have failed; P i elects itself the new coordinator If answer is received, P i begins time interval T´, waiting to receive a message that a process with a higher priority number has been elected If no message is sent within T´, assume the process with a higher number has failed; P i should restart the algorithm

Bully Algorithm (Cont.) If P i is not the coordinator, then, at any time during execution, P i may receive one of the following two messages from process P j P j is the new coordinator ( j > i ). P i , in turn, records this information P j started an election ( j > i ). P i , sends a response to P j and begins its own election algorithm, provided that Pi has not already initiated such an election After a failed process recovers, it immediately begins execution of the same algorithm If there are no active processes with higher numbers, the recovered process forces all processes with lower number to let it become the coordinator process, even if there is a currently active coordinator with a lower number

Ring Algorithm Applicable to systems organized as a ring (logically or physically) Assumes that the links are unidirectional, and that processes send their messages to their right neighbors Each process maintains an active list, consisting of all the priority numbers of all active processes in the system when the algorithm ends If process Pi detects a coordinator failure, I creates a new active list that is initially empty. It then sends a message elect(i) to its right neighbor, and adds the number i to its active list

Ring Algorithm (Cont.) If P i receives a message elect( j ) from the process on the left, it must respond in one of three ways: If this is the first elect message it has seen or sent, P i creates a new active list with the numbers i and j It then sends the message elect(i), followed by the message elect(j) If i  j , then the active list for P i now contains the numbers of all the active processes in the system P i can now determine the largest number in the active list to identify the new coordinator process If i = j , then P i receives the message elect(i) The active list for P i contains all the active processes in the system P i can now determine the new coordinator process.

Reaching Agreement There are applications where a set of processes wish to agree on a common “value” Such agreement may not take place due to: Faulty communication medium Faulty processes Processes may send garbled or incorrect messages to other processes A subset of the processes may collaborate with each other in an attempt to defeat the scheme

Faulty Communications Process P i at site A , has sent a message to process P j at site B ; to proceed, P i needs to know if P j has received the message Detect failures using a time-out scheme When P i sends out a message, it also specifies a time interval during which it is willing to wait for an acknowledgment message form P j When P j receives the message, it immediately sends an acknowledgment to P i If P i receives the acknowledgment message within the specified time interval, it concludes that P j has received its message If a time-out occurs, P j needs to retransmit its message and wait for an acknowledgment Continue until P i either receives an acknowledgment, or is notified by the system that B is down

Faulty Communications (Cont.) Suppose that P j also needs to know that P i has received its acknowledgment message, in order to decide on how to proceed In the presence of failure, it is not possible to accomplish this task It is not possible in a distributed environment for processes P i and P j to agree completely on their respective states

Faulty Processes (Byzantine Generals Problem) Communication medium is reliable, but processes can fail in unpredictable ways Consider a system of n processes, of which no more than m are faulty Suppose that each process P i has some private value of V i Devise an algorithm that allows each nonfaulty P i to construct a vector X i = ( A i, 1 , A i ,2 , …, A i,n ) such that:: If P j is a nonfaulty process, then A ij = V j. If P i and P j are both nonfaulty processes, then X i = X j . Solutions share the following properties A correct algorithm can be devised only if n  3 x m + 1 The worst-case delay for reaching agreement is proportionate to m + 1 message-passing delays

Faulty Processes (Cont.) An algorithm for the case where m = 1 and n = 4 requires two rounds of information exchange: Each process sends its private value to the other 3 processes Each process sends the information it has obtained in the first round to all other processes If a faulty process refuses to send messages, a nonfaulty process can choose an arbitrary value and pretend that that value was sent by that process After the two rounds are completed, a nonfaulty process Pi can construct its vector Xi = (Ai,1, Ai,2, Ai,3, Ai,4) as follows: A i,j = V i For j  i, if at least two of the three values reported for process P j agree, then the majority value is used to set the value of A ij Otherwise, a default value ( nil ) is used

Chapter 18 - Distributed Coordination

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Chapter 18 - Distributed Coordination