Mutual Exclusion What is mutual exclusion? Single processor systems Make sure that no other will use the shared data structure at the same time. Single processor systems use semaphores and monitors Three different algorithms Centralized Algorithm Distributed Algorithm Token Ring Algorithm

Mutual Exclusion:Centralized Algo(1) One process is elected as coordinator Other processes send it a message asking for permission coordinator grants permission or says no-permission (or doesn’t reply at all) queues the request When the critical region is free it sends a message to the first one in the queue

Mutual Exclusion: A Centralized Algorithm(2) Process 1 asks the coordinator (ask)for permission to enter a critical region. Permission is granted Process 2 then asks permission to enter the same critical region. The coordinator does not reply. When process 1 exits the critical region, it tells the coordinator,(release) when then replies to 2

Mutual Exclusion: A Centralized Algorithm(3) Coordinator only let one process to enter the critical region. The request is granted in the order: no process ever waits forever ( no starvation). Three messages is use in accessing the critical region/shared resources: Request Grant Release Drawback:coordinator is single point failure If process blocked after making a request- it is cannot distinguish either the coordinator is dead or resource not available. Performance bottleneck in a large system.

Mutual Exclusion:A Distributed Algo(1) There are total ordering of all event in the system Provide timestamps by using Lamport Algorithm Algorithm: A process wanting to enter the Critical Section (CS) Build a msg :- forms <cs-name, its process id, current-time> sends to all processes including itself. assume that sending is reliable; every msg is acknowledge

Mutual Exclusion: A Distributed Algorithm(2) Every receiving process sends an OK, if it is not interested in the CS if it is already in the CS, just queues the message if it itself has sent out a message for the CS compares the time stamps if an incoming message has lower timestamp it sends out an OK else it just queues it Once it receives an OK from everyone it enters the CS once its done, its sends an OK to everyone in its queue

Mutual Exclusion: A Distributed Algo(3) 8 12 Two processes(0&2) want to enter the same critical region at the same moment. Process 1 not interested for CS-> send OK to 0 and 2. 0 & 1 compare the timestamps=> Process 0 has the lowest timestamp, so it wins. When process 0 is done, it sends an OK also, so 2 can now enter the critical region.

A Token Ring Algorithm(1) Create a logical ring (in software) each process knows who is next When a process have the token, it can enter the CS Finished, release the token and pass to the next guy The token circulate at high speed around the ring if no process wants to enter the CS. No starvation at worst wait for each other process to complete Detecting that a token has been lost is hard What if a process crashes? recovery depends on the processes being able to skip this process while passing on the ring

A Token Ring Algorithm(2) 6+1%8=7 An unordered group of processes on a network. A logical ring constructed in software. Process must have token to enter. If don’t want to enter, pass token along. If token lost (detection is hard), regenerate token. If host down, recover ring.

Comparison A comparison of three mutual exclusion algorithms. Messages per entry/exit Delay before entry (in message times) Problems Centralized 3 2 Coordinator crash Distributed 2 ( n – 1 ) Crash of any process Token ring 1 to  0 to n – 1 Lost token, process crash Centralized most efficient Token ring efficient when many want to use critical region

The Transaction Model(1) A transaction is a unit of program execution that accesses and possibly updates various data items. A transaction must see a consistent database. During transaction execution the database may be inconsistent. When the transaction is committed, the database must be consistent. Two main issues to deal with: Failures of various kinds, such as hardware failures and system crashes Concurrent execution of multiple transactions

The Transaction Model (3) Examples of primitives for transactions. Primitive Description BEGIN_TRANSACTION Make the start of a transaction END_TRANSACTION Terminate the transaction and try to commit ABORT_TRANSACTION Kill the transaction and restore the old values READ Read data from a file, a table, or otherwise WRITE Write data to a file, a table, or otherwise Above may be system calls, libraries or statements in a language (Sequential Query Language or SQL)

The Transaction Model (4) Reserving Flight from White Plains to Malindi BEGIN_TRANSACTION reserve WP -> JFK; reserve JFK -> Nairobi; reserve Nairobi -> Malindi; END_TRANSACTION (a) BEGIN_TRANSACTION reserve WP -> JFK; reserve JFK -> Nairobi; reserve Nairobi -> Malindi full => ABORT_TRANSACTION (b) Transaction to reserve three flights commits Transaction aborts when third flight is unavailable

