Synchronization Part II. CS403/534 Distributed Systems Erkay Savas Sabanci University
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1 Synchronization Part II CS403/534 Distributed Systems Erkay Savas Sabanci University 1
2 Election Algorithms Issue: Many distributed algorithms require that one process act as a coordinator (initiator, etc). In many systems this is done manually. This leads to centralized solutions single point of failure The question is how to select this special process dynamically. Advantage of dynamic selection If the coordinator dies, then another coordinator can be elected dynamically. Assumptions: Every process has a unique ID and processes know each other s ID A running process with the highest ID becomes the coordinator 2
3 The Bully Algorithm Algorithm: When a process P notices that the coordinator is not responding, it holds an election as follows: 1. Process P sends an ELECTION message to all processes with higher ID numbers 2. If no one responds, P wins the election and becomes coordinator 3. If an OK message is returned by a process, P gives up and responding process holds an election. 3
4 The Bully Algorithm: Example (1) election election OK 1 OK election Process 4 holds an election Process 5 and 6 respond, telling 4 to stop Now 5 and 6 each hold an election 4
5 The Bully Algorithm: Example (2) d) Process 6 tells 5 to stop e) Process 6 wins and tells everyone OK 6 4 Coordinator
6 A Ring Algorithm (1) Principle: Processes are arranged on a ring and process with the highest ID number is elected as a coordinator Algorithm: Any process that notices the coordinator is down can start the election. It first builds an ELECTION message containing its own ID number Sends this message to its successor Process that receives this message adds its number and passes it along Eventually, the message comes back to the initiator with a list of alive processes. 6
7 A Ring Algorithm (2) [5,6,0] 1 [5,6,0,1] 0 2 [2] 7 3 [5, 6] no response 6 6 is the new coordinator 4 [2,3] [5] 5 Both 2 and 5 notice the coordinator (7) is down 7
8 Mutual Exclusion Problem: A number of processes in a distributed system want exclusive access to a resource Solutions: 1. Via a coordinator 2. Completely distributed, with no topology imposed 3. Completely distributed, making use of a (logical) ring Centralized approach The coordinator grants or defers permission It keeps the deferred requests in a queue When the resource is available, it picks up the deferred request from the top of queue and sends the blocked process a grant message 8
9 Mutual Exclusion: A Centralized Algorithm request coordinator 3 OK request 3 no reply 2 release 3 OK Queue Queue Queue a) Process 1 asks the coordinator for permission to enter a critical region. Permission is granted b) Process 2 then asks permission to enter the same critical region. The coordinator does not reply. c) When process 1 exits the critical region, it tells the coordinator, when then replies to 2 9
10 Distributed Mutual Exclusion Algorithm Assumptions: Processes use logical clocks (totally-ordered) Sending messages are reliable (i.e. ACKed) Algorithm: When a process wants to enter a critical region, it sends to all other processes a message containing the name of the critical region, its process ID, and its current logical time 1. If the receiver is not in the critical region and does not want to enter it, it sends back an OK message 2. If the sender is already in the critical region, it does not reply. It queues the request. 3. If the receiver also wants to enter the critical region but has not yet done so, it compares the timestamps. The one with the lower timestamp wins. 10
11 A Distributed Algorithm enters critical region OK 0 OK 1 2 OK 0 OK enters critical region a) Two processes want to enter the same critical region at the same moment. b) Process 0 has the lowest timestamp, so it wins. c) When process 0 is done, it sends an OK also, so 2 can now enter the critical region. 11
12 Essence: Token Ring Mutual Exclusion Algorithm Organize the processes in a logical ring, and let a token be passed between them. The one that holds the token is allowed to enter the critical region (if it wants to) Problems: The token may get lost (It is not easy to detect it) A process can crash and it cannot pass the token along. If we require a process receiving the token to acknowledge the receipt, a dead process can be detected. 12