Characteristics of Transaction(5) Atomic Completely happened or nothing Consistent The system not violate system invariant-one state to another Ex: no money lost after operations Isolated Operations can happen in parallel but as if were done serially Durable The result become permanent when its finish/commit ACID- FLAT TRANSACTION

Example: Funds Transfer Transaction to transfer $50 from account A to account B: 1. read(A) 2. A := A – 50 write(A) 4. read(B) 5. B := B + 50 6. write(B) Consistency requirement – the sum of A and B is unchanged by the execution of the transaction. Atomicity requirement — if the transaction fails after step 3 and before step 6, the system ensures that its updates are not reflected in the database.

Example: Funds Transfer continued Durability requirement — once the user has been notified that the transaction has completed (i.e., the transfer of the $50 has taken place), the updates to the DB must persist despite failures. Isolation requirement — if between steps 3 and 6, another transaction is allowed to access the partially updated database, it will see an inconsistent database (the sum A + B will be less than it should be). Can be ensured by running transactions serially.

Flat Transaction Simplest type of transaction; all sub transaction were group into a single transaction. Limitation what if want to keep first part of flight reservation? If abort and then restart, those might be gone. Does not allowed partial result to be committed or Aborted Solve by using nested transaction

Atomic Transactions Transaction: an operation composed of a number of discrete steps. All the steps must be completed for the transaction to be committed. The results are made permanent. Otherwise, the transaction is aborted and the state of the system reverts to what it was before the transaction started.

All or nothing property Example Buying a house: Make an offer Sign contract Deposit money in escrow Inspect the house Critical problems from inspection? Get a mortgage Have seller make repairs Commit: sign closing papers & transfer deed Abort: return escrow and revert to pre-purchase state All or nothing property

Basic Operations Transaction primitives: Begin transaction: mark the start of a transaction End transaction: mark the end of a transaction; try to commit Abort transaction: kill the transaction, restore old values Read/write data from files (or object stores): data will have to be restored if the transaction is aborted.

Programming in a Transaction System Begin_transaction Mark the start of a transaction End_transaction Mark the end of a transaction and try to “commit” Abort_transaction Terminate the transaction and restore old values Read Read data from a file, table, etc., on behalf of the transaction Write Write data to file, table, etc., on behalf of the transaction Atomic Transactions

Tools for Implementing Atomic Transactions (continued) Begin_transaction Place a begin entry in log Write Write updated data to log Abort_transaction Place abort entry in log End_transaction (i.e., commit) Place commit entry in log Copy logged data to files Place done entry in log Atomic Transactions

Programming in a Transaction System (continued) As a matter of practice, separate transactions are handled in separate threads or processes Isolated property means that two concurrent transactions are serialized I.e., they run in some indeterminate order with respect to each other Atomic Transactions

Programming in a Transaction System (continued) Nested Transactions One or more transactions inside another transaction May individually commit, but may need to be undone Example Planning a trip involving three flights Reservation for each flight “commits” individually Must be undone if entire trip cannot commit Atomic Transactions

Another Example Book a flight from Penang, KLIA to Waikato. No non-stop flights are available: Transaction begin Reserve a seat for Penang to KLIA (PNG→KLIA) Reserve a seat for KLIA to Bangkok (KLIA→BGK) Reserve a seat for Bangkok to Waikato (BGK→WK) Transaction end If there are no seatsavailable on the BGK→WK leg of the journey, the transaction is aborted and reservations for (1) and (2) are undone.

Tools for Implementing Atomic Transactions (single system) Stable storage i.e., write to disk “atomically” Log file i.e., record actions in a log before “committing” them Log in stable storage Locking protocols Serialize Read and Write operations of same data by separate transactions … Atomic Transactions

Tools for Implementing Atomic Transactions (continued) Crash recovery – search log If begin entry, look for matching entries If done, do nothing (all files have been updated) If abort, undo any permanent changes that transaction may have made If commit but not done, copy updated blocks from log to files, then add done entry Atomic Transactions

Distributed Atomic Transactions Atomic transactions that span multiple sites and/or systems Same semantics as atomic transactions on single system A C I D Failure modes Crash or other failure of one site or system Network failure or partition Byzantine failures Atomic Transactions

Properties of transactions: ACID Atomic The transaction happens as a single indivisible action. Others do not see intermediate results. All or nothing. Consistent If the system has invariants, they must hold after the transaction. E.g., total amount of money in all accounts must be the same before and after a “transfer funds” transaction. Isolated (Serializable) If transactions run at the same time, the final result must be the same as if they executed in some serial order. Durable Once a transaction commits, the results are made permanent. No failures after a commit will cause the results to revert.

Nested Transactions A top-level transaction may create subtransactions Problem: subtransactions may commit (results are durable) but the parent transaction may abort. One solution: private workspace Each subtransaction is given a private copy of every object it manipulates. On commit, the private copy displaces the parent’s copy (which may also be a private copy of the parent’s parent)

Nested Transaction Constructed from a number of sub-transaction Top-level transaction may fork children run in parallel in different machine The children itself may fork another child or subs transaction When one transaction is commit- it will make visible to their parent

Nested transactions transactions may be composed of other transactions T : top-level transaction T 1 = openSubTransaction 2 openSubTransaction : 11 12 211 21 prov.commit prov. commit abort commit Figure 12.13 transactions may be composed of other transactions several transactions may be started from within a transaction we have a top-level transaction and subtransactions which may have their own subtransactions •

Nested transactions (12.3) To a parent, a subtransaction is atomic with respect to failures and concurrent access transactions at the same level (e.g. T1 and T2) can run concurrently but access to common objects is serialised a subtransaction can fail independently of its parent and other subtransactions when it aborts, its parent decides what to do, e.g. start another subtransaction or give up •

Example Nested Transaction Nested transaction gives you a hierarchy Can distribute (example: WPJFK, JFKNairobi, Nairobi -> Malindi) Each of them can be manage independently But may require multiple databases Transaction:Booking a ticket WPJFK Commit JFKNairobi Commit Nairobi Malindi Abort

Distributed transaction A distributed transaction is composed of several sub-transactions each running on a different site. Separate algorithms are needed to handle the locking of data and committing the entire transaction. Differences between nested transaction and distributed transaction

Transaction:Implementation Two methods are used Private Workspace Writeahead Log Consideration on a file system

Private Workspace Conceptually, when a process starts a transaction, it is given a private workspace (copies) containing all the files and data objects to which it has access. When it commits, the private workspace replaces the corresponding data items in the permanent workspace. If the transaction aborts, the private workspace can simply be discarded. This type of implementation leads to many private workspaces and thus consumes a lot of space. Optimization: (as cost of copying is very expensive) No need for a private copy when a process reads a file. For writing a file, only the file’s index is copied.

Private Workspace Original file index and disk blocks for a three-block file The situation after a transaction has modified/update block 0 and appended block 3 Copy file index only. Copy blocks only when written. Modified block 0 and appended block 3 After committing;

More Efficient Implementation/Write ahead log Files are actually modified, but before changes are made, a record <Ti,Oid,OldValue,NewValue> is written to the writeahead log on the stable storage. Only after the log has been written successfully is the change made to the file. If the transaction succeeds and is committed, a record is written to the log, but the data objects do not have to be changed, as they have already been updated. If the transaction aborts, the log can be used to back up to the original state (rollback). The log can also be used for recovering from crash.

Don’t make copies. Instead, record action plus old and new values Writeahead Log Don’t make copies. Instead, record action plus old and new values x = 0; y = 0; BEGIN_TRANSACTION; x = x + 1; y = y + 2 x = y * y; END_TRANSACTION; (a) Log [x = 0 / 1] (b) [y = 0/2] (c) [x = 1/4] (d) Old value New value a) A transaction b) – d) The log before each statement is executed If transaction commits, nothing to do If transaction is aborted, use log to rollback

Concurrency Control (1) The goal of concurrency control is to allow several transactions to be executed simultaneously, but the collection of data item is remains in a consistent state. The consistency can be achieved by giving access to the items in a specific order General organization of managers for handling transactions.

Concurrency Control (2) General organization of managers for handling distributed transactions.