13 A Toke Ring Algorithm A logical ring constructed in software. 13
14 Comparison of Mutual Exclusion Algorithms Algorithm Messages per entry/exit Delay before entry (in message numbers) Problems Centralized 3 2 Coordinator crash Distributed 2 ( n 1 ) 2 ( n 1 ) Crash of any process Token ring 1 to 0 to n 1 Lost token, process crash A comparison of three mutual exclusion algorithms. Assumption: Point-to-point communication channels are used 14
15 Distributed Transactions They allow a process to access and modify many data items as a single atomic operation Therefore they are strongly related to mutual exclusion. If the process cannot complete all the suboperations, everything will be restored to the state before the transaction began All-ornothing property Outline The transaction model Classification of transactions Concurrency control 15
16 Transactions Example: A customer performs the following two operations on its bank account remotely 1. Withdraw an amount A from account 1 2. Deposit amount A to account 2 What happens if the connection is broken only after the first operation is performed? Two operations in this example, must be treated as part of a transaction either both would be completed, or neither would be completed 16
17 Transaction Primitives Primitive BEGIN_TRANSACTION END_TRANSACTION ABORT_TRANSACTION READ WRITE Description Make the start of a transaction Terminate the transaction and try to commit Kill the transaction and restore the old values Read data from a file, a table, or otherwise Write data to a file, a table, or otherwise Primitives must either be supplied by the underlying distributed systems or by the language runtime system 17
18 Example: Plane Reservation BEGIN_TRANSACTION reserve ANK -> IST; reserve IST -> CHI; reserve CHI -> LAX; END_TRANSACTION (a) 1. BEGIN_TRANSACTION 2. reserve ANK -> IST; OK 3. reserve IST -> CHI; OK 4. reserve CHI -> LAX full 5. ABORT_TRANSACTION (b) a) Transaction to reserve three flights commits b) Transaction aborts when third flight is unavailable 18
19 Transaction: ACID Properties Atomicity: To the outside world, a transaction is indivisible. Consistent: The transaction does not violate system invariants. e.g., the law of conservation of money in a bank. A transaction establishes a valid state transition. Invalid, intermediate states during the transaction s execution are possible Isolated: Concurrent transactions do not interfere with each other. Transactions are isolated or serializable. Durable: Once a transaction commits, the changes are permanent 19
20 Classification: Flat Transaction The simplest and most familiar one. Satisfies the ACID properties. Problem: It does not allow partial results to be committed or aborted. Example: flight reservation It would be nice if we made any subset of the three reservations (commit them) and dealt with the rest later. 20
21 Classification: Nested Transactions A nested transaction is constructed from a number of subtransactions Parent transaction may fork off subtransactions that may execute in isolation (possibly on different machines) Once a subtransaction commits, its state will become visible to the parent transaction. If the parent transaction aborts, all child subtransactions have to abort as well The permanency of subtransactions are not applicable A subtransaction is given a private copy of all the data in the entire system to manipulate it. 21
22 Classification: Distributed Transactions A distributed transaction is a flat, indivisible transaction that operates on distributed data. Nested transactions generally follow logical division of the work of the original transaction. A subtransaction can still be distributed The main problem of distributed transaction is that it requires distributed locking algorithms. 22
23 Implementing Transactions Solution 1: private workspace Case Study: Transactions on file system When a process starts a transaction, it is given a private workspace to work All the reads and writes are done on this private workspace, not on the original disk blocks. All the other processes see the original blocks of file until the transaction commits The transaction will have a private view of the file system After committing, this view will be globally unique 23
24 index Private Workspace original private index workspace index free blocks a) The file index and disk blocks for a three-block file b) The situation after a transaction has modified block 0 and appended block 3 c) After committing 24
25 Implementing Transactions Solution 2: writeahead log Use a writeahead log in which changes are recorded allowing you to rollback when things go wrong. x = 0; Log Log Log y = 0; BEGIN_TRANSACTION; x = x + 1; [x = 0 / 1] [x = 0 / 1] [x = 0 / 1] y = y + 2; [y = 0/2] [y = 0/2] x = y * y; [x = 1/4] END_TRANSACTION; (a) (b) (c) (d) a) A transaction b) d) The log before each statement is executed 25
26 Concurrency Control (1) Problem Increase efficiency by allowing several transactions to execute at the same time and we have to do it in such a way that consistency and isolation properties are not violated. Constraint Effect should be the same as if the transactions were executed in some serial (sequential) order. The same data items must be accessed in a specific order by different transactions However, the final result is the same as if all transactions had run sequentially. 26
27 Concurrency Control: Layered Organization Transactions READ/WRITE BEGIN_TRANSACTION END_TRANSACTION Transaction manager Atomicity LOCK/RELEASE or Timestamp operations Scheduler Controlling concurrency (isolation & consistency ) Data manager Execute read/write General organization of managers for handling transactions. 27
28 Concurrency Control in Distributed Systems transaction manager scheduler scheduler scheduler General organization of managers for handling distributed transactions. data manager data manager data manager Machine A Machine B Machine C 28
29 Serializability Consider a collection E of transactions: T 1,, T n. Goal is to conduct a serializable execution of E: Transactions in E are possibly concurrently executed according to some schedule S. Schedule S is equivalent to some totally ordered execution of suboperations in T 1,, T n. 29
30 Serializability: Example T 1 : BEGIN_TRANSACTION x = 0; x = x + 1; END_TRANSACTION T 2 : BEGIN_TRANSACTION x = 0; x = x + 2; END_TRANSACTION T 3 : BEGIN_TRANSACTION x = 0; x = x + 3; END_TRANSACTION (a) (b) (c) Schedule 1 Schedule 2 Schedule 3 x = 0; x = x + 1; x = 0; x = x + 2; x = 0; x = x + 3; x = 0; x = 0; x = x + 1; x = x + 2; x = 0; x = x + 3; x = 0; x = 0; x = x + 1; x = 0; x = x + 2; x = x + 3; Legal/serialized Legal/unseriliazed Illegal (d) a) c)three transactions T 1, T 2, and T 3 d)possible schedules 30
31 Serializability : Conflicts Model of computation: Since we are not interested in the specific computations of each transaction, a transaction can be modeled as a series of read and write operations on data items. Conflicting operations: Two operations oper(t i, x) and oper(t j, x) of two transactions T i and T j on the same data item x may conflict at data manager. read-write conflict: One operation is a read and the other is a write write-write conflict: Both are write operations Two read operations never conflict 31
32 Synchronization Concurrency control algorithms can generally be classified by looking at the way read and write operations are synchronized Synchronization can be achieved through Mutual exclusion mechanism on shared data (i.e. locking). Before writing or reading of a data item, a lock must be obtained. Explicitly ordering operations using timestamps. Data managers are forced to execute read and writes following timestamp order Optimistic and pessimistic concurrency control 32
33 Two-Phase Locking (2PL) Transactions indicate their intentions by requesting locks from the scheduler (a.k.a lock manager). Locks are either read lock (a.k.a. shared lock) or write lock (a.k.a. exclusive lock) Read locks and write locks conflict (because read and write operations are incompatible) Read lock Write lock Read lock Yes No Write lock No No Scheduler needs to apply an algorithm that provides only serializable schedules. 33