Serializability a) – c) Three transactions T1, T2, and T3 BEGIN_TRANSACTION x = 0; x = x + 1; END_TRANSACTION (a) BEGIN_TRANSACTION x = 0; x = x + 2; END_TRANSACTION (b) BEGIN_TRANSACTION x = 0; x = x + 3; END_TRANSACTION (c) Schedule 1 x = 0; x = x + 1; x = 0; x = x + 2; x = 0; x = x + 3 Legal Schedule 2 x = 0; x = 0; x = x + 1; x = x + 2; x = 0; x = x + 3; Schedule 3 x = 0; x = 0; x = x + 1; x = 0; x = x + 2; x = x + 3; Illegal (d) a) – c) Three transactions T1, T2, and T3 d) Possible schedules

One-phase atomic commit protocol The protocol Client request to end a transaction The coordinator communicates the commit or abort request to all of the participants and to keep on repeating the request until all of them have acknowledged that they had carried it out The problem some servers commit, some servers abort How to deal with the situation that some servers decide to abort?

Introduction to two-phase commit protocol Allow for any participant to abort First phase Each participant votes to commit or abort The second phase All participants reach the same decision If any one participant votes to abort, then all abort If all participants votes to commit, then all commit The challenge work correctly when error happens Failure model Server crash, message may be lost

The two-phase commit protocol When the client request to abort The coordinator informs all participants to abort When the client request to commit First phase The coordinator ask all participants if they prepare to commit If a participant prepare to commit, it saves in the permanent storage all of the objects that it has altered in the transaction and reply yes. Otherwise, reply no Second phase The coordinator tell all participants to commit ( or abort)

The two-phase commit protocol … continued Operations for two-phase commit protocol The two-phase commit protocol Record updates that are prepared to commit in the permanent storage When the server crash, the information can be retrieved by a new process If the coordinator decide to commit, all participants will commit eventually

Timeout actions in the two-phase commit protocol Communication in two-phase commit protocol New processes to mask crash failure Crashed process of coordinator and participant will be replaced by new processes Time out for the participant Timeout of waiting for canCommit: abort Timeout of waiting for doCommit Uncertain status: Keep updates in the permanent storage getDecision request to the coordinator Time out for the coordinator Timeout of waiting for vote result: abort Timeout of waiting for haveCommited: do nothing The protocol can work correctly without the confirmation

Two-phase commit protocol for nested transactions Nested transaction semantics Subtransaction Commit provisionally abort Parent transaction Abort: all subtransactions abort Commit: exclude aborting subtransactions Distributed nested transaction When a subtransaction completes provisionally committed updates are not saved in the permanent storage

Distributed nested transactions commit protocol Each subtransaction If commit provisionally Report the status of it and its descendants to its parent If abort Report abort to its parent Top level transaction Receive a list of status of all subtransactions Start two-phase commit protocol on all subtransactions that have committed provisionally

The information held by each coordinator Example of a distributed nested transactions The execution process The information held by each coordinator Top level coordinator The participant list: the coordinators of all the subtransactions in the tree that have provisionally committed but do not have aborted parent Two-phase commit protocol Conducted on the participant of T, T1 and T12

Different two-phase commit protocol Hierarchic two-phase commit protocol Messages are transferred according to the hierarchic relationship between successful participants The interface Flat two-phase commit protocol Messages are transferred from top-level coordinator to all successful participants directly

Locking Locking is the oldest, and still most widely used, form of concurrency control When a process needs access to a data item, it tries to acquire a lock on it - when it no longer needs the item, it releases the lock The scheduler’s job is to grant and release locks in a way that guarantees valid schedules

In 2PL, the scheduler grants all the locks during a growing phase, and releases them during a shrinking phase In describing the set of rules that govern the scheduler, we will refer to an operation on data item x by transaction T as oper(T,x)

Two-Phase Locking Rules (Part 1) When the scheduler receives an operation oper(T,x), it tests whether that operation conflicts with any operation on x for which it has already granted a lock If it conflicts, the operation is delayed If not, the scheduler grants a lock for x and passes the operation to the data manager The scheduler will never release a lock for x until the data manager acknowledges that it has performed the operation on x

Two-Phase Locking Rules (Part 2) Once the scheduler has released any lock on behalf of transaction T, it will never grant another lock on behalf of T, regardless of the data item T is requesting the lock for An attempt by T to acquire another lock after having released any lock is considered a programming error, and causes T to abort

Two-Phase Locking (1) Two-phase locking.