34 2PL: Rules 1. A transaction must lock an object before using it. 2. If the object is locked by another transaction, the requesting transaction must wait (unless it is a non-conflicting lock). 3. When a transaction releases a lock, it may not request another lock. These rules guarantee lock operation release Growing phase: A transaction collects locks on data items it needs to operate on Shrinking phase: starts with the first unlock operation 34
35 2PL Two-phase locking. 35
36 Strict two-phase locking. Strict-2PL 36
37 Strict-2PL: Advantages 1. A transaction reads a value written by a committed transaction; therefore a transaction never has to abort because another transaction aborts extreme case is cascaded aborts. 2. All lock acquisitions and releases can be handled by the system automatically; locks are acquired whenever a data item is to be accessed and released when the transaction commits. 37
38 2PL: Example (1) Initial values: x = y = z = 50; Two Transactions: T 1 : T 2 : x = x y = y z = y + z z = 2z + x x = 2x y = 2y (Serialized) Order: T 1 T 2 T 1 : x = 150 y = 200 z = 250 T 2 : x = 300 y = 400 z = 650 T 2 T 1 x = 200 y = 250 z =
39 Ordering without 2PL: Example Initial values: x = y = z = 50 T 1 lock x T 2 lock z modify x (x=150) x=x+100 wait for lock on x unlock x lock y lock x modify y (y=200) y=y+150 modify z (z = 250) z=2z + x unlock y unlock z lock z modify x (x=300) x = 2x modify z (z=450) z=y+z lock y unlock z modify y (y = 400) y= 2y unlock x and y Result: x = 300, y = 400, z =
40 2PL: Problems System can come to a deadlock If two transactions each try to acquire the same pair of locks but in the opposite order, a deadlock may occur (see the previous result) Solution Put a timeout on locks and abort transaction on lock expiration. Deadlock detection algorithms. Maintaining an explicit graph of which process has which locks and wants which locks; and checking the graph for cycles 40
41 Timestamp Ordering (1) Basic idea: Transaction manager assigns a unique timestamp ts(t i ) to each transaction T i. Every operation in T i is timestamped with ts(t i ). Each data item x has also a read timestamp, ts RD (x), and a write timestamp ts WR (x). ts RD (x)is set to the timestamp of the transaction that has most recently read x. Similarly, ts WR (x)is set to timestamp of the transaction that has most recently changed x. In case of conflicting operations on data item x, the data manager processes the one with the lowest timestamp first. 41
42 Timestamp Ordering (2) Suppose: two conflicting operations read(t i, x), write(t j, x) ts(t i ) < ts(t j ), but write(t j, x) has already been processed by the data manager Then, the scheduler must reject read(t i, x) Note: Timestamp ordering is rather aggressive for if a single operation is rejected, the transaction will have to be aborted Example: read(t, x) if ts(t) < ts WR (x) then T must be aborted if ts(t) > ts WR (x) then read(t, x) can be executed (ts RD (x):= max(ts(t), ts RD (x))) 42
43 Timestamp Ordering (3) Example: write(t, x) if ts(t) < ts RD (x) then T must be aborted if ts(t) > ts RD (x) then write(t, x) can be executed Suppose: two conflicting operations oper(t i, x) and oper(t j, x) ts(t i ) < ts(t j ), and oper(t i, x) is submitted to the data manager It holds back oper(t j, x) 43
44 Pessimistic Timestamp Ordering: Example (1) ts(t 1 ) << ts(t 2 ) < ts(t 3 ). Initially, ts RD (x) = ts WR (x) = ts(t 1 ) T 2 is trying to write x ts RD (x) ts WR (x) ts(t 2 ) ts(t 2 ) ts WR (x) (T 1 ) (T 1 ) (T 2 ) (T 2 ) (T 3 ) write is accepted time write is not accepted time ts(t 2 ) ts RD (x) (T 2 ) (T 3 ) write is not accepted time 44
45 Pessimistic Timestamp Ordering: Example (2) ts(t 1 ) << ts(t 4 ) < ts(t 2 ) < ts(t 3 ) Initially, ts RD (x) = ts WR (x) = ts(t 1 ) T 2 is trying to read x ts WR (x) ts(t 2 ) ts(t 2 ) ts WR (x) (T 1 ) (T 2 ) (T 2 ) (T 3 ) no conflict time Abort time ts WR (x) ts tent (x) ts(t 2 ) ts(t 2 ) ts tent (x) (T 1 ) (T 4 ) (T 2 ) (T 2 ) (T 3 ) time no conflict, T 2 just waits Abort time 45
46 Optimistic Concurrency Control Observation: 1. Maintaining locks is expensive 2. In practice, there are not many conflicts Alternative Go ahead immediately with all operations, use tentative writes everywhere (shadow or private copies), solve conflicts while committing Phases: 1. Allow operations tentatively 2. validate effects (through checking all transactions) 3. make updates permanent 46
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